2014 : WHAT SCIENTIFIC IDEA IS READY FOR RETIREMENT?

Everything? Well, wait a minute. Questioning A Theory of Everything may be beating a dead horse since I’m certainly not the first to be bothered by its implicit hyperbole but let’s face it, referring to one’s field of study as The Theory of Everything smacks of arrogance and naivité. Although it’s only been around for a relatively short period and may already be dying a natural death, the phrase, though certainly not the endeavour, should be retired from serious scientific literature and discourse.

Let me elaborate. The search for grand syntheses, for commonalities, regularities, ideas and concepts that transcend the narrow confines of specific problems or disciplines is one of the great inspirational drivers of science and scientists. Arguably, it is also a defining characteristic of homo sapiens sapiens. Perhaps the binomial form of sapiens is some distorted poetic recognition of this. Like the invention of gods and God, the concept of A Theory of Everything connotes the grandest vision of all, the inspiration of all inspirations, namely that we can encapsulate and understand the entirety of the universe in a small set of precepts, in this case, a concise set of mathematical equations. Like the concept of God, however, it is potentially misleading and intellectually dangerous.

Among the classic grand syntheses in science are Newton’s laws, that taught us that heavenly laws were no different than the earthly, Maxwell’s unification of electricity and magnetism, that brought the ephemeral aether into our lives, Darwin’s theory of natural selection, which reminded us that we’re just animals and plants after all, and the laws of thermodynamics that suggest we can’t go on forever. Each of these has had profound consequences not only in changing the way we think about the world, but also in laying the foundations for technological advancements that have led to the standard of living many of us are privileged to enjoy. Nevertheless, they are all, to varying degrees, incomplete. Indeed, understanding the boundaries of their applicability, the limits to their predictive power and the ongoing search for exceptions, violations and failures have provoked even deeper questions and challenges, stimulating the continued progress of science and the unfolding of new ideas, techniques and concepts.

One of the great ongoing scientific challenges is the search for a Grand Unified Theory of the elementary particles and their interactions, including its extension to understanding the cosmos and even the origin of space-time itself. Such a theory would be based on a parsimonious set of underlying mathematisable universal principles that integrate and explain all the fundamental forces of nature from gravity and electromagnetism to the weak and strong nuclear forces, incorporating Newton’s laws, quantum mechanics and general relativity. Fundamental quantities like the speed of light, the dimensionality of space-time and masses of the elementary particles would all be predicted, and the equations governing the origin and evolution of the universe through to the formation of galaxies and beyond would be derived, and so on. This constitutes The Theory of Everything. It is a truly remarkable and enormously ambitious quest that has occupied thousands of researchers for over fifty years at a cost of billions of dollars. Measured by almost any metric this quest, which is still far from its ultimate goal, has been enormously successful, leading, for example, to the discovery of quarks and the Higgs, to black holes and the Big Bang, to quantum chromodynamics and string theory…..and to many Nobel Prizes.

But Everything? Well, hardly. Where’s life, where are animals and cells, brains and consciousness, cities and corporations, love and hate, etc, etc? How does the extraordinary diversity and complexity seen here on earth arise? The simplistic answer is that these are inevitable outcomes of the interactions and dynamics encapsulated in the Theory. Time evolves from the geometry and dynamics of strings, the universe expands and cools, and the hierarchy from quarks to nucleons, to atoms and molecules, to cells, brains, and emotions and all the rest come tumbling out; a sort of deus ex machina, a result of "just" turning the crank of increasingly complicated equations and computations presumed, in principle, to be soluble to any sufficient degree of accuracy. Qualitatively, this extreme version of reductionism may have some validity, but Something is missing.

The "Something" includes concepts like information, emergence, accidents, historical contingency, adaptation and selection, all characteristics of complex adaptive systems whether organisms, societies, ecosystems or economies. These are composed of myriad individual constituents or agents that take on collective characteristics that are generally unpredictable, certainly in detail, from their underlying components even if the interactive dynamics are known. Unlike the Newtonian paradigm upon which The Theory of Everything is based, the complete dynamics and structure of complex adaptive systems cannot be encoded in a small number of equations. Indeed, in most cases, probably not even in an infinite number! Furthermore, predictions to arbitrary degrees of accuracy are not possible, even in principle.

Perhaps, then, the most surprising consequence of a visionary Theory of Everything is that it implies that, on the grand scale, the universe, including its origins and evolution, though extremely complicated, is not complex but, in fact, is surprisingly simple since it can be encoded in a limited number of equations, conceivably only one. This is in stark contrast to here on earth where we are integral to some of the most diverse, complex and messy phenomena that occur anywhere in the universe, and which require additional, possibly non-mathematisable concepts, to understand. So, while applauding and admiring the search for a Grand Unified Theory of all the basic forces of nature, let’s drop the implication that it can, in principle, explain and predict Everything. Let us instead incorporate a parallel quest for A Grand Unified Theory of Complexity. The challenge of developing a quantitative, analytic, principled, predictive framework for understanding complex adaptive systems is surely a grand challenge for the 21^st Century. Like all grand syntheses, it will inevitably remain incomplete but nevertheless will undoubtedly inspire significant, possibly revolutionary, new ideas, concepts, and techniques.

The idea to be abandoned is the idea that there is no reality in the quantum world. The idea probably came about because of two reasons. On the one hand, because of the fact that one cannot always ascribe a precise value to a physical property, and on the other hand, because within the wide spectrum of interpretations of quantum mechanics some suggest that the quantum state does not describe an external reality, but rather that the properties only come about in the mind of the observer and therefore that consciousness plays a crucial role.

Let us consider for a second the famous double-slit experiment. Such experiments or their equivalents have to date not only been performed with single photons or any other kind of single particles, like neutrons, protons, electrons etc., but even with very large macromolecules, such as buckyballs and even larger. Specifically we do the experiment with buckyballs—the C-60 or C-70 molecules. You have two slits and under the right experimental conditions, you observe a distribution of the buckyballs behind the slits which has maxima and minima, the interference pattern. This is due to interference of the probability waves passing through both slits. But, following Einstein in his famous debate with Niels Bohr, we might ask if we do the experiment with individual particles, individual buckballs one by one: Through which slit does an individual buckyball molecule pass? Would it not be natural to assume that every particle has to pass either slit? Quantum physics tells us that this is not a meaningful question. We cannot assign a well-defined position to the particle unless we actually perform an experiment which allows us to find out where it is. So, before we do the measurement, the position of the buckyball—and therefore the slit it passes through—is a concept devoid of any meaning. 

Suppose we now measure the position of the particle. Then we get an answer and know where it is. It is either near one slit or near the other slit. In that case, position is certainly an element of reality, and we can clearly say that quantum physics describes this reality. What is interesting is that having precise knowledge of one feature, namely the position, another kind of knowledge, namely the one encoded in the interference pattern, is not well-defined anymore. 

Where could consciousness come in here? Quantum mechanics tells us that the particle, before any observation, is in a superposition of passing through one slit and of passing through the other slit. If we now have two detectors, one each behind each slit, then either detector will register the particle. But quantum mechanics tells us that the measurement apparatus becomes entangled with the position observable of the particle, and thus itself does not have well-defined classical features, at least in principle. This, following the Hungarian-American Nobel prize winner Eugene Wigner, is a chain which can be followed until an observer registers the result. So if we would adopt that reasoning, it is the consciousness which would make reality happen. 

But you don't have to go so far. It is enough to assume that quantum mechanics just describes probabilities of possible measurement results. Then making an observation turns potentiality into actuality and, in our case, the position of the particle becomes a quantity one can talk reasonably about. But, whether it has a well-defined position or not, the buckyball very well exists. It is real in the double-slit experiment, even when it is impossible to assign its position a well-defined value.

Gordon Moore's 1965 paper stating that the number of transistors on integrated circuits will double every two years has become the most popular scientific analogy of the digital age. Despite being a mere conjecture it has become the go-to model to frame complex progress in a simple formula. There are good technological reasons to retire Moore's Law. For example the general consensus that Moore's Law will effectively cease to exist past a transistor size smaller than 5 nanometers. That would mean a peak and sharp drop-off in ten to twenty years. Another one is the potential of quantum computers pushing computing into new realms, expected to become reality in three to five years. But Moore's Law should be retired before its technological limits, because it has propelled the perception of progress into wrong directions. Allowing its end to become an event would just amplify the errors of reasoning.

First and foremost Moore's Law has allowed to perceive the development of the digital era as a linear narrative. The simple curve of progression is the digital equivalent of the ancient wheat and chessboard problem (with a potentially infinite chessboard). Like the Persian inventor of the game of chess who demanded from the king a geometric progression of grains all across the board, digital technology seems to develop exponentially. This model ignores the parallel nature of digital progress, which encompasses not only technological or economic development, but scientific, social and political change. Changes that can rarely be quantified.

Still the Moore's law model of perception has already found its way into the narrative of biotechnological history, where change become ever more complex. Proof of progress is claimed in the simplistic reasoning of a sharp decline in cost for sequencing a human genome from three billion Dollars in the year 2000 to the August 2013 cancellation of the Genomics X Prize for the first 1000 Dollar genome, because the challenge had been outpaced by innovation.

For both digital and biotechnical history the linear narrative has been insufficient. The prowess of the integrated circuit has been the technological spark to induce a massive development comparable with the wheel allowing the rise of urban society. Both technologies have been perfected over time, but their technological refinement falls short to illustrate the impact both had.

It is about 25 years ago that scientists at MIT's media lab told me about a paradigmatic change in computer technology. In the future, they said, the number of other computers connected to a computer will be more important than its number of transistors on its integrated circuits. For a writer interested but not part of the forefront of computer technology that was still groundbreaking news in 1988. A few years later the demo of a Mosaic browser was as formative as listening to the first Beatles record and seeing the first man on the moon had been for my parents.

Change since have been so multilayered, interconnected and rapid that comprehension has lagged behind ever since. Scientific, social and political changes occur in random patterns. Results have been mixed in equally random patterns. The slowdown of the music industry and media has not been matched in the publishing industry and film. The failed twitter revolution of Iran had quite a few things in common with the Arab spring, but even in the Maghreb the results differed wildly. Social networks have impacted societies sometimes in exact opposites—while the fad of social networks have resulted in cultural and isolation in Western society, it has created a counterforce of collective communication against the strategies of the Chinese party apparatus to isolate its citizenry from within.

Most of these phenomena have been only observed, not explained by now. It is mostly in hindsight that a linear narrative is constructed, if not imposed on. The inability of many of the greatest digital innovations like viral videos or social networks to be monetized are just one of many proofs how difficult it is to get a comprehensive grasp on digital history. Moore's law and its numerous popular applications to other fields of progress thus create an illusion of predictability in the least predictable of all fields—the course of history.

These errors of reasoning will be amplified, if Moore's Law is allowed to come to its natural end. Peak theories have become the lore of cultural pessimism. If Moore's law is allowed to become a finite principle, digital progress will be perceived as a linear progression towards a peak and an end. Neither will become a reality, because the digital is not a finite resource, but an infinite realm of mathematical possibilities reaching out into the analog world of sciences, society, economics and politics. Because this progress has ceased to depend on quantifiable basis and on linear narratives it will not be brought to a halt, not even slowed down, if one of its strains comes to an end.

In 1972 the wheat and chessboard problem became the mythological basis for the Club of Rome's Malthusian „The Limits to Growth". Moore's will create the disillusionment of a finite nature of the digital. It will become as popular as its illusion of predictability. After all there have bee no loonies carrying signs saying "The End is Not Near".

In the late summer of 1914, as European civilization began its extended suicide, dissenters were scarce. On the contrary: From every major capital, we have jerky newsreel footage of happy crowds, cheering in the summer sunshine. More war and oppression followed in subsequent decades, and there was never a shortage of willing executioners and obedient lackeys. By mid-century, the time of Stalin and Mao and their smaller-bore imitators, it seemed urgent to understand why people throughout the 20^th century had failed to rise up against masters who sent them to war, or to concentration camps, or to the gulag. So social scientists came up with an answer, which was then consolidated and popularized into something every educated person supposedly knows: People are sheep—cowardly, deplorable sheep.

This idea, that most of us are unwilling to "think for ourselves," instead preferring to stay out of trouble, obey the rules, and conform, was supposedly established by rigorous laboratory experiments. ("We have found," wrote the great psychologist Solomon Asch in 1955, "the tendency to conform in our society is so strong that reasonably intelligent and well-meaning young people are willing to call white black.")  Plenty of research papers still refer to one or another aspect of the sheep model as if it were a truth universally acknowledged, and a sturdy rock on which to build new hypotheses about mass behavior. Worse yet, it's rampant in the conversation of educate laypeople—politicians, voters, government officials. Yet it is false. It makes for bad assumptions and bad policies. It is time to set it aside.

Some years ago, the psychologists Bert Hodges and Anne Geyer examined one of Asch's own experiments from the 1950s. He'd asked people to look at a line printed on a white card and then tell which of three similar lines was the same length. Each volunteer was sitting in a small group, all of whose other members were actually collaborators in the study, deliberately picking wrong answers. Asch reported that when the group chose the wrong match, many individuals went along, against the evidence of their own senses.

But the experiment actually involved 12 separate comparisons for each subject, and most did not agree with the majority, most of the time. In fact, on average, each person agreed three times with the majority, and insisted on his own view nine other times. To make those results all about the evils of conformity is to say, as Hodges and Geyer note, that "an individual's moral obligation in the situation is to 'call it as he sees it' without consideration of what others say.''

To explain their actions, the volunteers didn't indicate that their senses had been warped or that they were terrified of going against consensus. Instead, they said they had chosen to go along that one time. It's not hard to see why a reasonable person would do so.

The "people are sheep" model sets us up to think in terms of obedience or defiance, dumb conformity versus solitary self-assertion (to avoid being a sheep, you must be a lone wolf). It does not recognize that people need to place their trust in others, and win the trust of others, and that this guides their behavior. (Stanley Milgram's famous experiments, where men were willing to give severe shocks to a supposed stranger, are often cited as Exhibit A for the "people are sheep" model. But what these studies really tested was the trust the subjects had in the experimenter.)

Indeed, questions about trust in others—how it is won and kept, who wins it and who doesn't—seem to be essential to understanding how collectives of people operate, and affect their members. What else is at work?

It appears that behavior is also susceptible to the sort of moment-by-moment influences that were once considered irrelevant noise (for example, divinity students in a rush were far less likely to help a stranger than were divinity students who were not late, in an experiment performed by John M. Darley and Dan Batson). And then there is mounting evidence of influences that discomfit psychologists because there doesn't seem to be much psychology in them at all. For example, Neil Johnson of the University of Miami and Michael Spagat of University College London and their colleagues have found the severity and timing of attacks in many different wars (different actors, different stakes, different cultures, different continents) adheres to a power law. If that's true, then an individual fighter's motivation, ideology, and beliefs make much less difference than we think for the decision to attack next Tuesday.

Or, to take another example, if as Nicholas Christakis' work suggests, your risks of smoking, getting an STD, catching the flu or being obese depend in part on your social network ties, then how much difference does it make what you, as an individual, feel or think?

Perhaps the behavior of people in groups will eventually be explained as a combination of moment-to-moment influences (like waves on the sea) and powerful drivers that work outside of awareness (like deep ocean currents). All the open questions are important and fascinating. But they're only visible after we give up the simplistic notion that we are sheep.

The world's languages differ to the point of inscrutability. Knowing the English word "duck" doesn't help you guess the French "canard" or Japanese "ahiru." But there are commonalities hidden beneath the superficial differences. For instance, human languages tend to have parts of speech (like nouns and verbs). They tend to have ways to embed propositions in other ones. ("John knows that Mary thinks that Paul embeds propositions in other ones.") And so on. But why?

An influential and appealing explanation is known as Universal Grammar: core commonalities across languages exist because they are part of our genetic endowment. On this view, humans are born with an innate predisposition to develop languages with very specific properties. Infants expect to learn a language that has nouns and verbs, that has sentences with embedded propositions, and so on.

This could explain not only why languages are similar but also what it is to be uniquely human and indeed how children acquire their native language. It may also seem intuitively plausible, especially to people who speak several languages: If English (and Spanish… and French!) have nouns and verbs, why wouldn't every language? To date, Universal Grammar remains one of the most visible products of the field of Linguistics—the one minimally counterintuitive bit that former students often retain from an introductory Linguistics class.

But evidence has not been kind to Universal Grammar. Over the years, field linguists (they're like field biologists with really good microphones) have reported that languages are much more diverse than originally thought. Not all languages have nouns and verbs. Nor do all languages let you embed propositions in others. And so it has gone for basically every proposed universal linguistic feature. The empirical foundation has crumbled out from under Universal Grammar. We thought that there might be universals that all languages share and we sought to explain them on the basis of innate biases. But as the purportedly universal features have revealed themselves to be nothing of the sort, the need to explain them in categorical terms has evaporated. As a result, what can plausibly make up the content of Universal Grammar has become progressively more and more modest over time. At present, there's evidence that nothing but perhaps the most general computational principles are part of our innate language-specific human endowment.

So it's time to retire Universal Grammar. It had a good run, but there's nothing much it can bring us now in terms of what we want to know about human language. It can't reveal much about how language develops in children—how they learn to articulate sounds, to infer the meanings of words, to put together words into sentences, to infer emotions and mental states from what people say, and so on. And the same is true for questions about how humans have evolved or how we differ from other animals. There are ways in which humans are unique in the animal kingdom and a science of language ought to be trying to understand these. But again Universal Grammar, gutted by evidence as it has been, will not help much.

Of course, it remains important and interesting to ask what commonalities, superficial and substantial, tie together the world's languages. There may be hints there about how human language evolved and how it develops. But to ignore its diversity is to set aside the most informative dimension of language. 

For over a century psychological theory held that once memories are consolidated from a short-term into long-term form, they remain stable and unchanging. Whether certain long-term memories are very slowly forgotten or are always present but cannot be retrieved was a matter of debate.

For the last 50 years, research on the neurobiological basis of memory seemed to support the psychological theory. Short-term memory was found to be mediated by biochemical changes at synapses, modifying their strength. Long-term memory was strongly correlated with long-term changes in the number of synapses, either increases or decreases. This intuitively made sense. Biochemical changes were rapid and could be quickly reversed, just like short-term memories. On the other hand, synapses although small were anatomical structures, visible under the microscope, and thus were thought to be stable for weeks, perhaps for years. Short-term memories could easily be prevented from consolidating into the long-term by dozens of inhibitors of different signaling molecules. In contrast, there was no known agent that erased a long-term memory.

Two recent lines of evidence have relegated this dominant theory of long-term memory ready for retirement. First is the discovery of reconsolidation. When memories are recalled, they undergo a brief period in which they are once again susceptible to disruption by many of the same biochemical inhibitors that affect the initial conversion of short- into long-term memory. This means that long-term memories are not immutable, but can be converted into short-term memory, and then reconverted back into long-term memory. If this reconversion doesn't happen, the specific long-term memory is effectively disrupted.

The second is the discovery of a few agents that do indeed erase long-term memories. These include inhibitors of the persistently active enzyme PKMzeta and of a protein translation factor with prion-like properties of perpetuation. Conversely, increasing the activity of the molecules enhances old memories. The persistent changes in synapse number that so strongly correlate with long-term memory may therefore be downstream of persistent biochemical changes. That memory erasing agents are so few suggests that there may be a relatively simple mechanism for long-term memory storage involving not hundreds of molecules as in short-term memory, but only a handful, perhaps working together.

Memory reconsolidation allows specific long-term memories to be manipulated. Memory erasure is extraordinarily potent and likely disrupts many, if not all long-term memories at the same time. When these two fields are combined, specific long-term memories will be erased or strengthened in ways never conceivable in prior theories.

For most of the 20th century, scientific thought was dominated by the idea of the uniformity of the universe and the uniqueness of laws of physics. Indeed, the cosmological observations indicated that the universe on the largest possible scales is almost exactly uniform, with the accuracy better than 1 in 10,000.

The situation is similar with respect to the uniqueness of the laws of physics. We knew, for example, that the electron mass is the same everywhere in the observable part of the universe, so the obvious assumption was that it must take the same value everywhere—that it was just a constant of nature. For a long time, one of the great goals of physics was to find a single theory—a Theory of Everything— that would unify all fundamental interactions and provide an unambiguous explanation for all known parameters of particle physics.

Some thirty years ago, a possible explanation arose for the uniformity of the universe. The main idea was that our part of the world emerged as a result of an exponentially rapid stretching of space called cosmic inflation. As all "wrinkles" and non-uniformities of space stretched out and disappeared, the universe became incredibly smooth. Add some quantum fluctuations, stretch them, and the uniformity became just a little bit less perfect, and galaxies emerged.

At first, inflationary theory looked like an exotic product of vivid imagination. But thanks to the enthusiastic work of thousands of scientists, many predictions of this theory have been confirmed by observations. And if the theory is correct, we finally have a scientific explanation of why the world is so uniform.

But inflation does not predict that this uniformity must extend beyond the observable part of the universe. To give an analogy: Suppose the universe is a surface of a big soccer ball consisting of black and white hexagons. If we inflate it, the size of each white or black part becomes exponentially large. If inflation is powerful enough, those who live in a black part of the universe will not ever see the white part. They will believe that the whole universe is black, and they will try to find a scientific explanation why it cannot have any other color. Those who live in a white universe will never see the black parts and therefore they may think that the whole world must be white. But both black and white parts may coexist in an inflationary universe without contradicting observations.

In the example given above, we were talking about black and white. But in physics, the number of different states of matter (the number of "colors") can be exponentially large. The best candidate for a Theory of Everything is string theory. It can be successfully formulated in spacetime with ten dimensions (nine dimensions of space and one of time). But we live in the universe with three dimensions of space. Where are other six? The answer is that they are compactified—squeezed into something so small that we cannot move in these directions, which is why we perceive the world as if it were three-dimensional.

From the early days of string theory, physicists knew that there are exponentially many different ways to compactly the extra 6 dimensions, but we did not know what can prevent the compactified dimensions from blowing up. This problem was solved about 10 years ago, and the solution validated the earlier expectations of the exponentially large number of possibilities. Some estimates of the number of different options are as large as 10⁵⁰⁰. And each of these options describes a part of the universe with a different vacuum energy and different types of matter.

In the context of the inflationary theory, this means that our world may consist of incredibly large number of exponentially large "universes" with 10⁵⁰⁰ different types of matter inside them.

A pessimist would argue that since we do not see other parts of the universe, we cannot prove that this picture is correct. An optimist, on the other hand, may counter that we can never disprove this picture either, because its main assumption is that other "universes" are far away from us. And since we know that the best of the theories developed so far allow about 10⁵⁰⁰ different universes, anybody who argues that the universe must have same properties everywhere would have to prove that only one of these 10⁵⁰⁰ universes is possible.

And then there is something else: There are many strange coincidences in our world. The mass of the electron is 2000 times smaller than the mass of the proton. Why? The only known reason is that if it would change few times, life as we know it would be impossible. The masses of the proton and neutron almost coincide. Why? If one of their masses would change just a little, life as we know it would be impossible. The energy of empty space in our part of the universe is not zero, but a tiny number, more than 100 orders of magnitude below the naive theoretical expectations. Why? The only known explanation is that we would be unable to live in the world with a much larger energy of vacuum.

The correlation between our properties and the properties of the world is called the anthropic principle. But if the universe came in only one copy, this correlation would not explain why. We would need to speculate about the divine cause making the universe custom-built for humans. However, with a multiverse consisting of many different parts with different properties, the correlation between our properties and the properties of the part of the world where we can live makes perfect sense.

Can we return back to the old picture of a single universe? Possibly. But in order to do so, we must (1) invent a better cosmological theory, (2) invent a better theory of fundamental interactions, and (3) propose an alternative explanation for the miraculous coincidences we just discussed. 

Genes and their interaction networks determine the phenotype of an organism—what it looks like and how it behaves. One of the biggest problems in modern evolutionary biology is understanding the relationship between genes and phenotypes. The prevailing theory is that all animals are built from essentially the same set of regulatory genes—a genetic toolkit, and that phenotypic variation within and between species arises simply by using shared genes differently. Scientists are now generating a vast amount of genomic data from an eclectic mix of organisms. These data are telling us to put to bed the idea that all life is underlain by a common toolkit of conserved genes. Instead, we need to turn our attention to the role of genomic novelty in the evolution of phenotypic diversity and innovation.

The idea of a conserved genetic toolkit of life comes from the 'evo-devo' (evolutionary and developmental biology) world. In short, it proposes that evolution uses the same ingredients in all organisms, but tinkers with the recipe. By expressing genes at different times in development and/or in different parts of the body, the same genes can be used in different combinations to allow evolvability, generating phenotypic diversity and innovation. Animals look different not because the molecular machinery is different, but because different parts of the machinery are activated to differing degrees, at different times, in different places and in different combinations. The number of combinations is huge, and so this is a plausible explanation for the development of complex and diverse phenotypes from even a small number of genes. For example, humans have a mere 21,000 genes in our genome, and yet we are arguably one of the most complex products of evolution.

A text-book example is the super-controller of development, Hox genes—a set of genes which tell bodies where to develop heads, tails, arms, legs, in every major animal group. Hox genes are in mice, worms, humans… they are inherited from a common ancestor. Other examples of toolkt genes are those that control eye development, or hair/plumage colouration. Toolkit genes are old, present in all animals and they do pretty much the same thing in all animals. There is no denying that conserved genomic material forms an important part of the molecular building blocks of life.

However. We can now sequence de novo the genomes and transcriptomes (the genes expressed at any one time/place) of any organism. We have sequence data for algae, pythons, green sea turtles, puffer fish, pied flycatchers, platypus, koala, bonobos, giant pandas, bottle-nosed dolphins, leafcutter ants, monarch butterfly, pacific oysters, leeches…the list is growing exponentially. And each new genome brings with it a suit of unique genes. Twenty percent of genes in nematodes are unique. Each lineage of ants contains about 4000 novel genes, but only 64 of these are conserved across all seven ant genomes sequenced so far.

Many of these unique ('novel') genes are proving important in the evolution of biological innovations. Morphological differences between closely related fresh water polyps, Hydra, can be attributed to a small group of novel genes. Novel genes are emerging as important in the worker castes of bees, wasps and ants. Newt-specific genes may play a role in their amazing tissue regenerative powers. In humans, novel genes are associated with devastating diseases, such as leukaemia and Alhzeimer's.

Life is genomically complex, and this complexity plays a crucial role in evolving diversity of life. It's easy to see how an innovation can be improved through natural selection, e.g. once the first eye evolved, it was subject to strong selection to increase the fitness (survival) of its owner. It is more challenging to explain how novelty first originates, especially from a conserved genomic toolkit. Darwinian evolution explains how organisms and their traits evolve, but not how they originate. How did the first eye arise? Or more specifically how did that master regulatory gene for eye development in all animals first originate? The capacity to evolve novel phenotypic traits (be they morphological, physiological or behavioural) is crucial for survival and adaptation, especially in changing (or new) environments.

A conserved genome can generate novelties through rearrangements (within or between genes), changes in regulation or genome duplication events. For example, the vertebrate genome has been replicated in their entirety twice in their evolutionary history; salmonid fish have undergone a further two whole genome duplications. Duplications reduce selection on the function of one of the gene copies, allowing that copy to mutate and evolve into a new gene whilst the other copy maintains business as usual. Conserved genomes can also harbour a lot of latent genetic variation—fodder for evolving novelty—which is not exposed to selection. Non-lethal variation can lie dormant in the genome by not being expressed, or by being expressed at times when it doesn't have a lethal effect on the phenotype. The molecular machinery that regulates expression of genes and proteins depends on minimal information, rules and tools: transcription factors recognise sequences of only a few base-pairs as binding sites, which gives them enormous potential for plasticity in where they bind. Pleiotropic changes across many conserved genes using different combination of transcription, translation and/or post-translation activity are a good source of genomic novelty. E.g. the evolution of beak shapes in Darwin's finches is controlled by pleiotropic changes brought about by changes in the signalling patterns of a conserved gene that controls bone development. The combinatorial power of even a limited genetic toolkit gives it enormous potential to evolve novelty from old machinery.

But the presence of unique genes in all evolutionary lineages studied to date now tells us that de novo gene birth, rather than a reordering of old ingredients, is important in phenotypic evolution. The over-abundance of non-coding DNA in genomes is less puzzling, if they are a melting pot for genomes to exploit and create new genes and gene function, and ultimately phenotypic innovation. The current thinking is that genomes are constantly producing new genes all the time, but that only a few become functional.

Our story started simply: all life is a product of gentle evolutionary tinkering of a shared molecular toolkit. The unimaginable time has arrived where we can unpack the molecular building blocks of any creature. And these data are shaking things up. What a surprise? Not really. Perhaps the most important lesson from this is that no theory is completely right, and that good theories are those that continue evolving and embracing innovation. Let's evolve theories (keeping the bits that are proven correct), not retire them.

It was born out of a mistranslation and has been misused ever since....but let us do a little thought experiment first:

Let's say you are a scientist and you noticed a phenomenon you would like to tell the world about. "The brain...", you say, "... can listen to a conversation and make sense of the frequencies, decode them into symbols and meaning... but when it is confronted with two such conversations simultaneously, it cannot deal with both threads in parallel. At best it can try to switch back and forth quickly, trying to keep up with the information."

So much for your theory—you formulate your findings and share it with colleagues, it gets argued and debated, just as it should be.

But now something odd happens: while all your discussions were in English, and you wrote it in English, and despite the fact that a large percentage of the leading scientists and Nobel Laureates are English speaking...somehow the prevailing language for publication is....Mongolian! There is a group in Ulaan Baatar, merrily taking your findings with great interest and your whole theory shows up all over the place...in Mongolian.

But here is the catch: you wrote that it is not possible to listen to two conversations at the same time, and thus their meaning to you is, well, undefined, until you decide to follow one of them properly.

However, as it turns out, Mongolian has no such word—"undefined"! Instead it got translated with an entirely different term: "uncertain", and the general interpretation of your theory has suddenly mutated from "one or the other of two conversations will be unknown to you" to the rather distinctly altered interpretation "you can listen to one, but the other will be.....entirely meaningless".

Saying that I am "unable to understand" both of them properly is one thing, but... my inability to perceive it does not render each of the conversations suddenly "meaningless", does it?

All of this is of course just an analogy. But it is pretty close to exactly what did happen—just the other way round: the scientist was Werner Heisenberg.

His observation was not about listening to simultaneous conversations but measuring the exact position and momentum of a physical system, which he described as impossible to determine at the same time. And although he discussed this with numerous colleagues in German (Einstein, Pauli, Schrödinger, Bohr, Lorentz, Born, Planck just to name some of the Solvay Conference group of 1927) the big step came in the dissemination in English, and there is the Mongolian in our analogy!

Heisenberg’s idea had quickly been dubbed Unschärferelation, which transliterates to “unsharpness relationship,” but as there is really no such term in English ('blurred', 'fuzzy', 'vague' or 'ambiguous' have all been tried), the translation ended up as "the Uncertainty Principle"—when he had not used either term at all (some point to Eddington). And what followed is really quite close to the analogy as well: rather than stating that either position or momentum are "as yet undetermined", it became common usage and popular wisdom to jump to the conclusion that there is complete "uncertainty" at the fundamental level of physics, and nature, even free will and the universe as such. Laplace's Demon killed as collateral damage (obviously his days were numbered anyway....)

Einstein remained skeptical his entire life: to him the "Unbestimmtheit" (Indeterminacy) was on the part of the observer: not realizing certain aspects of nature at this stage in our knowledge—rather than proof that nature itself is fundamentally undetermined and uncertain. In particular implications like the "Fernwirkung" (action at a distance) appeared to him "spukhaft" (spooky, eerie). But even in the days of quantum computing, qbits and tunnelling effects, I still would not want to bet against Albert ;) His intuitive grasp of nature survived so many critics and waves of counter-proof ended up counter-counter-proved.

And while there is plenty of reason to defend Heisenbergs findings, it is sad to see such a profound meme in popular science, which is merely based on a loose attitude towards translation ( and there are many other such cases...). I would love to encourage writers in French or Swedish or Arabic to point out the idiosyncracies and unique value of those languages—not for semantic pedantry but the benefit of alternate approaches.

German is not just good for Fahrvergnügen, Weltanschauung & Zeitgeist, there are many wonderful subtle shades of meaning. It is like a different tool to apply to thinking—and that's a good thing: a great hammer is a terrible saw.

The habitable zone defines those distances from a star where a planet similar to Earth would have the surface temperatures for water to be liquid. In the Solar System this zone stretches from in-between the orbits of Venus and Earth out to Mars. Its boundaries are approximate as they are applied to different planetary systems and sometimes the concept is used more broadly, e.g., to galaxies. The habitable zone concept has a venerable history in the search for alien life beyond Earth and most recently it contributed to the spectacular success of the NASA Kepler exoplanet-hunting mission. However, in the post-Kepler era it is a scientific concept that is ready for retirement.

The simple definition of the habitable zone is appealing to use in statistical estimates of habitable planets, because it depends on few parameters that are easy to measure. It is also easy to grasp: not too hot, not too cold—the Goldilocks zone. Simple and robust statistics are crucial to estimating the abundance and distribution of small planets like Earth in the Galaxy and the Kepler space mission excels at that. If our goal now is to search for life, then it is good to know where we should be heading to—habitable exoplanets. Then the "habitable" in the habitable zone is a misnomer, or a gross overstatement at the least. Even in our Solar System we contemplate alien life beyond its confines, e.g. on moons of Jupiter and Saturn. Today we need a concept of what makes an environment habitable—capable of letting life emerge and keep sustaining it over geological timescales, be it on a planet or on a moon. Finding out what makes a planet living and how to recognize a living planet with our telescopes is the big question.

The past year has been historic in the search for alien life. Thanks to Kepler and other exoplanet surveys we now know that Earth-like planets are so common that many close analogs to our home planet should reside in our neighbourhood of the Galaxy. This makes them amenable to remote sensing exploration with existing technology and telescopes under construction. The search for life is set to begin, but we need to understand better what to look for.

In retiring the habitable zone concept, it makes sense to revert to its original name—circa mid-20^th century, as the "liquid water belt"—a region very important to the rich geochemistry of rocky planets. Living planets among them will feel like home.

Could one really have the nerve to suggest "retiring" the idea of entropy? (I actually do not believe that we abandon old ideas before new ones are developed. Old ideas disappear, or are modified, only when new better ideas are developed. They are never just retired.) So, no, we should not retire entropy, but perhaps treat it with a little less importance, and recognize the paradox it creates.

Entropy, the measure of the degree of disorder in a system, has held a lofty place in physics, being part of a Law no less (not just a theory). The Second Law of Thermodynamics says that in any closed system entropy always increases with time. Unless work is done to prevent it, a closed system will eventually reach maximum entropy and a state of thermal equilibrium. Max Planck believed that entropy (along with energy) was the most important property of physical systems. Sir Arthur Eddington is quoted as saying that "The law that entropy increases—the second law of thermodynamics—holds, I think, the supreme position among the laws of Nature." But as a young physics student in college I must admit I never understood their excitement (and I was not the only student to be unimpressed). The Second Law seemed of minor importance compared to the First Law of Thermodynamics, the conservation of energy—energy could be transformed into different forms, but it was always conserved. The First Law had beautiful partial differential equations (as did all the conservation equations of physics) whose solutions accurately described and predicted so much of the world, and literally changed all our lives. The Second Law was not a conservation equation and had no beautiful partial differential equations. It wasn't even an equality. Has the idea of entropy and the Second Law had any major affect on science and engineering or changed the world?

The Second Law was a statistical "law", initially a generalization of conclusions reached when looking at the motion of molecules/particles. As students it was easy for us to understand the classic example of how hot (fast moving) molecules on one side of a closed box mixed with cold (slowing moving) molecules on the other side, and why they could not separate again once they were together and all at the same temperature. We understood why it was irreversible. And we understood the concept of the "arrow of time".  Sure, the mathematics of the First Law (and the other conservation equations of physics) worked in both directions of time, but with initial conditions and boundary conditions, we always knew which way things moved. It didn't seem to require another Law. In fact, the Second Law (as applied now to all situations) seemed to be an assumption rather than a Law. Especially when it was applied to an entire Universe, that we understand so little about.

When looking at the Universe (whatever that entails, which may be more than our presently visible/observable universe) the First Law tells us that all the energy in the Universe will be conserved, although it may be converted into various forms. But the Second Law says that at some time in the future no more energy transformations can take place. The Universe will reach some stage of maximum entropy and thermal equilibrium. The Second Law essentially says that the Universe must have had a beginning and a end. That is very difficult to accept. The universe must be timeless, for if there was a beginning what was there before this beginning. Something cannot come out of nothing (and by "nothing" I mean the lack of anything, even things we do not know about yet).

Of course, the present Big Bang theory has a beginning (of sorts) and our present form of the universe has apparently expanded out from a singularity, but we do not know what came before that, and oscillating models of the universe are being proposed, so that the Universe is timeless. With such models, if entropy is very high at the end of our universe and was very low at the beginning of our universe, what process could essentially reset entropy to a low value? As relates to an oscillating universe, should entropy perhaps really be conserved somehow? Could there be some type of energy conversion that does not require work (in our classical sense)? Could the Universe actually be the one and only possible perpetual motion machine (forbidden by the Second Law)? If existence is endless in time, it would seem so.

The whole idea of entropy has always felt wrong or misplaced in other ways also. We talk about the Universe going from order to disorder. Yet this supposed order is merely because all the matter of the universe was compressed together in some tiny volume/singularity and when it expanded out there was less order because the particles were more spread out. And yet order is being created all the time.

The greatest result of our expanding and evolving universe is the great and ever increasing complexity that has resulted, first, from gravity condensing matter, then supernova explosions creating higher number elements, then from chemical evolution, and then, most dramatically, from biological evolution (driven by natural selection), culminating in self-reproducing life and eventually the incredible complexity of our brains. Complexity is synonymous with low entropy. The expanding universe has countless small (relative to the size of the universe) pockets of extremely low entropy surrounded by vast areas of higher entropy (much of which resulted from the creation of these low entropy areas). Are the higher orders of complexity (and thus lower orders of entropy) taken into account when trying to balance the entropy of the Universe? There are in fact many scientific papers written today in cosmology trying to sum up the Universe’s total entropy, with formulas that could end up being incrdibly too simple to account for all the (as yet unknown) physics going on in our strange Universe.

We cannot retire entropy, but should we maybe rethink it?

I do not propose we should do away with the study of change, the area under the curve, or bury Isaac Newton and Gottfried Leibniz. However, for decades now, learning calculus has been the passing requirement for entry into modern fields of study, by combining the rigorous requirements of science, technology, engineering and math. Universities still carry on the tradition that undergraduates are required to take anywhere from one to three semesters of calculus as a pure math discipline. This is typically learning complex math concepts uncontextualized, removed from practical applications and heavily emphasizing proofs and theorems.

Because of this, calculus has become a hazing ritual for those interested in going into one of the most needed fields today: computer science. Calculus has very little relevance to the day-to-day work of many coders, hackers and entreprenuers, yet poses a significant recruiting barrier to fill in sorely needed ranks in today's modern digital workforce. And for what reason?

This is particularly urgent in the area of programming and coding. Undergraduate computer science programs are starting to bounce back from a dearth of enrollment that plagued them in the early Internet era, but it could do a lot more to fill the ranks. Some of this is due to a lingering view that computer science is an extension of mathematics, from an era when computers were primarily crafted as the ultimate calculators. 

Calculus remains in many curricula as more of a rite of passage than for any particular need. It is one way of problem solving and it is a bellwether for the ability to absorb more complex ideas and concepts. But holding it up as a universal obstacle course through which one must pass to program and code is counterproductive, yet the bulk of computer science programs geared towards undergraduate education require it. Leaving in this obtuse math requirement is lazy curricular thinking. It sticks with a model that weeds out people for no good reason related to their ability to program.

This gets us to ask the question: What makes for good programmers? The ability to deconstruct complex problems into a series of smaller, doable ones. A proficiency to think procedurally on systems and structures. The ability to manipulate bits and do amazing things with them. 

If calculus is not a good fit for these, what should replace it? Discrete math, combinatorics, computability, graph theory are far more important than calculus. These are all standard, necessary and immensely relevant fields in most modern computer science programs, but they typically come after the calculus requirement gauntlet.

People are finding other formal and peer-learning methods to pick up coding outside the higher education environment: meetups, code-a-thons, online courses, video tutorials. Moving past the calculus would bring these folks into the fold earlier and more methodically.

Relaxing the calculus requirement does not mean we turn universities into trade schools. We still want our research scientists in training and our Ph.D. candidates in STEM to know and master calculus, linear algebra and differential equations. But for too long, calculus has served as a choke point for training digital-savvy self-starting innovators. 

Clemson University experimented with moving calculus further down the curriculum, not as a prerequisite, but as a class in sync with the need for it in other STEM classes. Its 2004 longitudinal study showed, "a statistically significant improvement in retention in engineering" when it reconfigured its approach to introducing math in later semesters. We need more of these experiments and more radical curricular thinking to get past the same prerequisite model that has dominated the field for decades. Sadly, the structure and administration of academia makes it hard to do this.

How can so many people be interested in coding and programming, yet not be served by our top institutions of higher learning? We have not evolved with the times by treating computer science largely as a STEM discipline, instead of thinking of it as a whole new capability that cuts across every field in academia. The sooner we evolve beyond STEM-oriented thinking, the better.

Since long before Erwin Schrödinger's seminal 1944 work, "What Is Life?", physicists have aspired to rigorously define the characteristics that distinguish some matter as living and other matter as not. However, the analogous task of identifying the universally distinguishing physical properties of intelligence has remained largely underappreciated. 

Based on recent discoveries, I have now come to suspect that the reason for this lack of progress in physically defining intelligence is due to the entire scientific concept of treating intelligence as a static property—rather than a dynamical process—being ready for retirement.

In particular, recent results have shown that an extremely rudimentary physical process called causal entropic forcing is able to replicate model versions of signature cognitive adaptive behaviors seen previously only in humans and certain non-human animal intelligence tests. These findings collectively suggest that a variety of key characteristics associated with human intelligence, including upright walking, tool use, and social cooperation, should instead be viewed as side effects of a deeper dynamical process that attempts to maximize future freedom of action. This freedom-maximizing process can only be meaningfully said to exist over an extended time period, and as such, is not a static property.

It's time we retired studying intelligence as a property.

There is a persistent belief in our society that morality is acquired slowly and at considerable effort after birth. That is, it is common to view young children as moral "blank slates," beginning life with no real moral leanings of any kind. On this viewpoint, children first encounter the moral world in person, via their own experiences and observations. Children then actively (or passively, but fewer scholars believe this today) combine such experiences and observations with advances in impulse control, perspective taking, and complex reasoning, allowing them to become more and more "moral" over time.

I think that moral blank slate-ism should be retired. First, though it works well with a picture of infants as "blooming, buzzing confusions" and of toddlers as selfish egoists, developmental psychological research from (at least) the last decade suggests that neither picture is true. For instance, by 3 months of age infants can already process prosocial and antisocial interactions between unknown third parties, preferring those who help, versus hinder, someone to achieve a goal. Indeed, after viewing such interactions, 3-month-olds show a highly reliable tendency to look at the Helper over the Hinderer, and 4.5-month-olds (who can reach) show the same tendency to reach for Helpers. Most strikingly, infants' preferences do not seem to reflect simply preferring those who make good things happen (what we might call an "outcome bias"): in the first year infants prefer those who harm (not help) those who've hindered others before, those with helpful intentions, even if the outcomes they cause are bad.

Rather than selfish egoists, all kinds of prosocial behaviors begin in infancy-helping, sharing, informing, etc. Though these behaviors may result from intensive early socialization, research suggests that infants and toddlers are internally, rather than externally, motivated to be prosocial. For instance, infants help and give without being prompted, and toddlers will actually choose to help over doing other (really) fun things. These behaviors may result from different emotional states: toddlers are negatively aroused by seeing others in need, whereas they find helping others (even at a cost to themselves) emotionally rewarding.

The second reason that I think moral blank slate-ism should be retired is that (because morality is born from experience) it leads us to attribute differences in moral outcomes to differences in experience. This leads to the notion that all of us can be led to be appropriately moral, given the right (and none of the wrong) inputs. Moral failings, then, result from flawed inputs.

Obviously experience plays a critical role in moral development: countless studies indicate some causal relationship between experiences relevant to morality (parenting styles, observed violence, abuse, etc.) and moral outcomes. But consider Dylan Klebold and Eric Harris, shooters at Columbine High School in 1999. They were just the first two of what is now a painfully long list of mass murders of children, by children, in North America. After Columbine, people said that Dylan and Eric played too many violent video games, were bullied in school, or even that their parents hadn't bothered to teach them right from wrong. The first two things certainly happened (probably not the third); but the rate of video game playing and bullying in children is extremely high. What about the 99.9999% of children today who do NOT shoot up their schools? What was different about Eric and Dylan?

Eric was a psychopath. Psychopaths are extremely low on empathy, and (perhaps) as a result, don't mind killing people for fun—the rate of psychopaths in the population of murderers is much higher than average. Psychopathy is a developmental disorder, and is considered one of the least treatable of the mental illnesses. Curiously, it is also one of the latest diagnosed - typically not until adolescence or adulthood. Since we know interventions need to be started early to be effective (think of recent gains in autism treatment from earlier diagnosis), it's perhaps a given that a late-diagnosed disorder would not be susceptible to intervention. My worry, in a nutshell, is that moral blank slate-ism's focus on experience makes us reluctant to identify enduring, temperament-based predictors of antisociality in our children, and when we do it's too late to treat them. It is not that I don't share the reluctance to "pigeonhole" kids—but it is curious that blaming individual differences on varied experience may be preventing us from using experience to level the playing field through intervention.

Other studies show a link between very early measures of empathy and antisocial behavior later in life within the typically-developing population. These measures usually involve someone acting distressed in front of the infant, and determining whether the infant looks at him/her with concern/distress or not. Most infants do, most of the time. A recent study found that non-abused 6-14-month-olds who showed "disregard" for others' distress were significantly more likely to be antisocial as adolescents. This result suggests that, outside of psychopathy per se, warning signs for antisocial behavior may emerge extremely early, before experience could have played much of a role.

Again, experience matters. Several studies have now documented that experience may influence moral outcomes via a "gene-environment interaction." That is, rather than a simple equation in which, say, adverse experiences lead to antisocial children: [child + abuse – ameliorating experiences = violence], the relationship between abuse and antisocial behavior is only observed in children with particular versions of various genes known to regulate certain social hormones. That is, whether they have been abused or not, children with the "safe" gene alleles are all about equally (un)likely to engage in antisocial behavior. Children with the "at risk" alleles, on the other hand, are more susceptible to the damages of abuse.

To close, I think the common view of infants as moral blank slates has led to a mistaken view of the infant and how moral behavior and cognition work. To the extent that understanding how moral development begins, and understanding all of the causes of individual differences, makes us better equipped to address various moral-developmental paths, I think moral blank slate-ism should be retired.

Physics has a time-honored tradition of laughing in the face of our most basic intuitions. Einstein's relativity forced us to retire our notions of absolute space and time, while quantum mechanics forced us to retire our notions of pretty much everything else. Still, one stubborn idea has stood steadfast through it all: the universe.

Sure, our picture of the universe has evolved over the years—its history dynamic, its origin inflating, its expansion accelerating. It has even been downgraded to just one in a multiverse of infinite universes forever divided by event horizons. But still we've clung to the belief that here, as residents in the Milky Way, we all live in a single spacetime, our shared corner of the cosmos—our universe.

In recent years, however, the concept of a single, shared spacetime has sent physics spiraling into paradox. The first sign that something was amiss came from Stephen Hawking's landmark work in the 1970s showing that black holes radiate and evaporate, disappearing from the universe and purportedly taking some quantum information with them. Quantum mechanics, however, is predicated upon the principle that information can never be lost.

Here was the conundrum. Once information falls into a black hole, it can't climb back out without traveling faster than light and violating relativity. Therefore, the only way to save it is to show that it never fell into the black hole in the first place. From the point of view of an accelerated observer who remains outside the black hole, that's not hard to do. Thanks to relativistic effects, from his vantage point, the information stretches and slows as it approaches the black hole, then burns to scrambled ash in the heat of the Hawking radiation before it ever crosses the horizon. It's a different story, however, for the inertial, infalling observer, who plunges into the black hole, passing through the horizon without noticing any weird relativistic effects or Hawking radiation, courtesy of Einstein's equivalence principle. For him, information better fall into the black hole, or relativity is in trouble. In other words, in order to uphold all the laws of physics, one copy of the bit of information has to remain outside the black hole while its clone falls inside. Oh, and one last thing—quantum mechanics forbids cloning.

Leonard Susskind eventually solved the information paradox by insisting that we restrict our description of the world to either the region of spacetime outside the black hole's horizon or to the interior of the black hole. Either one is consistent—it's only when you talk about both that you violate the laws of physics. This "horizon complementarity," as it became known, tells us that the inside and outside of the black hole are not part and parcel of a single universe. They are two universes, but not in the same breath.

Horizon complementarity kept paradox at bay until last year, when the physics community was shaken up by a new conundrum more harrowing still— the so-called firewall paradox. Here, our two observers find themselves with contradictory quantum descriptions of a single bit of information, but now the contradiction occurs while both observers are still outside the horizon, before the inertial observer falls in. That is, it occurs while they're still supposedly in the same universe.

Physicists are beginning to think that the best solution to the firewall paradox may be to adopt "strong complementarity"—that is, to restrict our descriptions not merely to spacetime regions separated by horizons, but to the reference frames of individual observers, wherever they are. As if each observer has his or her own universe.

Ordinary horizon complementarity had already undermined the possibility of a multiverse. If you violate physics by describing two regions separated by a horizon, imagine what happens when you describe infinite regions separated by infinite horizons! Now, strong complementarity is undermining the possibility of a single, shared universe. On glance, you'd think it would create its own kind of multiverse, but it doesn't. Yes, there are multiple observers, and yes, any observer's universe is as good as any other. But if you want to stay on the right side of the laws of physics, you can only talk about one at a time. Which means, really, that only one exists at a time. It's cosmic solipsism.

Sending the universe into early retirement is a pretty radical move, so it better buy us something pretty in the way of scientific advancement. I think it does. For one, it might shed some light on the disconcerting low quadrupole coincidence—the fact that the cosmic microwave background radiation shows no temperature fluctuations at scales larger than 60 degrees on the sky, capping the size of space at precisely the size of our observable universe – as if reality abruptly stops at the edge of an observer's reference frame.

More importantly, it could offer us a better conceptual grasp of quantum mechanics. Quantum mechanics defies understanding because it allows things to hover in superpositions of mutually exclusive states, like when a photon goes through this slit and that slit, or when a cat is simultaneously dead and alive. It balks at our Boolean logic, it laughs at the law of the excluded middle. Worse, when we actually observe something, the superposition vanishes and a single reality miraculously unfurls.

In light of the universe's retirement, this all looks slightly less miraculous. After all, superpositions are really superpositions of reference frames. In any single reference frame, an animal's vitals are well defined. Cats are only alive and dead when you try to piece together multiple frames under the false assumption that they're all part of the same universe.

Finally, the universe's retirement might offer some guidance as physicists push forward with the program of quantum gravity. For instance, if each observer has his or her own universe, then each observer has his or her own Hilbert space, his or her own cosmic horizon and his or her own version of holography, in which case what we need from a theory of quantum gravity is a set of consistency conditions that can relate what different observers can operationally measure.

Adjusting our intuitions and adapting to the strange truths uncovered by physics is never easy. But we may just have to come around to the notion that there's my universe, and there's your universe—but there's no such thing as the universe.

Nature and nature's laws lay hid in night;
God said "Let Newton be" and all was light. 
—Alexander Pope

The breathtaking advance of scientific discovery has the unknown on the run. Not so long ago, the Creation was 8,000 years old and Heaven hovered a few thousand miles above our heads. Now Earth is 4.5 billion years old and the observable Universe spans 92 billion light years. Pick any scientific field and the story is the same, with new discoveries—and new life-touching wonders—arriving almost daily. Like Pope, we marvel at how hidden Nature is revealed in scientific light.

Our growing corpus of scientific knowledge evokes Teilhard de Chardin's arresting metaphor of the noosphere, the growing sphere of human understanding and thought. In our optimism, this sphere is like an expanding bubble of light in the darkness of ignorance.

Our optimism leads us to focus on the contents of this sphere, but its surface is more important for it is where knowledge ends and mystery begins. As our scientific knowledge expands, contact with the unknown grows as well. The result is not merely that we have mastered more knowledge (the sphere's volume), but we have encountered an ever-expanding body of previously unimaginable mysteries. A century ago, astronomers wondered whether our galaxy constituted the entire universe; now they tell us we probably live in an archipelago of universes.

The science establishment justifies its existence with the big idea that it offers answers and ultimately solutions. But privately, every scientist knows that what science really does is discover the profundity of our ignorance. The growing sphere of scientific knowledge is not Pope's night-dispelling light, but a campfire glow in the gloom of vast mystery. Touting discoveries helps secure finding and gain tenure, put perhaps the time has come to retire discovery as the ultimate measure of scientific progress. Let us measure progress not by what is discovered, but rather by the growing list of mysteries that remind us of how little we really know.

The idea that people operate mainly in the service of narrow self-interest is already moribund, as social psychology and behavioral economics have shown. We now know that people are not rational actors, instead often operating on automatic, based on bias, or happy with hunches. Still, it's not enough to make us smarter robots, or to accept that we are flawed. The rational actor's corollary—all we need is to show more competence—also needs to be laid to rest. Even regular people who are not classical economists sometimes think that sheer cut-throat competence would be enough—on the job, in the marketplace, in school, and even at home.

Talent and problem-solving ability indeed are crucial, of course. But there's more.

We are social beings, embedded in a human environment even more than in a natural or a constructed one. If other people are our ecological niche, then we need to understand how to live amongst them. We do this by figuring out two things about them, not only how good they will be at getting where they want to go, but also: Where are they trying to go?

People are a miracle of self-propelled agency. Not for nothing are humans attuned to each other’s intentions.  We need—and our ancestors needed—to know whether others have friendly or hostile intentions toward us. In my world, we call this a person’s warmth, and others have called it trustworthiness, morality, communality, or worthy intentions.

People are most effective in social life if we are—and show ourselves to be—both warm and competent. This is not to say that we always get it right, but the intent and the effort must be there. This is also not to say that love is enough, because we do have to prove capable to act on our worthy intentions. The warmth-competence combination supports both short-term cooperation and long-term loyalty. In the end, it's time to recognize that people survive and thrive with both heart and mind.

Human beings are the unequivocal world champions of niceness. We act kindly not only towards people who belong to our own social groups or can reciprocate our generosity, but also towards strangers thousands of miles away who will never know we helped them. All around the world, people sacrifice their resources, well being, and even their lives in the service of others.

For behavioral scientists, the great and terrible thing about altruism—behavior that helps others at a cost to the helper—is its inherent contradictions. Prosocial behaviors appear to contradict economic and evolutionary axioms about how humans should behave: selfishly, nasty and brutish, red in tooth and claw, or whichever catchphrase you prefer. After all, how could organisms that sacrifice for others survive, and why would nature endow us with such self-defeating tendencies?

In recent decades, researchers have largely solved this problem, offering reasons that perfectly self-oriented organisms might behave altruistically. Solving the "altruism paradox" becomes trivial when individuals help family members (thus advancing helpers' genes) or others who can reciprocate (increasing helpers' chances of future gains) or help others in public (enhancing helpers' reputation). We see these motives at work all around us, in parenting, favors for bosses, and opera patrons donating just enough to get their names on the "gold donor" plaques in theater lobbies.

More recently, my colleagues and I, as well as other neuroscientists, uncovered another "selfish"motive for altruism: helping others simply feels good. Giving to others engages brain structures associated with reward and motivation, similar to those that come online when people see beautiful faces, win money, or eat chocolate. Further, "reward-related" brain activity associated with helping track people's willingness to act generously. This doesn't mean that altruism is the psychological equivalent of Ben and Jerry's, but it does provide converging evidence for James Andreoni's idea that generosity produces a hedonic "warm glow."

One common response I receive when presenting this work has grown increasingly bothersome. Often, an audience member will claim that if people experience helping as rewarding, then their actions are not "really" altruistic at all. The claim as I understand it traces back to the Kantian notion—embedded in the "cost to the helper" section of altruism's definition—that virtuous action is motivated by principle alone, and that cashing in on that action, whether through material gain or psychological pleasure, disqualifies it as being virtuous. Oftentimes, this contention devolves into long, animated, and (to my mind) useless attempts to find space for true altruism amid an avalanche of ulterior motives.

This altruism hierarchy, with a near-mystic "true" altruism residing somewhere in the distance and our sullied attempts at it crowding real life, is widespread. It also plays out directly in people’s judgment. For instance, a study published yesterday by George Newman and Daylain Cain demonstrated that people judge people as less moral when they act altruistically and gain in the process, than when they gain from clearly non-altruistic behavior. In essence, people view "tainted altruism" as worse than no altruism at all. 

I think the altruism hierarchy should be retired. I do believe that people often help others absent the goal of any personal gain. Dan Batson, Philip Kitcher, and others have done the philosophical and empirical work of distinguishing other-oriented and self-oriented motives for prosociality. But I also believe that the reservation of terms such as "pure" or "real" for actions bereft of any personal gain is less than useful.

This is for two reasons, which both connect with the broader idea of self-negation. First, the altruism hierarchy is logically self-negating. Attempts to identify true altruism often boil down to redacting motivation from behavior altogether. The story goes that in order to be pure, helping others must dissociate from personal desire (to kiss up, look good, feel rewarded, and so forth). But it is logically fallacious to think of any human behavior as amotivated. De facto, when people engage in actions, it is because they want to. This could represent an overt desire to gain personally, but could also stem from previous learning (for instance, that helping others in the past has felt good or provided personal gain) that translates into an intuitive prosocial preference. Disqualifying self-motivated behavior from being altruistic obscures the universality of motivation in producing all behavior, generous or not.

Second, the altruism hierarchy is morally self-negating. It often appears to me that critics of "impure" altruism chide helpers for acting in human ways, for instance by doing things that feel good. The ideal, then, seems to entail acting altruistically while not enjoying those actions one bit. To me, this is no ideal at all. I think it's profound and downright beautiful to think that our core emotional makeup can be tuned towards others, causing us to feel good when we do. Color me selfish, but I'd take that impure altruism over a de-enervated, floating ideal any day.

In evolution classes, Lamarckism–the notion promoted by Lamarck that an organism could acquire a trait during its lifetime and pass that trait to its offspring–is usually briefly discussed and often ridiculed. Darwin's theory of natural selection is presented as the one true mechanism of evolutionary change.

In Lamarck's famous example, giraffes that ate leaves from higher branches could potentially grow longer necks than giraffes that ate from lower branches, and pass on their longer necks to their offspring. The inheritance of acquired characteristics was originally considered a legitimate theory of evolutionary change, with even Darwin proposing his own version of how organisms might inherit acquired characteristics.

Experimental hints of intergenerational transfer of acquired traits came in 1923 when Pavlov reported that while his first generation of white mice needed 300 trials to learn where he hid food, their offspring needed only 100, and their grandchildren only 30. But Pavlov's description didn't make clear whether the mice were all housed together allowing for some communication between mice or other kinds of learning. Still other early studies of potential intergenerational trait transfer in plants, insects, and fish also suffered from alternative interpretations or poorly controlled experiments. Lamarckism was dismissed.

But more recent studies–using modern reproduction techniques like in vitro fertilization and proper controls–can physically isolate generations from each other and rule out any kind of social transmission or learning. For example, mice that were fear-conditioned to an otherwise neutral odor produced baby mice that also feared that odor. Their grandbaby mice feared it too. But unlike in Pavlov's studies, communication couldn’t be the explanation. Because the mice never fraternized, and cross-fostering experiments further ruled out social transmission, the newly acquired specific fear had to be encoded in their biological material. (Biochemical analysis showed that the relevant change was likely in the methylation of olfactory reception genes in the sperm of the parents and offspring. Methylation is one example of an epigenetic mechanism.) Natural selection is still the primary shaper of evolutionary change, but the inheritance of acquired traits might play an important role too.

These findings fit in a relatively new field of study called epigenetics. Epigenetic control of gene expression contributes to cells in a single organism (which share the same DNA sequence) developing differently into e.g. heart cells or neurons. But the last decade has shown actual evidence–and possible mechanisms–for how the environment and the organism's behavior in it might cause heritable changes in gene expression (with no change in the DNA sequence) that are passed onto offspring. In recent years, we have seen evidence of epigenetic inheritance across a wide range of morphological, metabolic, and even behavioral traits.

The intergenerational transmission of acquired traits is making a comeback as a potential mechanism of evolution. It also opens up the interesting possibility that better diet, exercise, and education which we thought couldn't affect the next generation–except with luck through good example–actually could.
 

One idea in the study of emotion and its impact on psychological health is overdue for retirement: that negative emotions (like sadness or fear) are inherently bad or maladaptive for our psychological well-being, and positive emotions (like happiness or joy) are inherently good or adaptive. Such value judgments are to be understood, within the framework of affective science, as depending on whether an emotion impedes or fosters a person's ability to pursue goals, attain resources, and function effectively within society. Claims of the sort "sadness is inherently bad" or "happiness is inherently good" must be abandoned in light of burgeoning advances in the scientific study of human emotion.

Let's start with negative emotions. Early hedonic theories defined well-being, in part, as the relative absence of negative emotion. Empirically based treatments like cognitive-behavioral therapy also focus heavily on the reduction of negative feelings and moods as part of enhancing well-being. Yet a strong body of scientific work suggests that negative emotions are essential to our psychological well-being. Here are 3 examples. First, from an evolutionary perspective, negative emotions aid in our survival—they provide important clues to threats or problems that need our attention (such as an unhealthy relationship or dangerous situation). Second, negative emotions help us focus: they facilitate more detailed and analytic thinking, reduce stereotypic thinking, enhance eyewitness memory, and promote persistence on challenging cognitive tasks. Third, attempting to thwart or suppress negative emotions—rather than accept and appreciate them—paradoxically backfires and increases feelings of distress and intensifies clinical symptoms of substance abuse, overeating, and even suicidal ideation. Counter to these hedonic theories of well-being, negative emotions are hence not inherently bad for us. Moreover, the relative absence of them predicts poorer psychological adjustment.

Positive emotions have been conceptualized as pleasant or positively valenced states that motivate us to pursue goal-directed behavior. A longstanding scientific tradition has focused on the benefits of positive emotions, ranging from cognitive benefits such as enhanced creativity, social benefits like fostering relationship satisfaction and prosocial behavior, and physical health benefits such as enhanced cardiovascular health. From this work has emerged the assumption—both implicitly and explicitly—that positive emotional states should always be maximized. This has fueled the birth of entire subdisciplines and garnered momentous popular attention. But there's a mounting body of work against the claim that positive emotions are inherently good. First, positive emotions foster more self-focused behavior, including increased selfishness, greater stereotyping of out-group members, increased cheating and dishonesty, and decreased empathic accuracy in some contexts. Second, positive emotions are associated with greater distractibility and impaired performance on detail-oriented cognitive tasks. Third, because positive emotion may promote decreased inhibition it has been associated with greater risk-taking behaviors and mortality rates. Indeed, the presence of positive emotions is not always adaptive and sometimes can impede our well-being and even survival.

We are left to conclude that valence is not value: we cannot infer value judgments about emotions on the basis of their positive or negative valence. There is no intrinsic goodness or badness of an emotion merely because of its positivity or negativity, respectively. Instead, we must refine specific value-based determinants for an emotion's functionality. Towards this end, emerging research highlights critical variables to focus on. Importantly, the context in which an emotion unfolds can determine whether it helps or hinders an individual's goal, or which types of emotion regulatory strategies (reappraising or distracting) will best match the situation. Related, the degree of psychological flexibility someone possesses—including how quickly one can shift emotions or rebound from a stressful situation—promotes critical clinical health outcomes. Likewise, we find that psychological well-being is not entirely determined by the presence of one type or kind of an emotion but rather an ability to experience a rich diversity of both positive and negative emotions. Whether or not an emotion is "good" or "bad" seems to have surprisingly little to do with the emotion itself, but rather how mindfully we ride the ebbs and tides of our rich emotional life.
 

We humans have had a tough time coping with our unremarkable place in the grand scheme of things. First Copernicus trashed our belief that we live at the centre of the universe, followed shortly thereafter by Herschel and co. who suggested that our sun was not at the centre of it either; then Darwin came along and showed that according to our biological heritage, we are just another animal. But we have clung on for dear life to one remaining belief about our specialness; that we, and we alone, have conscious minds. It is time to retire, or indeed euthanize and cremate, this anthropocentric pomposity.

Descartes thought of animals as mindless automata, and vivisection without anesthetic was common among early medical researchers. Throughout much of the 20^th century, psychologists believed that animals—while clearly resembling humans in their neuroanatomy—perform their activities essentially unthinkingly, a viewpoint that reached its zenith (or perhaps, my preferred word, nadir) in Behaviorism, the psychological doctrine that rejects inner mental states like plans and purposes as unable to be studied, or—in the radical version—as not even existing. The undeniable fact that humans have inner mental states and purposes was attributed to our special psychological status: we have language, and therefore we are different. Animals remain essentially Cartesian automata though.

Many of our scientific experiments have validated this view. Rats in a skinner box (named after the most radical Behaviorist of all, BF Skinner) do indeed appear to act mindlessly—they press the levers over and over again, they seem slow to learn, slow to adapt to new contingencies, they don't really seem to think about what they are doing. Furthermore, in further testament to its mindlessness, quite large regions of the brain can be damaged without affecting performance. Rats in a maze seem similarly clueless—they take a long time to learn (weeks to months sometimes) and a long time to adapt to change. Clearly, rats and other animals are stupid—and more than that, they are mindless.

Fond though I am of rats, I would not wish to defend their intelligence. But the assumption that they do not have inner mental states needs examining. Behaviorism arose from the argument of parsimony (Occam's razor)—why postulate mental states in animals when their behavior can be explained in simpler ways? The success of Behaviorism arose in part from the fact that the kinds of behaviors studied back then could, indeed, be explained by operation of mindless, automatic processes. It does not take deep reflection to press a lever in a skinner box any more than it does to key in your PIN. But in the mid-20th century, a development occurred that began to overturn the view that all behavior is mindless. This development was single neuron recording, the ability to follow the activity of individual brain cells—the little cogs and sprockets that make up the workings of the actual brain. Using this technique, behavioral electrophysiologists have been able to actually see, for themselves, the operation of inner mental processes in animals.

The most striking discovery along these lines has been the place cells, neurons in the hippocampus, a small but vitally important structure located deep in the temporal lobes. Place cells are (we now know) key components of an internal representation of the environment—often called the cognitive map—which forms when an animal explores a new place, and which reactivates when the animal re-enters that place. Single neuron recording shows us that this map forms spontaneously, in the absence of reward and independently of the animal's behavior. When an animal is choosing between alternative routes to a goal, place cells representing the alternative possibilities become spontaneously active even though the animal has not gone there yet—as if the animal is thinking about the choices. Place cells certainly seem to be an internal representation: furthermore, we humans have them too, and human place cells reactivate when people think about places.

Place cells may well be an internal representation of the kind eschewed by behaviorists, but does this mean, though, that rats and other animals have minds? Not necessarily… place cells could still be part of an automatic and unconscious representation system. Our own ability to conjure up remembered or imagined images "in our mind's eye" to use for recollection or planning might still be special. This seems unlikely though, doesn't it? Mindlessness would only be a parsimonious conjecture if we didn't know about our own minds. But we do… and we know that we are extraordinarily like animals in every respect, right down to the place cells. To suppose that the ability to mentally represent the outside world sprang into existence, fully formed, in the evolutionary transition (if the concept of "transition" even makes sense) between animals and humans seems improbable at best, deeply arrogant at worst. When we look into the animal brain we see the same things we see in our own brains. Of course we do, because we are just animals after all. It is time to admit yet again that we are not all that special. If we have minds, creatures with brains very like ours probably do too. Unravelling the mechanisms of these minds will be the great challenge for the coming decades.
 

Max Planck famously described the progress of quantum physics as being "one funeral at a time" as the old-school physicists died off and their jobs were taken by young men who followed the new quantum religion.

This brutal style of scientific revolution has left some rather rigid scar tissue. For many years it has been almost taboo to suggest that the questions at the foundations of quantum mechanics might actually have an answer. Yet new results in different areas of physics, chemistry and engineering are beginning to suggest that there might possibly be an answer after all.

At the Solvay Conference in 1927, Niels Bohr and Werner Heisenberg out-debated Albert Einstein and Louis de Broglie; they persuaded the world that we should just take the tools of the new quantum mechanics on trust rather than trying to derive them from underlying classical principles. This Copenhagen school, the "shut up and calculate" school, of quantum mechanics rapidly became the orthodoxy. It was reinforced when calculations by John Bell were experimentally verified by Alain Aspect in 1982 and appeared to show that reality at the quantum level could not be both local and causal.

While some philosophers of physics toyed with exotic interpretations of quantum mechanics, most physicists shrugged; they accepted that quantum foundations were a "certified insoluble" problem, and told their graduate students not to even think about wasting their lives on that. Others just loved the idea that physics proves the world is too complex to understand, and that the proof is beyond the comprehension of outsiders. Physicists could be the new high priests as the quantum became the core magic. Recently we've got quantum with everything, from cryptography to biology; the word has become a magic spell for fundraising. So long as no-one dared challenge this for fear of being thought a crank or dismissed as an outsider, we were stuck.

Things are starting to change. In physics, Yves Couder and Emmanuel Fort found that bouncing droplets on a bath of vibrating oil mimic many phenomena previously thought unique to the quantum world, including single-slit and double-slit refraction, tunnelling and quantised energy levels. In chemistry, Masanao Ozawa and Werner Hofer have shown that the uncertainty principle is only approximately true: modern scanning probe microscopes can often measure the position and momentum of atoms slightly more accurately than Heisenberg predicted – which should worry people who claim that quantum cryptography is "provably" secure! In computing, the promised quantum computers are still stuck at factoring 15, despite hundreds of millions in research funding over almost twenty years. And the physicist Theo van Nieuwenhuizen has pointed out a contextuality loophole in Bell's theorem that looks rather hard to fix.

There's a striking parallel with another big problem in science—consciousness. For years, the few first-division academics who dared tackle such problems tended to be near retirement and famous enough to shrug off disapproval; just as Dan Dennett and Nick Humphrey wrote on consciousness, Tony Leggett and Gerard 't Hooft wrote on quantum foundations. So the flame was kept alight. But it's time to bring some tinder. Viennese physicists have now organised two symposia on emergent quantum mechanics, as people finally dare to wrestle with what might be going on down there.

So the idea I'd like to retire is the idea that some questions are just too big for normal working scientists to tackle. Old-timers should not try to erect taboos around the problems that eluded us. We must cheerfully challenge the young: "prove us wrong!" As for young scientists, they should dare to dream, and to aim high.

It is time for science to retire the fiction of statistical independence. 

The world is massively interconnected through causal chains. Gravity alone causally connects all objects with mass. The world is even more massively correlated with itself. It is a truism that statistical correlation does not imply causality. But it is a mathematical fact that statistical independence implies no correlation at all. None. Yet events routinely correlate with one another. The whole focus of most big-data algorithms is to uncover just such correlations in ever larger data sets. 

Statistical independence also underlies most modern statistical sampling techniques. It is often part of the very definition of a random sample. It underlies the old-school confidence intervals used in political polls and in some medical studies. It even underlies the distribution-free bootstraps or simulated data sets that increasingly replace those old-school techniques. 

White noise is what statistical independence should sound like. 

The hisses and pops and crackles of true white-noise samples are all statistically independent of one another. This holds no matter how close the noise samples are in time. That means the frequency spectrum of white noise is flat across the entire spectrum. Such a process does not exist because it would require infinite energy. That has not stopped generations of scientists and engineers from assuming that white noise contaminates measured signals and communications. 

Real noise samples are not independent. They correlate to some degree. Even the thermal noise that bedevils electronic circuits and radar devices has only an approximately flat frequency spectrum and then over only part of the spectrum. Real noise does not have a flat spectrum. Nor does it have infinite energy. So real noise is colored pink or brown or some other strained color metaphor that depends on how far the correlation reaches among the noise samples. Real noise is not and cannot be white.

A revealing problem is that there are few tests for statistical independence. Most tests tell at most whether two variables (not the data itself) are independent. And most scientists would be hard pressed to name even them. 

So the overwhelming common practice is simply to assume that sampled events are independent. Just assume that the data is white. Just assume that the data are not only from the same probability distribution but that the data are statistically independent. An easy justification for this is that almost everyone else does it and it's in the textbooks. This assumption has to be one of the most widespread instances of groupthink in all of science.

The reason we so often assume statistical independence is not its real-world accuracy. We assume statistical independence because of its armchair appeal: It makes the math easy. It often makes the intractable tractable.  

Statistical independence splits compound probabilities into products of individual probabilities. (Then often a logarithm converts the probability product into a sum because it is easier still to work with sums than products). And it is far easier to lecture would-be gamblers that successive coin flips are independent than to conduct the fairly extensive experiments with conditional probabilities required to factually establish such a remarkable property. That holds because in general a compound or joint probability always splits into a product of conditional probabilities. The so-called multiplication rule guarantees this factorization. Independence further reduces the conditional probabilities to unconditional ones. Removing the conditioning removes the statistical dependency. 

Andrei Markov made the first great advance over independence or whiteness when he studied events that statistically depend on only the immediate past. That was over a century ago. 

We still wrestle with the math of such Markov chains and find surprises. The Google search algorithm rests in large part on finding the equilibrium eigenvector of a finite Markov chain. The search model assumes that Internet surfers jump at random from web page to web page much as a frog hops from lily pad to lily pad. The jumps and hops are not statistically independent. But they are probabilistic. The next web page you choose depends on the page you are now looking at. Real web surfing may well involve probabilistic dependencies that reach back to several visited web sites. It is a good bet that the human mind is not a Markov process. Yet relaxing independence to even one-step or two-step Markov dependency has proven a powerful way to model diverse streams of data from molecular diffusion to speech translation.

It takes work to go beyond the simple Markov property where the future depends only the present and not on the past. But we have ever more powerful computers that do just such work. And many more insights will surely come from the brains of motivated theoreticians. Giving up the crutch of statistical independence can only spur more such results.

Science needs to take seriously its favorite answer: It depends.

The standard model of particle physics has aesthetic shortcomings that leave us with questions: Why so many free parameters? Why not an elegant, single fundamental force to account for all forces? Why three generations of quarks and leptons? Now that we have a mechanism for how fundamental particles acquire mass, why do they have those particular couplings to the Higgs field, covering such a huge range of masses? Why the even more extreme range of strengths of the fundamental forces? The common potential danger with each of these questions is the answer, "That's just the way it is."

In addition to these aesthetic concerns we have contradictions between observation and prediction in the explored universe: We have not found a source of energy to fuel our accelerating expansion. There is insufficient baryonic matter to explain astronomical observations. Sticking with the topic of matter, we thankfully live in a large pocket of it that shouldn't have survived annihilation. In fact, we see matter dominance everywhere we look and have no sufficient source of matter vs. anti-matter asymmetry to account for this. We may never access solutions to this set of problems, but it is clear that accounting for each of them requires at least a tweak and at best a fundamental re-write of existing models. Their issues go beyond inelegance.

Experimentalists, myself included, have been chasing aesthetically motivated, or partially aesthetically motivated, theories through the data. With a few years of running the Large Hadron Collider at the energy frontier and a host of careful measurements in particle, nuclear and atomic physics carried out all over the globe, large regions of "new physics" parameter space have been recently excluded. Theorists have answered with pivots and extensions, adapting their proposed models in ways that push us to more challenging experimental conditions.

This exchange has felt healthy and has definitely been fun. The close interactions have allowed for fast progress testing new ideas. Even though these searches for non-standard model physics have resulted in new limits rather than discovery, it has been thrilling to make measurements that might provide evidence toward a grand unified theory. However, our current era of scare resources requires tighter thinking. I think it's time to more carefully scrutinize our theoretical foundations.

Of course, including aesthetic considerations in the scientific toolbox has resulted in huge leaps forward. The drive for elegance has repeatedly enabled scientists to uncover underlying structure. The permission to consider aesthetics is part of what drew many of us to becoming scientists in the first place. I'm not arguing to abandon it forever. But we are currently in a data-rich period in particle physics after years (at least at the energy frontier) of being data-poor. Ensuring that data get the final say is more fundamental than anything else in the practice of science and the data we have in-hand have the potential to say a lot about the standard model. There is even more on the line when we consider which experiments to pursue next.

At this stage, with 96% of the universe's content in the dark, it is a mistake for us to put aesthetic concerns in the same realm as contradictions when it comes to theoretical motivation. With no explanation for dark energy, no confirmed detection of dark matter and no sufficient mechanism for matter/anti-matter asymmetry, we have too many gaps to worry about elegance. Theorists will keep pushing on grand unified theories, including developing the mathematics that will enable further progress. Experimentalists have an opportunity and responsibility to provide direction through agnostic hunts for discrepancies between our data and standard model predictions. This includes, of course, measuring the hell out of the newly discovered Higgs Boson.

It is time for us to admit that some of the models we have been chasing from our brilliant theory colleagues might actually be (gorgeous) Hail Mary passes to the universe. Our next significant jump of understanding will likely come because we are forced there by painstakingly determined constraints from the data rather than by a lucky good catch.

It's time for "The Rocket Scientist" to retire.

This is "The Rocket Scientist" of cliche fame: "It doesn't take a Rocket Scientist to know...." 

"The Rocket Scientist" is a personage rather than a principle, and a fictitious personage at that. He (or she) was constructed by popular usage, not by scientists. Still, the cliche perpetuates outdated public perceptions of scientific principles, and that's critical. "The Rocket Scientist" needs a good retirement party.

I'll start with a disclaimer. My dreams of that retirement gala may appear tinged with professional envy. I have never heard anyone say, "It doesn't take an Ethnohistorian to know...."  I never will. So yes, the cliche does slight the humanities—but that's not my concern. Rather, "The Rocket Scientist," as popularly conceived, dangerously slights the sciences. Our earth cannot afford that.

"The Rocket Scientist" stands outside society, frozen on a higher plane. Widely embraced and often repeated, the cliche reflects a general public's comfort with divorcing science from personal experience. The cliche imposes a boundary (of brilliance) between the scientist and everyone else.

This makes for popular movies and television shows, but it's insidious. Artificially constructed boundaries isolate. They focus attention on differences and distinctions. In contrast, it's the exploration of relationships and process that feeds rapid scientific developments today: systems biology, epigenetics, neurology and brain research, astronomy, medicine, quantum physics. Complex relationships also shape the urgent challenges identified in this year's Edge question. Global epidemics, climate change, species extinction, finite resources—these all comprise integral interconnections.

Approaching such problems demands an appreciation of diversity, complexity, relationships and process. Popular understanding of contemporary science demands the same. We can only address urgent global issues when policy-makers see science clearly—when they view diversity, complexity, relationships and process as essential to understanding, rather than as obstacles to it.

At present, though, constructed boundaries pervade our institutions and policy structures, not only our cliches. Examples abound. Universities segment researchers and students into disciplinary compartments with discreet budgetary line items, competing for scarce resources. ("Interdisciplinary" makes a good buzzword, but the paradigm on which our institutions rest militates against it.) The model of nations negotiating as autonomous entities has failed abysmally to address climate change. In my provincial government’s bureaucracy, separate divisions oversee oceans and forests, as if a fatal barrier slices the ecosystem at the tideline.

Time suffers, too. Past gets alienated from present, and present from future, as our society zooms in on short term fiscal and political deadlines. Fragmented time informs all other challenges, and makes them all the more dire.

So much of our society still operates on a paradigm of simplification, compartmentalization and boundaries, when we need a paradigm of diversity, complexity, relationships and process. Our societal structures fundamentally conflict with the messages of contemporary science. How can policy-makers address crucial global issues while ignoring contemporary scientific principles?

The real world plays out as a video. The relationships between frames make the story comprehensible. In contrast, "The Rocket Scientist" stands like a snapshot, fictitiously yet firmly alone on a lofty pinnacle: apart from society, not a part of it. Yes, it's just a cliche, but language matters, and jokes instruct. It's time for "The Rocket Scientist" to retire.

I'll close with another disclaimer. I mean no offense to real rocket scientists. (Some of my best friends have been rocket scientists!) Real rocket scientists exist. They inhabit the real world, with all the attending interconnections, relationships and complexities. "The Rocket Scientist" embodies the opposite. We'll all be well served by that retirement.
 

British Chef Heston Blumenthal's imaginative "Hot and Iced Tea," is a syrupy concoction that's prepared by putting a divider down the middle of a glass, then filling one side with a hot tea and the other with an iced version. Because of the viscous consistency of the liquid, when the divider is removed, the two halves keep separate long enough for a lucky diner to sample a perfectly, and simultaneously, hot and iced tea. When you sip Blumenthal's tea it makes no sense to argue about whether it's really cold or really hot. You could, of course, take care to sip only from the cold side, or only from the hot. But the cup of tea is really both.

I think much of the world, the sciences, certainly the social and behavioral sciences, look more like that cup of tea than we often let on.

We typically assume, for example, that happiness and sadness are polar opposites and, thus, mutually exclusive. But recent research on emotion suggests that positive and negative affects should not be thought of as existing on opposite sides of a continuum, and that, in fact, feelings of happiness and sadness can co-occur. When participants are surveyed immediately after watching certain films, or graduating from college, they are found to feel both profoundly happy and sad. Our emotional experience, it turns out, is a lot like a viscous cup of tea: It can run hot and cold at the same time.

The same can be true of good and evil. Like sipping from the hot or the cold side of the cup, we now know that minor contextual nuance can make all the difference. In one classic study, psychologists Darley and Batson recruited Seminary students to deliver a sermon on the parable of the Good Samaritan. While half the seminarians were told they were comfortably ahead of schedule, others were led to believe they were running late. On their way to give the talk, all participants encountered an ostensibly injured man slumped in a doorway, groaning and needing help. Whereas the majority of those with time to spare stopped to help, a mere 10% of those who were running late stopped, the rest stepping over the victim and rushing along. Notwithstanding their ethical training and biblical scholarship, the minor nuance of a time constraint proved critical to the seminarians' decision to ignore the pleas of a suffering man. Like a high-concept cup of tea, both hot and iced, each of these men were both caring and indifferent, displaying one trait or the other depending on arbitrary twists of fate.

Or consider John Rabe, the bald and bespectacled German engineer, known as "the living Buddha of Nanking." Rabe was the legendary head of the International Safety Zone, who was credited with having saved hundreds of thousands of Chinese lives during a savage Japanese occupation. On the other side of the cup, Rabe was simultaneously the leader of the Nazi party in the same city. In 1938 he assured audiences that he supported the German political system "100 percent."

In its essence, this sort of anti-Manichaean perspective posits that not only one alternative always obtains. If you believe people are only always good, or always only evil, if you think the cup is only ever hot, or only cold, well then you're just wrong - you haven't felt the cup, and you have a terribly naïve understanding of nature. But as long as your views are not that extreme, as long as you recognize the possibility of both cold and hot, then in many cases you needn't choose - it turns out they're both there.

From the little I understand, physicists question the classical distinction between wave and matter, and biologists refuse to choose between nature and nurture. But let me stay close to what I know best. In the social sciences, there is ongoing, and often quite heated, debate about whether or not people are rational, and about whether they're selfish. And there are compelling studies in support of either camp, the hot and the iced. People can be cold, precise, selfish and calculating. Or they can be hot-headed, confused, altruistic, emotional and biased. In fact, they can be a little of both; they can exhibit these conflicting traits at the very same time. People can be perfectly calibrated weather forecasters but hopelessly overconfident investors; ruthless rulers and cuddly pet owners; compassionate friends and apathetic parents. Research on decisions made in demanding contexts has found that people can be thoughtful and calculating as they focus on issues of immediate concern, but negligent and misguided when it comes to issues—sometimes very closely related and equally or more important—just at the periphery of their attention.

As we all know, history is filled with very smart people who did really stupid things, and with good people who acted horribly. Are we altruistic or selfish? Smart or stupid? Good or evil? Like that hot and iced tea, there is always a little of both—it just depends on which side you drink from.

It seems almost redundant to call for the retirement of the free willing self as the idea is neither scientific nor is this the first time that the concept has been dismissed for lacking empirical support. The self did not have to be discovered as it is the default assumption that most of us experience, so it was not really revealed by methods of scientific enquiry. Challenging the notion of a self is also not new. Freud's unconscious ego has been dismissed for lacking empirical support since the cognitive revolution of the 1950s.

Yet, the self, like a conceptual zombie, refuses to die. It crops up again and again in recent theories of decision-making as an entity with free will that can be depleted. It re-appears as an interpreter in cognitive neuroscience as capable on integrating parallel streams of information arising from separable neural substrates. Even if these appearances of the self are understood to be convenient ways of discussing the emergent output of multiple parallel processes, students of the mind continue to implicitly endorse that there is a decision-maker, an experiencer, a point of origin.

We know that the self is constructed because it can be so easily deconstructed through damage, disease and drugs. It must be an emergent property of a parallel system processing input, output and internal representations. It is an illusion because it feels so real, but that experience is not what it seems. The same is true for free will. Although we can experience the mental anguish of making a decision, our free will cannot be some kind of King Solomon in our mind weighing up the pros and cons as this would present the problem of logical infinite regress (who is inside their head and so on?). The choices and decisions we make are based on situations that impose on us. We do not have the free will to choose the experiences that have shaped our decisions.

Should we really care about the self? After all, trying to live without the self is challenging and not how we think. By experiencing, evoking and talking about the self, we are conveniently addressing a phenomenology that we can all relate to. Defaulting to the self in explanations of human behavior enables us to draw an abrupt stop in the chain of causality when trying to understand thoughts and actions. How notable that we do this all so easily when talking about humans but as soon as we apply the same approach to animals, one gets accused of anthropomorphism!

By abandoning the free willing self, we are forced to re-examine the factors that are really behind our thoughts and behavior and the way they interact, balance, over-ride and cancel out. Only then we will begin to make progress in understanding how we really operate.

For most of the past century, mainstream social scientists have assumed that attractiveness is superficial, arbitrary, and infinitely variable across cultures. Many still cling to these views. Their appeal has many motivations. First, beauty is undemocratically distributed, a violation of the belief that we are all created equal. Second, if physical desirability is superficial ("you can't judge a book by its cover"), its importance can be denigrated and dismissed, taking a back seat to deeper and more meaningful qualities. Third, if standards of beauty are arbitrary and infinitely variable, they can be easily changed.

Two movements in the 20^th century seemed to lend scientific support for these views. The first was behaviorism. If the content of human character was built through experienced contingencies of reinforcement during development, those contingencies must have created standards of attractiveness. The second was seemingly astonishing ethnographic discoveries of cross-cultural variability in attractiveness. If the Maori in New Zealand found particular types of lip tattoos attractive and the Yanomamo of the Amazon rain forest prized nose or cheek piercings, then surely all other beauty standards must be similarly arbitrary.

The resurgence of sexual selection theory in evolutionary biology, and specifically the importance of preferential mate choice, created powerful reasons to question the theoretical position long held by social scientists. We now know that in species with preferential mate choice, from scorpionflies to peacocks to elephant seals, physical appearance typically matters greatly. It conveys critical reproductively valuable qualities such as health, fertility, dominance, and 'good genes.' Are humans a bizarre exception to all other sexually reproducing species?

Evolutionary theorizing, long antedating the hundreds of empirical studies on the topic, suggested that we were not. In mate selection, Job One, as someone in business might say, is the successful selection of a fertile partner. Those who failed to find fertile mates left no descendants. Everyone alive today is the product of a long and literally unbroken line of ancestors who succeeded. If any had failed at the critical task, we would not be here today. As evolutionary success stories, each modern human has inherited the mate preferences of their successful ancestors.

Cues recurrently observable to our ancestors that were reliably, statistically, probabilistically correlated with fertility, according to this theory, should become part of our evolved standards of beauty. In both genders, these include cues to health—symmetrical features and absence of sores and lesions, for example. Since fertility is sharply age-graded in women, more so than in men, cues to youth should figure prominently in gender-specific standards of attractiveness. Clear skin, full lips, an unclouded sclera, feminine estrogen-dependent features, a low waist-to-hip ratio, and many other cues to female fertility are now known to be pieces of the puzzle of universal standards of female beauty.

Women's evolved standards of male attractiveness are more complex. Masculine features, hypothesized to signal healthy immune functioning in men, are viewed as attractive more by women seeking short-term than long-term mates, more when women are ovulating than when in the luteal phase of their menstrual cycle, and more by women higher in mate value, perhaps because of their ability to attract and control such men. Women's judgments of men's attractiveness are more dependent on multiple contexts—cues to social status, the attention structure, positive interactions with babies, being seen with attractive women, and many others. The greater complexity and variability of what women find attractive in men is reflected in another key empirical finding—there is far less consensus among women about which men are attractive than among men about which women are attractive.

The theory that 'beauty is in the eyes of the beholder' in the sense of being superficial, arbitrary, and infinitely culturally variable can safely be discarded. I regard it as one of the 'great myths' perpetrated by social scientists in the 20^th century. Its scientific replacement—that beauty is 'in the adaptations of the beholder' as anthropologist Donald Symons phrases it—continues to be disturbing to some. It violates some of our most cherished beliefs and values. But then so did the notion that the earth was not flat or the center of the universe.

In physics, we use the convention that the suffix "-on" denotes a quantized unit of something. For example, in classical physics, there are electromagnetic waves. But in the quantum version of the theory, originating with Einstein's 1905 Nobel Prize winning work, we know that under certain circumstances, it is more precise to regard electromagnetic radiant energy as being distributed in particles called photons. This "wave-particle duality" is the underpinning of modern physics: not just photons, but a zoo of what were once called elementary particles that include protons, neutrons, pions, mesons, and of course the Higgs boson. (Neutrino? It's a long story …).

And what about you? You are a person. Are you a quantum of something too? Well, clearly there are no fractional humans, and we are trivially quantized. But elementary particles, or units, are useful conceptually because they can be considered in isolation, devoid of interactions, like point particles in an ideal gas. You would certainly not fulfil that description, networked, online, and cultured as you undoubtedly are. Your strong interactions with other humans mean that your individuality is complicated by the fact that you are part of a society, and can only function properly in such a milieu. We could go further and say that you are a quantum of a spatially-distributed field, but one that describes the density around each point in space of humans, rather than the electromagnetic field intensity. This description turns out to be technically very powerful for describing the behavior of ecosystems in space and time, particularly to describe extinction, where discontinuous change is important. It seems apt to invoke here the strangely oxymoronic term "Indivi-duality", a counterpart to wave-particle duality.

The notion of individual has several other connotations. It can mean discrete or single, but its etymology is also reminiscent of "indivisible". Clearly we are not indivisible, but are constituted from cells, themselves constituted of cytoplasm, nucleic acids, proteins etc., themselves constituted of atoms, which contain neutrons, protons, electrons, all the way down to the elementary particles which themselves are now believed to be products of string theory, itself known now not to be a final description of matter. In other words, it's "turtles all the way down", and there are no indivisible units of matter, no meaning to the notion of elementary particle, no place to stop. Everything is made of something, and so on ad infinitum.

However, this does not mean that everything is simply the sum of its parts. Take the proton for example, made up of three quarks. It has a type of intrinsic angular momentum called spin, which was initially expected to be the sum of that of its constituent quarks. Yet experiments carried out over the last 20-30 years have shown clearly that this is not the case: the spin arises out of some shared collective aspect of the quarks and short-lived fluctuating particles called gluons. The notion of individual quarks is not useful when the collective behavior is so strong. The proton is made of something, but its properties are not found by adding up the properties of its parts. When we try to identify the something, we discover that, as with Los Angeles, there is no "there" there.

You probably already knew that naïve reductionism is often too simplistic. However, there is another point. It's not just that you are composite, something you already knew, but you are in some senses not even human. You have perhaps a hundred trillion bacterial cells in your body, numbering ten times more than your human cells, and containing a hundred times as many genes as your human cells. These bacteria are not just passive occupants of the zoo that is you. They self-organize into communities within your mouth, guts and elsewhere; and these communities—microbiomes—are maintained by varied, dynamic patterns of competition and cooperation between the different bacteria, which allow us to live.

In the last few years, genomics has given us a tool to explore the microbiome by identifying microbes by their DNA sequences. The story that is emerging from these studies is not yet complete but already has led to fascinating insights. Thanks to its microbes, a baby can better digest its mother's milk. And your ability to digest carbohydrates relies to a significant extent on enzymes that can only be made from genes not present in you, but in your microbiome. Your microbiome can be disrupted, for example due to treatment by antibiotics, and in extreme cases can be invaded by dangerous monocultures, such as Clostridium difficile, leading to your death. Perhaps the most remarkable finding is the gut-brain axis: your gastrointestinal microbiome can generate small molecules that may be able to pass through the blood-brain barrier and affect the state of your brain: although the precise mechanism is not yet clear, there is growing evidence that your microbiome may be a significant factor in mental states such as depression and autism spectrum conditions. In short, you may be a collective property arising from the close interactions of your constitutents.

Now, maybe it is true then that you are not an individual in one sense of the word, but how about your microbes? Well, it turns out that your microbes are a strongly interacting system too: they form dense colonies within you, and exchange not only chemicals for metabolism, but communicate by emitting molecules. They can even transfer genes between themselves, and in some cases do that in response to signals emitted by a hopeful recipient: a bacterial cry for help! A single microbe in isolation does not do these things; thus these complex behaviors are a property of the collective, and not the individual microbes. Even microbes that would seem to be from the same nominal species can have genomes which differ in content by as much as 60% of their genes! So much for the intuitive notion of species! That’s another too-anthropomorphic scientific idea that does not apply to most of life.

Up to now I talked about connections in space. But there are also connections in time. If the stuff that makes the universe is strongly connected in space, and not usefully thought of as the aggregate sum of its parts, then attributing a cause of an event to a specific component may also not be meaningful. Just as you can't attribute the spin of a proton to any one of its constituents, you can't attribute an event in time to a single earlier cause. Complex systems have neither a useful notion of individuality nor a proper notion of causality.

Physics has always been regarded as playing out on an underlying stage of space and time. Special relativity joined these into spacetime, and general relativity taught us that this spacetime itself bends and ripples—but it has remained part of the foundations of physics. However the need to give a quantum-mechanical description of reality challenges the very notion that space and time are fundamental.

We specifically face the problem of reconciling the principles of quantum mechanics with the physics of gravity. At first, physicists believed this meant that spacetime could violently fluctuate to the point of losing meaning—though only at extremely short distances. But attempts to reconcile quantum principles with gravitational phenomena indicate a more profound challenge to the foundational role of spacetime. This comes to the fore when studying both black holes and evolution of the Universe. Spacetime structure is seemingly problematic also at very long distances. 

Quantum mechanics appears to be an inevitable aspect of physics and is remarkably resistant to modification. If quantum principles govern nature, it seems likely that spacetime arises from more fundamentally quantum structures. This is the theme that spacetime is emergent, perhaps roughly similar to the emergence of fluid behavior from the interactions of atoms. 

The problem with fundamental spacetime is even more strongly hinted at from multiple developing perspectives. Notable among these hints is the physics of black holes, where it appears that evolution that respects quantum principles must violate the classical spacetime dictum that information does not propagate faster than the speed of light. Something is apparently very wrong with the standard spacetime picture. Additional evidence mounts when one considers the large-scale structure of the Universe, given quantum principles and the presence of dark energy. Here ultimately spacetime undergoes strong quantum fluctuations at very long scales, and seems to lose meaning. More hints have come from candidate mathematical approaches to fluctuating spacetime.

The apparent need to retire classical spacetime as a fundamental concept is profound, and confronts the reality that a clear successor is not yet in sight. Different approaches to the underlying quantum framework exist; some show promise but none yet clearly resolve our decades-old conundrums in black holes and cosmology. The emergence of such a successor is likely to be a key element in the next major revolution in physics.

Markets can have terrible consequences. Take just one example. In an ingenious experiment, researchers showed that people who enter a market where the lives of animals were priced as commodities were more likely to devalue the lives of those animals—treating those lives as nothing more than opportunities for profit.

Markets can have uplifting consequences. Take just one example. In a series of investigations, researchers show that efficient markets have contributed to the development of countless life-saving drugs (albeit sometimes with a little governmental help), bettering the lives of billions.

Yet in popular and scientific discourse, it is uncommon to see markets described as anything except truly evil and fundamentally flawed (left-leaning pundits and scholars), or truly perfect and self-correcting (right-leaning pundits and scholars).

It is time to retire both theories: that markets are good, and that markets are bad.

Taking a step back and remembering what markets are—an aggregation of many individuals—makes it obvious that markets are very unlikely to be good or bad. Replace the word "markets" with another shorthand term for an aggregation of individuals: "groups." We certainly don't view groups as good or bad. Groups are capable of amazing selflessness, generosity, and heroism; they are also capable of selfishness, greed, and cruelty. They are capable of amazing performance (think of Bell Labs); they are also capable of terrible performance (think of the many dysfunctional groups of which you have been a member).

When we think of groups, we think of the conditions under which groups are likely to behave well or behave poorly. We don't often think of them as self-correcting, as always performing well over time, or most importantly, as either inherently good or inherently bad.

Applying the same logic to markets—think of them in this context as "groups writ large"—will assist with the development of a richer and more accurate theory of when and why markets are likely to have terrible or uplifting consequences.

Scientists sometimes resist new ideas and hang on to old ones longer than they should, but the real problem is the failure of the public to understand that the possibility of correction or disproof is a strength and not a weakness. We live in an era when it is increasingly important that the voting public be able to evaluate scientific claims and be able to make analogies between different kinds of phenomena, but this can be a major source of error. The process by which scientific knowledge is refined is largely invisible to the public. The truth-value of scientific knowledge is dependent upon its openness to correction, yet we all carry around ideas that science has long since revised—and are disconcerted when asked to abandon them. Surprise: you will not necessarily drown if you go swimming after lunch.

A blatant example is the role of competition in evolution, which is treated by many as a scientifically established law of nature, and often taken for granted by economists and psychologists...at the same time that others argue that evolution, being a "theory" is no more than a "guess." Biology has been steadily giving increasing recognition to the importance of symbiosis in evolution, alongside competition, as well as diversification that by-passes competition, but "the survival of the fittest," a metaphor drawn by Darwin from the description of early industrial society by Herbert Spencer, survives as a binding metaphor for human behavior.

Most people are not comfortable with the notion that knowledge can be authoritative, can call for decision and action, and yet be subject to constant revision, because they tend to think of knowledge as additive, not recognizing the necessity of reconfiguring in response to new information. It is precisely this characteristic of scientific knowledge that encourages the denial of climate change and makes it so difficult to respond to what we do know in a context where much is still unknown. 

What kind of evidence will convince the doubters of the reality of what might best be called climate disruption? Perhaps the exploration of scientific ideas in need of retirement should be an annual event, with a clear emphasis on the fact that each new synthesis of complex data is potentially more inclusive. Retiring concepts that no longer fit is not primarily a matter of eliminating error but of integrating new information and newly recognized connections into our understanding.     
 

We fancy ourselves intelligent, conscious and alert, and thinking our way through life. This is an illusion. We are deluded by our brain's generation of a sketchy, rational narrative of subconscious, sometimes irrational or fictitious events that we accept as reality. These narratives are so compelling that they become common sense and we use them to guide our lives. In cases of brain damage, neurologists use the term confabulation to describe a patient's game but flawed attempt to produce an accurate narrative of life events. I suggest we be equally wary of everyday, non-pathological confabulation and retire the common sense hypothesis that we are rational beings in full conscious control of our lives. Indeed, we may be passengers in our body, just going along for the ride, and privy only to second-hand knowledge of our status, course and destination.

Behavioral and brain science detects chinks in our synthetic, neurologically generated edifice of reality. Research on sensory illusions indicates that percepts are simply our best estimate of the nature of physical stimuli, not a precise rendering of things and events. The image of our own body is an oddly shaped product of brain function. Memory of things past is also fraught with uncertainty; it is not the reading-out of information from the brain's neurological data bank, but an ongoing construct subject to error and bias. The brain also makes decisions and initiates action before the observer is consciously aware of detecting and responding to stimuli. My own research found that people confabulate narratives to rationalize their laughter, such as "It was funny," or "I was embarrassed," neglecting laughter's involuntary nature and frequent contagiousness.

Our lives are guided by a series of these guesstimates about the behavior and mental state of ourselves and others that, although imperfect, are adaptive and sufficiently accurate to enable us to muddle along. However, as scientists, we demand more than default explanations based on common sense. Behavioral and brain science provides a path to understanding that challenges the myths of mental life and everyday behavior. One of its delights is that it often turns reality on its head, revealing hidden processes and providing revelations about who we are, what we are doing, and where we are going.

It is a statistical fact that you are more likely to die while horseback riding (1 serious adverse event every ~350 exposures) than from taking Ecstasy (1 serious adverse event every ~10,000 exposures). Yet, in 2009, the scientist who said this was fired from his position as the chairman of the UK's Advisory Council on the Misuse of Drugs. Professor David Nutt's remit was to make scientific recommendations to government ministers on the classification of illegal drugs based on the harm they can cause. He was dismissed because his statement highlighted how the UK Government's policies on narcotics are at odds with scientific evidence. Today, the medical use of drugs such as cannabis remains technically illegal.

Such incidents of silencing are sadly commonplace when it comes to politically controversial scientific topics. The US Government muzzled climate scientists in a similar manner in 2007, when it was reported that 46% of 1600 surveyed scientists were warned against using terms like "global warming" and 43% said their published work had been revised in ways that altered their conclusions. US preparations for oncoming climate change were checked as a result, a failing that persists today. Going back further, the story of Nikolai Vavilov is chilling. Vavilov was a plant geneticist in the Soviet Union under Joseph Stalin. He was jailed in 1940 for criticizing the pseudo-scientific views of Trofim Lysenko, a protégé of Stalin. Vavilov died of starvation in prison a few years later; scientific dissent from Lysenko's "theories" of Lamarkian inheritance was outlawed in 1948. Soviet agriculture languished for decades because of Lysenkoism; meanwhile famine decimated the population.

The scientific method is defined by the Oxford English Dictionary as "a method or procedure...consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses". It is our finest instrument for unearthing the truth. Applied correctly it is blind to and corrects for our inherent biases. Scientists are trained to wield this formidable tool in their quest to understand the universe around us. The truths they uncover can be at odds with our current beliefs; but when the facts (based on evidence and arrived at through rigorous testing) change, minds also need to change.

I use the examples above of sidelined scientists to illustrate the consequences of excluding science from the policy making process. But sometimes the sidelining is self-imposed: scientists can be genuinely reluctant to get involved in such activity and instead prefer to focus on gathering data and publishing results.

There is a tacit understanding, a custom in the culture of science, that scientists practice the scientific method in the confines of the Ivory Tower. Scientists are seen as impartial, aloof, individuals with a single-minded focus on their work and out of touch with the realities of the world around them. Scientists are expected to only do science, to find the truth and then leave it up to everyone else to decide what to do with it.

This is untenable. Scientists have a moral obligation to engage with the public about their findings; to advise and speak out on policy, and to critique its consequent implementation. Science impacts on the life of every single species on our planet. It is ludicrous that the very people who discover the facts are not part of any subsequent policy-making dialogue. Science needs to be an essential component of the public discourse; currently it is not. The consequences of that disconnect can be dire, as evinced by the criminalization of drugs that can provide relief to sufferers of chronic pain, troubling delays in programs of vital national importance, and the famine that slaughtered millions of Soviet citizens under Stalin's regime.

Scientists should not simply stick to doing science. Perhaps we need to extend the scientific method to include a requirement for communication. Young scientists should be taught the value and necessity of communicating their findings to the general public. Scientists should not shy away from controversy, because some topics should not be controversial to begin with. The scientific evidence for the efficacy of vaccines, the process of evolution, the existence of anthropogenic climate change is accepted in the scientific community. Yet, within the public sphere, goaded by a sensationalizing mainstream media and politicians seeking re-election, these settled facts are made to appear tentative. Science is based on evidence, and if that evidence tells us something new we need to incorporate that into our policies. We cannot ignore it simply because it is unpopular or inconvenient.

By passionately advocating for evidence-based policy scientists will expand scientific research, reversing the trend of recent years; and by thus visibly working for the common weal scientists will earn the public's trust, protecting long-term investigations from short-sighted cuts. Scientific advancement is utterly dependent on public funding and public backing. The Space Race, the Human Genome Project, the search for the Higg's Boson and the Mars Curiosity Rover Mission were all enthusiastically embraced by the public. The progress of science demands that scientists engage the public. But for that to happen the notion that a scientist should stay hidden away in a laboratory needs to be retired.
 

As a young man, Gottfried Wilhelm Leibniz had a beautiful dream: to discover the calculus that could map every single idea in the world into symbols. Such a universal calculus would put an end to all scholarly bickering—every passionate Edge discussion, for one, could be swiftly resolved by dispassionate calculation. Leibniz optimistically estimated that a few skilled persons should be able to work the whole thing out in five years. Yet nobody, Leibniz included, has yet found that holy grail.

"I was much struck how entirely vague and arbitrary is the distinction between species and varieties." Darwin (1859)

For centuries, there was one way to bring order to the vastness of biological diversity—Linnaean classification. In the 18^th century, Carl Linnaeus devised a method for dividing species based upon their description—do they look the same, do they behave the same? With Linnaean classification, you could divide the natural world into discrete kinds—you could count them, saying confidently "there are two species of elephants" or "there are four kinds of bears." Some psychologists seek to bring the same order to the mind, claiming that "there are six emotions," "there are five types of personality," or "there are three moral concerns." These psychologists are inspired by the precision, order and neatness of Linnaeus' ideas—the only problem is that Linnaeus was wrong.

Linnaeus lived about a hundred years before Darwin introduced the theory of evolution, and long believed that species were fixed and unchangeable. His religious roots led him to see species as a product of divine providence and his job was simply to catalog these distinct kinds, once writing "God created, Linnaeus ordered." If God created a certain number of distinct species, cataloging and counting them made sense. It was meaningful to ask "how many salamanders did God create?"

Evolution, however, destroyed the sanctity of species. Species were not created whole from The Beginning, but instead emerged over time through the repetition of a simple algorithm: heredity, mutation and selection. Evolution showed that a dizzying diversity of life—from viruses, to cacti, to humans—were explained through a basic set of common processes expressed in different environments. This common process means that lines between species are more in the mind of humans than in nature, with many intermediate animals (e.g., lungfish) and hybrids (e.g., ligers) that defy easy categorization. Moreover, in geological time, these divisions are even more arbitrary, with species diverging and converging as continents separate and collide.

Biology has all but realized that species are not reflections of eternal Divine Order, but simply a useful way to intuitively organize the world. Unfortunately, psychology lags behind. Many psychologists believe that the mental world is fixed and countable, that the appearance of mental states reflects a deeper essence. Introductory psychology textbooks contain numbered lists of psychological species—5 kinds of human needs, 6 basic emotions, 3 moral concerns, 3 kinds of love, 3 parts of the mind—with these lists depending primarily upon the intuitions of those who are doing the counting.

Like Linnaeus in the 18^th century, these intuitive taxonomies were once the best we could do because psychology lacked an understanding of basic psychological process. However, social cognition and neuroscience has revealed these processes, and found that diverse mental experiences—from emotion to morality to motivation—are combinations of more basic affective and cognitive processes. This research suggests that psychological states are not firmly demarcated "things" with enduring essences, but are instead fuzzy constructs that emerge from common psychological processes expressed across different environments.

Just as evolution can create infinite species by expressing a common process in specific environments, so too can the mind create infinite mental species. One can no sooner count emotions or moral concerns than snowflakes or colors. To be sure, there are descriptive similarities and differences across instances, but any groupings are arbitrary and rest heavily on the intuition of researchers. This is why scientists can never agree on the fundamental number of anything; one scientist may divide a mental experience into 3, another 4, and another 5.

It is time for psychology to abandon the enterprise of numbering nature, and recognize that psychological species are neither distinct nor real. Biology has long recognized the arbitrary and constructed natured of species; why are we more than 200 years behind? The likely answer is that people—even including psychologists and philosophers—believe that intuitions, as products of the mind, are accurate reflections of its structure. Unfortunately, decades of research demonstrates the flaws of intuitive realism, revealing that intuitions about the mind are poor guides to underlying psychological processes.

Psychologists must move from counting to combining. Counting is simply describing the world; one psychologist's intuitive ordering of mental states in one culture, at one time. Combining seeks to find basic psychological elements and discover how they interact to create the mental world. In biology, counting asks "how many salamanders are there?" whereas combining asks "what processes lead to salamander diversity." Counting is bound to a specific environment and time, whereas combining recognizes these factors as processes themselves. Psychology must follow biology and move from numbering individual species to exploring underlying systems.

This process has already begun. Thomas Insel, the head of NIMH has prioritized systems over species in psychopathology research. He rejects the utility of the DSM, suggesting that intuitive taxonomies obscure underling process of psychopathology, and impedes the discovery of treatments. NIMH funds proposals that examine the underlying affective, conceptual and neurological systems, which may explain why the "distinct" disorders of depression and anxiety are so often comorbid, and why serotonin reuptake inhibitors (SSRIs) appear to help diverse disorders. Psychopathology does not easily fit into categories, and neither do other psychological phenomena.

Of course, we shouldn't throw out the baby with the bathwater. It is still necessary to catalog the natural world to allow meaningful discussion. Even in biology, where the power of the process of evolution is undisputed, most acknowledge the utility of Linnaeus' system and continue to use the names he provided years ago. But he key is not to confuse human constructions with natural order; what is useful to humans is not necessarily true of nature. Intuitive taxonomies are a necessary first step in psychological science but even Linnaeus—as he learned more about the world—recognized the arbitrariness of his system and the species he labeled. It is time for psychology to recognize this fact as well, and leave behind Linnaeus and the 18^th century.

Consciousness is a hot topic in neuroscience and some of the brightest researchers are hunting for the neural correlates of consciousness (NCCs)—but they will never find them. The implicit theory of consciousness underlying this quest is misguided and needs to be retired.

The idea of the NCCs is simple enough and intuitively tempting. If we believe in the 'hard problem of consciousness'—the mystery of how subjective experience arises from (or is created by or generated by) objective events in a brain—then it's easy to imagine that there must be a special place in the brain where this happens. Or if there is no special place then some kind of 'consciousness neuron', or process or pattern or series of connections. We may not have the first clue how any of these objective things could produce subjective experience but if we could identify which of them was responsible (so the thinking goes), then we would be one step closer to solving the mystery.

This sounds eminently sensible as it means taking the well-worn scientific route of starting with correlations before moving on to causal explanations. The trouble is it depends on a dualist—and ultimately unworkable—theory of consciousness. The underlying intuition is that consciousness is an added extra—something additional to and different from the physical processes on which it depends. Searching for the NCCs relies on this difference. On one side of the correlation you measure neural processes using EEG, fMRI or other kinds of brain scan; on the other you measure subjective experiences or 'consciousness itself'. But how?

A popular method is to use binocular rivalry or ambiguous figures which can be seen in either of two incompatible ways, such as a Necker cube that flips between two orientations. To find the NCCs you find out which version is being consciously perceived as the perception flips from one to the other and then correlate that with what is happening in the visual system. The problem is that the person has to tell you in words 'Now I am conscious of this', or 'Now I'm now conscious of that'. They might instead press a lever or button, and other animals can do this too, but in every case you are measuring physical responses.

Is this capturing something called consciousness? Will it help us solve the mystery? No.

This method is really no different from any other correlational studies of brain function, such as correlating activity in the fusiform face area with seeing faces, or prefrontal cortex with certain kinds of decision-making. It correlates one type of physical measure with another. This is not useless research. It is very interesting to know, for example, where in the visual system neural activity changes when the reported visual experience flips. But discovering this does not tell us that this neural activity is the generator of something special called 'consciousness' or 'subjective experience' while everything else going on in the brain is 'unconscious'.

I can understand the temptation to think it is. Dualist thinking comes so naturally to us. We feel as though our conscious experiences are of a different order from the physical world. But this is the same intuition that leads to the hard problem seeming hard. It is the same intuition that produces the philosopher's zombie—a creature that is identical to me in every way except that it has no consciousness. It is the same intuition that leads people to write, apparently unproblematically, about brain processes being either conscious or unconscious.

Am I really denying this difference? Yes. Intuitively plausible as it is, this is a magic difference. Consciousness is not some weird and wonderful product of some brain processes but not others. Rather, it is an illusion constructed by a clever brain and body in a complex social world. We can speak, think, refer to ourselves as agents and so build up the false idea of a persisting self that has consciousness and free will.

We are tricked by an odd feature of consciousness. When I ask myself 'what am I conscious of now?' I can always find an answer. It's the trees outside the window, the sound of the wind, the problem I am worried about and cannot solve—or whatever seems most vivid at the time. This is what I mean by being conscious now, by having qualia. But what was happening a moment before I asked? When I look back I can use memories to claim that I was conscious of this or that and not conscious of something else, relying on the clarity, logic, consistency and other such features to decide.

This leads all too easily to the idea that while someone is awake they must always be conscious of something or other. And that leads along the slippery path to the idea that if we knew what to look for we could peer inside someone's brain and find out which processes were the conscious ones and which the unconscious ones. But this is all nonsense. All we will ever find is the neural correlates of thoughts, perceptions, memories and the verbal and attentional processes that lead us to think we are conscious.

When we finally have a better theory of consciousness to replace these popular delusions we will see that there is no hard problem, no magic difference and no NCCs. 

There! I said it! The venerable notion of Unification needs to go. I don't mean the smaller unifications that we scientists search for all the time, connecting as few principles with as many natural phenomena as possible. This sort of scientific economy is a major foundational stone for what we do: we search and we simplify. Over the centuries, scientists have done wonders following this motto. Newton's law of universal gravity, the laws of thermodynamics, electromagnetism, universal behavior in phase transitions…

The trouble starts when we take this idea too far and search for the über-unification, the theory of everything, the arch-reductionist notion that all forces of Nature are merely manifestations of a single force. This is the idea that needs to go. And I say this with a heavy heart, given that my early career aspirations and formative years were very much fueled by the impulse to unify it all.

The idea of unification is quite old, as old as Western philosophy. Thales, the first pre-Socratic philosopher, already posited that "all is water," thus dreaming up a single material principle to describe all of Nature. Plato proposed elusive geometrical forms as the archetypal structures behind all there is. Math became equated with beauty and beauty with truth. From there, the highest of post-Plato aspirations was to erect a purely mathematical explanation for all there is, the all-encompassing cosmic blueprint, the masterwork of a supreme intelligence. Needless to say, the whole thing was always about our intelligence, even if often blamed on some foggy "Mind of God" metaphor.

We explain the world the way we think about it. There is no way out of our minds.

The impulse to unify it all runs deep in the souls of mathematicians and theoretical physicists, from the Langlands program to superstring theory. But here is the rub: pure mathematics is not physics. The power of mathematics comes precisely from its detachment from physical reality. A mathematician can create any universe she wants, and play all sorts of games with it. A physicist can't, for his job is to describe Nature as we perceive it. Nevertheless, the unification game has been an integral part of physics since Galileo, and has produced what it should: approximate unifications. Yes, even the most sacred of our unifications are only approximations. Take, for example, electromagnetism. The equations describing electricity and magnetism are only perfectly symmetric in the absence of any sources of charge or magnetism, that is, in empty space. Or take the famous (and beautiful) Standard Model of particle physics, based on the "unification" of electromagnetism and the weak nuclear force. Here again, we don’t have a real unification since the theory retains two forces all along. (In more technical jargon, there are two coupling constants and two gauge groups.) A real unification, such as the conjectured Grand Unification between the strong, the weak, and the electromagnetic forces, proposed 40 years ago, remains unfulfilled.

So, what's going on? Why do so many insist in finding the One in Nature while Nature keeps telling us that it's really about the many?

For one thing, the scientific impulse to unify is cryptoreligious. The West has bathed in monotheism for thousands of years, and even in polytheistic cultures there is always an alpha-God in charge (Zeus, Ra, Para-Brahman…) For another, there is something deeply appealing in equating all of Nature to a single creative principle: to decipher the "mind of God" is to be special, is to answer to a higher calling. Pure mathematicians who believe in the reality of mathematical truths are monks of a secret order, open only to the initiated. In the case of high-energy physics, all unification theories rely on sophisticated mathematics related to pure geometric structures: the belief is that Nature's ultimate code exists in the ethereal world of mathematical truths and that we can decipher it.

Recent experimental data has been devastating to such belief. No trace of supersymmetric particles, of extra dimensions, or of dark matter of any sort, all long-awaited signatures of unification physics. Maybe something will come up: to find we must search. The trouble with unification in high-energy physics is that you can always push it beyond the experimental range. "The Large Hadron Collider got to 7 TeV and found nothing? No problem! Who said Nature should opt for the simplest versions of unification? Maybe it’s all happening at much higher energies, well beyond its reach."

There is nothing wrong with this kind of position. You can believe it until you die and die happy. Or you can conclude that what we do best is to construct approximate models of how Nature works and that the symmetries we find are only descriptions of what really goes on.

Perfection is too hard a burden to impose on Nature.

People often see this kind of argument as defeatist, or as coming from someone who got frustrated and gave up. (As in "he lost his faith.") Big mistake. To search for simplicity is essential to what scientists do. It's what I do. There are essential organizing principles in Nature, and the laws we find are excellent ways to describe them. But the laws are many, not one. We are successful pattern-seeking rational mammals. That, alone, is cause for celebration. However, let us not confuse our descriptions and models with reality. We may hold perfection in our mind's eye as a sort of ethereal muse. Meanwhile, Nature is out there, doing its thing. That we manage to catch a glimpse of its inner workings is nothing short of wonderful. And that should be good enough.

The term "quantum jump has entered everyday language as a metaphor for a large, discontinuous change. It has also become widespread in the vast but sadly repetitive landscape of pseudo-science and mysticism.

The term comes from physics, and is indeed used by physicists (though rarely in published papers). It evokes the fact that mutually distinguishable states in quantum physical systems are always discrete. Yet there is no such phenomenon in quantum physics as a "quantum jump": under the laws of quantum theory, change is always continuous in both space and time. OK, maybe some physicists still subscribe to an exception to that, namely the so-called "collapse of the wave function" when an object is observed by a conscious observer. But that nonsense is not the nonsense I am referring to here. I'm referring to misconceptions even about the sub-microscopic world—like: "when an electron in a higher-energy state undergoes a transition to a lower energy level, emitting a photon, it quantum-jumps from one discrete orbit to another without passing through intermediate states".

Even worse: "when an electron in a tunnel diode approaches the barrier that it does not have enough energy to penetrate (so that under classical physics it would bounce off), the quantum phenomenon of tunneling allows it to appear mysteriously on the other side without ever having been in the region where it would have negative kinetic energy".

The truth is that the electron in such situations does not have a single energy, or position, but a range of energies and positions, and the allowed range itself can change with time. If the whole range of energies of a tunneling particle were below that required to surmount the barrier, it would indeed bounce off. And if an electron in an atom really were at a discrete energy level, and nothing intervened to change that, then it would never make a transition to any other energy.

Quantum jumps are an instance of what used to be called "action at a distance": something at one location having an effect, not mediated by anything physical, at another location. Newton called this "so great an Absurdity that I believe no Man who has in philosophical Matters a competent Faculty of thinking can ever fall into it". And the error has analogues in fields quite distant from classical and quantum physics. For example in political philosophy the "quantum jump" is called revolution, and the absurd error is that progress can be made by violently sweeping away existing political institutions and starting from scratch. In the philosophy of science it is Thomas Kuhn's idea that science proceeds via revolutions—i.e. victories of one faction over another, both of which are unable to alter their respective "paradigms" rationally. In biology the "quantum jump" is called saltation: the appearance of a new adaptation from one generation to the next, and the absurd error is called saltationism.

Newton was wrong that there is a maximum size of error that competent people can fall into, but right that this particular one is severe. All those versions of it are mistaken for a single reason: they all require information of the requisite kind to appear from nowhere. In reality, the space on the far side of the barrier cannot "know" that an electron, and not a proton or a bison, must appear there, until some physical change, originating at the electron, reaches it. The same holds when it is not a spatial gap but a directly informational one: Political institutions, and biological adaptations, instantiate information—knowledge—about how a complex system can better meet the challenges facing it, and knowledge can be created only by processes of piecemeal variation and selection. And Kuhn's vision cannot explain how science has in fact been delivering knowledge about physical reality at an ever-accelerating rate.

Quantum jumps in all these fields represent a retreat from explanation, and therefore in effect an appeal to the supernatural. They all have the logic of the Sidney Harris cartoon "Then a Miracle Occurs" (depicting a mathematician with a gap in his proof). As Richard Dawkins puts it, "saltationism is creationism". And in all cases the reality that fills the gap, the idea that truly explains the phenomenon, is much more interesting and delightful than any faith in its mystery could be.

Many believe this seeming axiom, that beauty leads to descriptive power. Our experience seems to show this, mostly from the successes of physics. There is some truth to it, but also some illusion.

There is a ready explanation of how a distant primate came into the beginnings of a mathematical appreciation of nature. Hunting, that primate found it easier to fling rocks or spears at fleeing prey than chase them down. Some of his fellows found the curve of a flung stone difficult to achieve, but he did not. He found the parabola beautiful and simpler to achieve, because that pleasurable sensation provided evolutionary feedback. Over eons this led to an animal that invented complex geometries, calculus and beyond.

This is a huge leap, of course, an evolutionary overshoot. We seem to be smarter than needed simply to survive in the natural world—earlier hominids did, even spreading over most of the planet. We did go through some population bottlenecks in our past, perhaps as recent as about 130,000 years ago. Perhaps those recent eras of intense selection explain why we have such vastly disproportionate mental abilities.

Still there remain, beyond evolutionary arguments, two mysteries in math: whence its amazing ability to describe nature, and why its intrinsic beauty and elegance?

Parabolas are elegant, true. They describe how hard bodies fly through the air under gravity. But the motion of a falling leaf, on the other hand, demands several differential equations taking into account wind velocity, gravity, geometry of the leaf, fluid flow and much else. A cruising airplane is even harder to describe. Neither case is elegant or simple.

So the utility of math stands separately from its intrinsic beauty. Mathematics is most elegant when we simplify the system considered. So with a baseball we account for the initial acceleration and angle, the air and gravity, and out comes a parabola as a good approximation. Not so the leaf.
And that parabola? We see its simple beauty far too slowly to be of any use in real time. Our appreciation comes afterward. To actually make a parabola work for us in baseball, we learn how to throw. Such learning builds on hard-wired neuronal networks in the brain, selected for over evolutionary times, since knowing how to throw a missile is adaptive. A human pitcher can more subtly affect the trajectory by throwing curves, knuckle balls etc. Those are certainly more complex trajectories and probably less elegant, but still well within the capability of our nervous systems. But for well-learned actions, all that processing goes on at unconscious levels. In fact, too much conscious attention to the details of action can interfere. Athletes know this—it's the art of staying in the zone. Probably that zone is where the mind runs on its sense of rightness, beauty, economy of effort.

Further, elegance is hard to define, as are most aesthetic judgments. Richard Feynman once noted that it is simple to make known laws more elegant, say by starting with Newton's force law, F=ma, then defining R=F – ma. The equation R=0 is visually more elegant, but contains no more information. The Lagrangian method in dynamics is elegant—just write the expression for kinetic energy minus the potential energy—but one must know a fundamental theory to do so; the elegance of the Lagrangian comes later, as a mathematical aid.

More recently, it is hard to devise an elegant cosmological theory that yields directly the small cosmological constant we observe. Some solve this problem by invoking the Anthropic Principle, and thus multiverses of some sort. But this ventures near a violation of another form of the elegance standard, Occam's Razor. Imagining a vast sea of multiverses, with us arising in one where conditions produce intelligent beings, seems to many excessive. It invokes a plentitude we can never see. The scientific test of multiverse cosmology is whether it leads to predictable consequences.

Can multiverses converse with each other? That would be a way of verifying the basis of such theories. Most multiverse models seem to say there is no possible communication between the infinitude of multiverses. Brane theory, though, comes from models where no force law operates between branes, except gravitation. Perhaps someday an instrument like LIGO, the Laser Interferometer Gravitational-Wave Observatory, can detect such waves from branes. But is it elegant to shift confirmation onto some far future technology? Sweeping dust under a rug seems inelegant to me.

Evolution doesn't care about beauty and elegance, just utility. Beauty does play a secondary role, though. The male who best throws the spear to bring down prey is appreciated and may have a choice of many mates. It just so happens that the effective and now beautiful act of spear throwing is describable with fairly simple math.
We make the short step to say the underlying math is also beautiful.

Math's utility implies that for a suitably simple model of the universe there should be a fairly simple mathematical theory of everything, something like general relativity, describable by a one-line equation. Searching for it on that intuitive basis may lead us to such a theory. I suspect a model that captures the full complexity of the universe, though, would take up a lot more than one line.

When we say a math model is elegant and beautiful, we express the limits of our own minds. It is not a deep description of the world. In the end, simple models are much easier to comprehend than complex ones. We cannot expect that the path of elegance will always guarantee we are on the right track.

I know. The universe has been around for 13.8 billion years and is likely to survive for another hundred billion years or more. Plus, where would the universe retire to? Florida isn't big enough. But it is time to retire the twenty-five hundred year old scientific idea of the universe as the single volume of space and time that contains everything. Twenty-first century cosmology strongly suggests that what we see in the cosmos—stars, galaxies, space and time since the big bang—does not encompass all of reality. Cosmos, buy the condo.

What is the universe, anyway? To test your knowledge of the universe, please complete the following sentence. The universe

(a) consists of all things visible and invisible—what is, has been, and will be.

(b) began 13.8 billion years ago in a giant explosion called the big bang, and encompasses all planets, stars, galaxies, space and time.

(c) was licked out of the salty rim of the primordial fiery pit by the tongue of a giant cow.

(d) All of the above.

(Correct answer below.)

The idea of the universe as an observed and measured thing has persisted for thousands of years. Those observations and measurements have been so successful that today we know more about the origin of the universe than we do about the origin of life on earth. But the success of observational cosmology has brought us to a point where it is no longer possible to identify the universe—in the sense of answer (a) above—with the observed cosmos—answer (b). The same observations that establish the detailed history of the universe imply that the observed cosmos is a vanishingly small fraction of an infinite universe. The finite amount of time since the big bang means that our observations only extend a little more than ten billion light years from earth. Beyond the horizon of our observation lies more of the same, space filled with galaxies stretching on forever. No matter how long the universe exists, we will have access to only a finite part, while an infinite amount of universe remains beyond our grasp. All but an infinitesimal fraction of the universe is unknowable.

That's a blow. The scientific concept, universe = observable universe, has thrown in the towel. Perhaps that's OK. What's not to like about a universe that encompasses infinite unknowable space? But the hits keep coming. As cosmologists delve deeper into the past, they find more and more clues that, for better or worse, there is more out there than just the infinite space beyond our horizon. Extrapolating backwards before the big bang, cosmologists have identified an epoch called inflation, in which the universe doubled in size many times over a tiny fraction of a second. The vast majority of spacetime consists of this rapidly expanding stuff. Our own universe, infinite as it is, is just a 'bubble' that has nucleated in this inflationary sea.

It gets worse. The inflationary sea contains an infinity of other bubbles, each an infinite universe in its own right. In different bubbles the laws of physics can take different forms. Somewhere out there in another bubble universe, the electron has a different mass. In another bubble, electrons don't exist. Because it consists not of one cosmos but of many, the multi-bubble universe is often called a multiverse. The promiscuous nature of the multiverse may be unappealing (William James, who coined the word, called the multiverse a 'harlot'), but it is hard to eliminate. As a final insult to unity, the laws of quantum mechanics indicate that the universe is continually splitting into multiple histories or 'worlds,' out of which the world that we experience is only one. The other worlds contain the events that didn’t happen in our world.

After a two millenium run, the universe as observable cosmos is kaput. Beyond what we can see, an infinite array of galaxies exists. Beyond that infinite array, an infinite number of bubble universes bounce and pop in the inflationary sea. Closer by, but utterly inaccessible, the many worlds of quantum mechanics branch and propagate. MIT cosmologist Max Tegmark calls these three kinds of proliferating realities the type I, type II, and type III multiverses. Where will it all end? Somehow, a single, accessible universe seemed more dignified.

There is hope, however. Multiplicity itself represents a kind of unity. We now know that the universe contains more things than we can ever see, hear or touch. Rather than regarding the multiplicity of physical realities as a problem, let's take it as an opportunity.

Suppose that everything that could exist, does exist. The multiverse is not a bug, but a feature. We have to be careful: the set of everything that could exist belongs to the realm of metaphysics rather than of physics. Tegmark and I have shown that with a minor restriction, however, we can pull back from the metaphysical edge. Suppose that the physical universe contains all things that are locally finite, in the sense that any finite piece of the thing can be described by a finite amount of information. The set of locally finite things is mathematically well-defined: it consists of things whose behavior can be simulated on a computer (more specifically, on a quantum computer). Because they are locally finite, the universe that we observe and the various multiverses are all contained within this computational universe. As is, somewhere, a giant cow.

Answer to quiz: (c)

Brain plasticity refers to the fact that neurons are capable of changing their structural and functional properties with experience. That seems hardly surprising since every part of the body changes with age. What is special about brain plasticity (but not unique to this organ) is that the changes are mediated by specific events that are in some sense adaptive. The field of brain plasticity primarily derives from the pioneering studies of Torsten Wiesel and David Hubel who showed that depriving one eye of normal visual input during early development resulted in a loss of functional connections of that eye with the visual cortex, while the connections of the eye not deprived of visual input expanded.

These studies convincingly demonstrated that early brain connections are not hard-wired, but could be modified by early experience hence they were plastic. For this work, and related studies, done in the 1960's Wiesel and Hubel received the Nobel Prize in 1981. Since that time there have been thousands of studies showing a wide diversity of neuronal changes in virtually every region of the brain, ranging from molecular to the systems level, in young, adult and aged subjects. As a result, by the end of the 20th century our view of the brain evolved from the hard wired to the seemingly ever changeable. Today plasticity is one of the most commonly used words in the neuroscience literature. Indeed, I have employed this term many times in my own research articles and used it in the titles of some of my edited books. So what's wrong with that, you may ask?

For one thing, the widespread use of "brain plasticity" to virtually every type of change in neuronal structure and function has rendered this term largely meaningless. When virtually all and any change in neurons is characterized as plasticity, the term encompasses so much that it no longer conveys any useful information. It is also the case, that many studies invoke brain plasticity as the underlying cause of modified behavioral states without having any direct evidence for neuronal changes. Particularly egregious are the studies showing improvements in performance on some particular task with practice. The fact that practice improves performance has been noted before anything was known about the brain. Does it really add anything to invoke that improvements in function demonstrate a remarkable degree of brain plasticity. The word "remarkable" is often used to denote practice effects in seniors as if those old enough to receive social security are incapable of showing enhanced performance with training.

Studies of this type have lead to the launch of a growing brain training industry. Many of these programs are focused on the very young. Particularly popular in past years was the "Mozart effect" which led parents, who had no interest in classical music themselves, to play continuously pieces by Mozart to their infants. This movement seems to have abated, replaced by a plethora of games that are suppose to improve the brains of children of all ages. But the largest growth in the brain plasticity industry has focused on the aging brain. Given the concerns that most of us have about memory loss and decreasing cognitive abilities with age this is understandable. There are large profits to be made as evident by the number of companies that have proliferated in this sector in recent years.

There is of course nothing wrong with having children or seniors engage in activities that challenge their cognitive functions. In fact, there may be some genuine benefits in doing so. Certainly undergoing such training is preferable to watching television for many hours each day. It is also the case that any and all changes in performance reflect some underlying changes in the brain. How could it be otherwise, since the brain controls all behaviors? But as yet, we do not know what occurs in the brain when performance improves on a specific video game, nor do we understand how to make such changes long lasting and generalizable to diverse cognitive states.  Terming such efforts brain training or enhanced brain plasticity is often just hype intended to sell a product. This does not mean that the so-called brain exercises should be abandoned. They are unlikely to cause harm and may even do some good. But please refrain from invoking brain plasticity, remarkable or otherwise, to explain the resulting improvements.

Suppose that a scientist wants to study a striking animal behavior; say, the courtship display of the stickleback fish, or the cooperative agriculture of leafcutter ants. She will, of course, ultimately want to know the underlying mechanisms of these behaviors: How do they work? How did they evolve? What can we learn from them? But no student of animal behavior would dream of asking these questions without first systematically discovering the facts; beginning with extensive field observation in the wild, then moving to experiments and modeling in the lab. Why, then, have linguists emphatically denied any value in directly observing linguistic behavior?

The culprit is a bad idea: that a science of language should be concerned only with competence (the mental capacity for producing sentences), and never with performance (what happens when we actually talk). Here is its decidedly dualist reasoning: When the idealized language patterns tucked away in the mind get 'externalized' in communication, they are filtered and shaped by contingencies such as motor constraints, attention and memory limitations, errors of execution, local conventions, and more. As a result, it is argued, performance bears little useful relation to the pre-defined underlying object of study: competence. Students of linguistics have been taught not to waste their time with the worldly facts of performance.

This idea belies an unaccountably narrow view of what language is. It has diverted linguists' attention from many substantial questions, each with deep implications. Just a few examples: Without looking at performance, we wouldn't see the systematic and ingenious ways in which people handle the constant speech errors, hesitations, and misfires of conversation, along with the social delicacies of navigating these bouts of turbulence. Without looking at performance, we would not be witnessing the emerging breakthroughs from statistical research on newly-available large language corpora, with results suggesting that we can infer competence from experience with performance. Nor, finally, would linguistics have a causal account of how languages evolve historically: In the cycle of language transmission going from public (someone speaks) to private (someone's mental state is affected) and back to public (that person speaks), and so on indefinitely, both the private domain of competence and the public domain of performance are equally indispensable.

Influential traditions in the discipline of linguistics have embraced an idea that makes little sense, given the fact that language is, after all, just another striking animal behavior. Instead, the science of language should begin with fieldwork observation, for performance is ultimately our only evidence for competence. Perhaps the most unfortunate outcome of this idea is that generations of linguists who have eschewed the study of performance now have nothing to say about the essentially social function of language, nor about those aspects of social agency, cooperation, and social accountability that universally define our species' unique communicative capacity.
 

Whilst studying political economy during the late 1980s, I was deeply inspired by the pioneer of ecology and economics Hans-Christoph Binswanger who is now in his eighties and is being rediscovered by younger artists and activists (e.g. Tino Sehgal) who often quote him as an influence.

The wisdom of Bingswanger's work is that he recognised early on that endless growth is unsustainable, both in human and planetary terms. The current focus in mainstream economics is, he argues, too much on labour and productivity and too little on natural and intellectual resources. Dependency on endless growth, as the crisis that always emerges at the end of each cyclical bull market should teach us, is unrealistic.

Binswanger's goal was to investigate the similarities and differences between aesthetic and economic values through an examination of the historical relationship between economics and alchemy, which he made as interesting as it (at first) sounds outlandish. In his 1985 book Money and Magic, he showed how the brash concept of unlimited growth was inherited from the medieval discourse of alchemy, the search for a process that could turn lead into gold.

A focus of Binswanger's research has been on Goethe, especially his role in shaping social economics while finance minister at the court of Weimar. In Goethe's Faust, the eponymous character thinks in terms of infinite progress, while Mephisto recognizes the destructive potential of such an idea. At the beginning of part two of the play, Mephistopheles urges the ruler of an empire that is facing financial ruin because of profligate government spending to issue promissory notes, thus solving its debt problems.

Binswanger had been fascinated by the Faust legend since his childhood, and during his studies, he discovered that Goethe's introduction of paper money into his play was inspired by the story of the Scottish economist John Law, who in 1716 was the first man to establish a French bank issuing paper money. Strikingly, after Law's innovation, the Duke of Orleans got rid of all his alchemists because he realized that the immediate availability of paper money was far more powerful than any attempt to turn lead into gold.

Binswanger also connects money and art in a novel way. Art, he points out, is based on imagination and is part of the economy, while a bank's process of creating money in the form of promissory notes or coins is connected to imagination, since it is based on a prospective idea of bringing into being something that has yet to exist. At the same time, a company imagines producing a certain good and needs money to realize this, so it takes out a loan from a bank. If the product is sold, the 'imaginary' money that was created in the beginning has a counter-value in real products.

In classical economic theory, this process can be continued endlessly. Binswanger recognizes in Money and Magic, that this endless growth exerts a quasi-magical fascination. He produces a way of thinking about the problems of rampant capitalist growth, encouraging us to question the mainstream theory of economics, and to recognize how it differs from the real economy. But instead of rejecting the market wholesale, he suggests ways in which to moderate its demands. Thus the market does not have to disappear or be replaced, but can be understood as something to be manipulated for human purposes, rather than obeyed.

Another way of interpreting Binswanger's ideas is as follows: for most of human history, a fundamental problem has been the scarcity of material goods and resources, and so we have become ever more efficient in our methods of production and created rituals to enshrine the importance of objects in our culture. Less than a century ago, human beings made a world-changing transition through their rapacious industry. We now inhabit a world in which the overproduction of goods, rather than their scarcity, is one of our most fundamental problems. Yet our economy functions by inciting us to produce more and more with each passing year. In turn, we require cultural forms to enable us to sort through the glut, and our rituals are once again being directed towards the immaterial, towards quality and not quantity. This requires a shift in our values, from producing objects to selecting amongst those that already exist.

Whilst studying political economy during the late 1980s in St Gallen, I was deeply inspired by the pioneer of ecology and economics Hans-Christoph Binswanger (born 1929, Zürich). The director of the University of St Gallen's Institute for Economics and Ecology from 1962 to 1994, he is now in his eighties and is being rediscovered by younger artists and activists (e.g. Tino Sehgal) who often quote him as an influence.

The wisdom of Bingswanger's work is that he recognised early on that endless growth is unsustainable, both in human and planetary terms. The current focus in mainstream economics is, he argues, too much on labour and productivity and too little on natural and intellectual resources. Dependency on endless growth, as the crisis that always emerges at the end of each cyclical bull market should teach us, is unrealistic.

Binswanger's goal was to investigate the similarities and differences between aesthetic and economic values through an examination of the historical relationship between economics and alchemy, which he made as interesting as it (at first) sounds outlandish. In his 1985 book Money and Magic, he showed how the brash concept of unlimited growth was inherited from the medieval discourse of alchemy, the search for a process that could turn lead into gold.

A focus of Binswanger's research has been on Goethe, especially his role in shaping social economics while finance minister at the court of Weimar. In Goethe's Faust, the eponymous character thinks in terms of infinite progress, while Mephisto recognizes the destructive potential of such an idea. At the beginning of part two of the play, Mephistopheles urges the ruler of an empire that is facing financial ruin because of profligate government spending to issue promissory notes, thus solving its debt problems. Binswanger had been fascinated by the Faust legend since his childhood, and during his studies, he discovered that Goethe's introduction of paper money into his play was inspired by the story of the Scottish economist John Law, who in 1716 was the first man to establish a French bank issuing paper money. Strikingly, after Law's innovation, the Duke of Orleans got rid of all his alchemists because he realized that the immediate availability of paper money was far more powerful than any attempt to turn lead into gold.

Binswanger also connects money and art in a novel way. Art, he points out, is based on imagination and is part of the economy, while a bank's process of creating money in the form of promissory notes or coins is connected to imagination, since it is based on a prospective idea of bringing into being something that has yet to exist. At the same time, a company imagines producing a certain good and needs money to realize this, so it takes out a loan from a bank. If the product is sold, the 'imaginary' money that was created in the beginning has a counter-value in real products.

In classical economic theory, this process can be continued endlessly. Binswanger recognizes in Money and Magic, that this endless growth exerts a quasi-magical fascination. He produces a way of thinking about the problems of rampant capitalist growth, encouraging us to question the mainstream theory of economics, and to recognize how it differs from the real economy. But instead of rejecting the market wholesale, he suggests ways in which to moderate its demands. Thus the market does not have to disappear or be replaced, but can be understood as something to be manipulated for human purposes, rather than obeyed.

Another way of interpreting Binswanger's ideas is as follows: for most of human history, a fundamental problem has been the scarcity of material goods and resources, and so we have become ever more efficient in our methods of production and created rituals to enshrine the importance of objects in our culture. Less than a century ago, human beings made a world-changing transition through their rapacious industry. We now inhabit a world in which the overproduction of goods, rather than their scarcity, is one of our most fundamental problems. Yet our economy functions by inciting us to produce more and more with each passing year. In turn, we require cultural forms to enable us to sort through the glut, and our rituals are once again being directed towards the immaterial, towards quality and not quantity. This requires a shift in our values, from producing objects to selecting amongst those that already exist.

Ever since the landmark invention of diverse statistical techniques 100 years ago that allow us to properly compare the difference between the averages of two groups, we have deluded ourselves into thinking that it is such differences that are the salient—and often the only—important difference between groups. We have spent a century observing and interpreting such differences. We've become almost obsessed, and we should stop.

Yes, we can reliably say that men are taller than women, on average; that Norwegians are richer than Swedes; that first-born children are smarter than second-born children. And we can do experiments to detect tiny differences in means—between groups exposed and unexposed to a virus, or between groups with and without a particular allele of a gene. But this is too simple and too narrow a view of the natural world.

Our focus on averages should be retired. Or, if not retired, we should give averages an extended vacation. During this vacation, we should catch up on another sort of difference between groups that has gotten short shrift: we should focus on comparing the difference in variance (which captures the spread or range of measured values) between groups.

Part of the reason we've focused so much on the average is that the statistical tools for computing and comparing averages are so much easier and well developed. It is much harder to compare whether the variance of one group is different than the variance of another. But this calls to mind the well-known anecdote of the drunk searching for his keys on his knees under a lamp-post. His friend comes out of the bar and asks, "What are you doing on the ground?" "Searching for my keys," he replies. "Where did you lose them?" the friend asks. "Over there," the drunk says, pointing some distance away, "But the light is better here."

Drunk with statistical power, we've persuaded ourselves that the mean of a distribution is its most important property. But often it is not.

For example, we have focused on the differences in average wealth between groups—whether the US is richer than other countries, and what might have caused this, or whether bankers make more money than consultants and how this affects the professional choices of graduating college students. But the distribution of wealth in the groups may be equally important in explaining collective and individual outcomes and choices. Even if the US and Sweden have the same average income (roughly speaking), the variance in income is much higher in the US (income inequality is greater), and this fact, rather than any difference in means between the groups, may help explain what happens to people in these societies. For example, it may be the case that it is better for the health of a group, and (on average!) for the health of the individuals within it, for the group to have a more equal distribution of income—even if the average income is somewhat lower. We might wish for more equality at the expense of wealth.

Here is a hypothetical example leading to the opposite practical conclusion about inequality: when forming a crew of sailors for a sailboat, what would be best? To have all ten of the sailors have the same level of myopia, with mean vision of 20/200, or to have a group of sailors where nine of them had even worse vision, but one of them had perfect vision? The average vision could be the same in both groups, but, for the purposes of sailing the boat effectively, and for the survival of all aboard, it might be better to have more rather than less inequality. We might wish for more inequality at the expense of vision.

Or consider a medical example of how variance is important: there may be two conditions with equal average prognoses, say advanced AIDS and advanced liver cirrhosis, but doctors may offer "Do Not Resuscitate" orders to AIDS patients at much higher rates. It's tempting to conclude that doctors are more eager to avoid resuscitation in AIDS patients, perhaps for discriminatory reasons. But the real reason may be that the variance in survival in the AIDS group is much higher, and there may be many more patients in that group who will die imminently. It may be to this fact that the doctors are oriented rather than to the average survival of the two groups; the doctors may reason that they can wait to offer DNR orders to the cirrhosis patients.

A familiarity with variance would also allow us to make sense of the famously controversial hypothesis regarding why there are more male math professors at major universities: the mean overall math aptitude among men and women might be the same, but the variance in men might be higher. If so, this would mean that there are more men at the very bottom of the distribution (and, indeed, boys are roughly three times more likely to be mentally disabled than girls), but also that there are more men at the upper end of the distribution.

When we focus mainly on the mean, we miss the chance to observe interesting and important things about the world. And a restricted view has adverse practical as well as scientific implications. Do we want a richer, less equal society? Do we want educational programs to increase the equality of test scores, or the average? Will a cancer drug that makes some patients live longer and kills others sooner still be preferred by patients even if it has no effect on average survival? To really understand the relevant tradeoffs, we must acquire not only the tools, but also the vision, to focus on variance. 

We humans are fundamentally storytellers. We like to organize events into chains of causes and effects that explain the consequences of our actions. We like to assign credit and blame. This makes sense from an evolutionary standpoint. The ultimate job of our nervous system is to make actionable decisions, and predicting the consequences of those decisions is important to our survival.

Science is a rich source of powerful explanatory stories. For example, Newton explained how a force causes a mass to accelerate. This gives us a story of how an apple drops from a tree or a planet circles around the Sun. It allows us to decide how hard the rocket engine needs to push to get it to the Moon. Models of causation allow us to design complex machines like factories and computers that have fabulously long chains of causes and effects. They convert inputs into the outputs that we want.

It is tempting to believe that our stories of causes and effects are how the world works. Actually, they are just a framework that we use to manipulate the world and to construct explanations for the convenience of our own understanding. For example, Newton's equation, F= Ma, does not really say that force causes acceleration any more than it says that mass causes force. We humans tend to think of force as contingent, because we often have the choice as to whether to apply it or not. On the other hand, we tend to think of mass as not being under our control. Thus, we personify nature, imagining it almost as if natural forces are deciding to push on masses. It is much harder for us to imagine accelerations deciding to cause mass, so we tell the story a certain way. We credit gravitational force for keeping the planets orbiting around the Sun, and blame it for pulling the apple down from the tree.

This convenient personification of nature helps us use our mental storytelling machinery to explain the natural world. The cause-and-effect paradigm works particularly well when science is used for engineering, to arrange the world for our convenience. In this case, we can often set things up so that the illusion of cause-and-effect is almost a reality. The computer is a perfect example. The key to what makes a computer work is that the inputs affect the outputs, but not vice versa. The components used to construct the computer are constructed to create that same one-way relationship. These components, such as logic gates, are specifically designed to convert contingent inputs into predictable outputs. In other words, the logic gates of the computer are constructed to be atomic building blocks of cause-and-effect.

The notion of cause-and-effect breaks down when the parts that we would like to think of as outputs affect the parts that we would prefer to think of as inputs. The paradoxes of quantum mechanics are a perfect example of this, where our mere observation of a particle can "cause" a distant particle to be in a different state. Of course there is no real paradox here, there is just a problem with trying to apply our storytelling framework to a situation where it does not match.

Unfortunately, the cause-and-effect paradigm does not just fail at the quantum scale. It also falls apart when we try to use causation to explain complex dynamical systems like the biochemical pathways of a living organism, the transactions of an economy, or the operation of the human mind. These systems all have patterns of information flow that defy our tools of storytelling. A gene does not "cause" the trait like height, or a disease like cancer. The stock market did not go up "because" the bond market went down. These are just our feeble attempts to force a storytelling framework onto systems that do not work like stories. For such complex systems, science will need more powerful explanatory tools, and we will learn to accept the limits of our old methods of storytelling. We will come to appreciate that causes and effects do not exist in nature, that they are just convenient creations of our own minds.

Any enterprise has its limits and boundary conditions, and science is no exception. When the reach of science moves beyond these boundary conditions, when it demands respect and obedience that it hasn't earned, the results can be counter-productive. One example is Evidence-Based Medicine (EBM), which is the scientific idea that I think we should retire.

The concept behind EBM is certainly admirable: a set of best practices validated by rigorous experiments. EBM seeks to provide healthcare practitioners with treatments they can trust, treatments that have been evaluated by randomized controlled trials, preferably blinded. EBM seeks to transform medicine into a scientific discipline rather than an art form. What's not to like? We don't want to return to the days of quack fads and unverified anecdotes.

But we should only trust EBM if the science behind best practices is infallible and comprehensive, and that's certainly not the case. Medical science is not infallible. Practitioners shouldn't believe a published study just because it meets the criteria of randomized controlled trial design. Too many of these studies cannot be replicated. Sometimes the researcher got lucky and the experiments that failed to replicate the finding never got published or even submitted to a journal (the so-called publication bias). In rare cases the researcher has faked the results. Even when the results can be replicated they shouldn't automatically be believed—conditions may have been set up in a way that misses the phenomenon of interest so a negative finding doesn't necessarily rule out an effect.

And medical science is not comprehensive. Best practices often take the form of simple rules to follow, but practitioners work in complex situations. EBM relies on controlled studies that vary one thing at a time, rarely more than two or three. Many patients suffer from multiple medical problems, such as Type 2 diabetes compounded with asthma. The protocol that works with one problem may inappropriate for the others. EBM formulates best practices for general populations but practitioners treat individuals, and need to take individual differences into account. A treatment that is generally ineffective might still be useful for a sub-set of patients. Further, physicians aren't finished once they select a treatment; they often have to adapt it. They need expertise to judge whether a patient is recovering at an appropriate rate. Physicians have to monitor the effectiveness of a treatment plan and then modify or replace it if it isn't working well. A patient's condition may naturally fluctuate and physicians have to judge the treatment effects on top of this noisy baseline.

Sure, scientific investigations have done us all a great service by weeding out ineffective remedies. For example, a recent placebo-controlled study found that arthroscopic surgery provided no greater benefit than sham surgery for patients with osteoarthritic knees. But we also are grateful for all the surgical advances of the past few decades (e.g., hip and knee replacements, cataract treatments) that were achieved without randomized controlled trials and placebo conditions. Controlled experiments are therefore not necessary for progress in new types of treatments and they are not sufficient for implementing treatments with individual patients who each have unique profiles.

Worse, reliance on EBM can impede scientific progress. If hospitals and insurance companies mandate EBM, backed up by the threat of lawsuits if adverse outcomes are accompanied by any departure from best practices, physicians will become reluctant to try alternative treatment strategies that have not yet been evaluated using randomized controlled trials. Scientific advancement can become stifled if front-line physicians, who blend medical expertise with respect for research, are prevented from exploration and are discouraged from making discoveries. 

Humans are biologically exceptional. We're exceptionally long-lived and exceptionally cooperative with non-kin. We have exceptionally small guts and exceptionally large brains. We have an exceptional communication system and an exceptional ability to learn from other members of our species. Scientists love to study biologically exceptional human traits such as these, and that's a perfectly reasonable research strategy. Human evolutionary exceptionalism, however—the tendency to assume that biologically exceptional human traits come into the world through exceptional processes of biological evolution—is a bad habit we need to break. Human evolutionary exceptionalism has sown misunderstanding in every area it has touched. Here are three examples.

Human niche construction. Humans have exerted biologically exceptional effects on their environments. In our evolutionary past, these so-called niche construction effects occasionally created the necessary and sufficient condition for natural selection: a generationally persistent covariance between genes and fitness. For example, earlier hominins' experimentation with cooking (which required the generationally persistent availability of culturally transmitted knowledge on how to control fire) made their food more digestible. Consequently, genetic mutations that shrank the human gut, teeth, and jaw muscles were naturally selected because they enabled resources to be re-assigned to the construction of new adaptive faculties (including cognitive ones).

For years, Niche Construction theorists have argued that standard evolutionary theory cannot account for such interactions between humans' culturally mediated environmental effects and natural selection. In response, they have promoted niche construction as a "neglected evolutionary process" that collaborates with natural selection to direct evolution. However, they obtain persuasive force for this argument by re-defining what evolution is. Humans' niche construction activities have undoubtedly exposed new covariances between genetic variation and fitness during human evolution, but those activities have neither created that variation nor filtered it, so they don't constitute an evolutionary process. Culturally mediated human niche construction is real, important, sometimes evolutionarily significant, and certainly worthy of study, but it doesn't compel a revision to our understanding of how evolution works.

Major Evolutionary Transitions. Over the past three billion years, natural selection has yielded several pivotal innovations in how genetic information gets assembled, packaged, and transmitted across generations. These so-called major evolutionary transitions have included the transition from RNA to DNA; the union of genes into chromosomes; the evolution of eukaryotic cells; the advent of sexual reproduction; the evolution of multicellular organisms; and the appearance of eusociality (notably, among ants, bees, and wasps) in which only a few individuals reproduce and the others work as servants, soldiers, or babysitters. The major evolutionary transitions concept, when properly applied, is useful and clarifying.

It is therefore regrettable that the concept's originators made category mistakes by characterizing two distinctly human traits as outcomes of major evolutionary transitions. Their first category mistake was to liken human societies (which are exceptional among the primates for their nested levels of organization, their mating systems, and a hundred other features) to those of the eusocial insects because the individuals in both kinds of societies "can survive and transmit genes . . . only as part of a social group." This is an unfortunate case of science by analogy: The fact that humans are adapted for living in social groups does not imply that they, like ants, bees, wasps, and termites, need groups to reproduce. If the chemistry, timing, and lighting are just right, any human male and any human female, plucked from their social groups at random, can manage to convey genetic information to the next generation just fine.

Their second category mistake was to hold up human language as the outcome of major evolutionary transition. To be sure, human language, as the only communication system with unlimited expressive potential that natural selection ever devised, is biologically exceptional. However, the information that language conveys is contained in our minds, not in our chromosomes. We donxt yet know precisely where or when human language evolved, but we can be reasonably confident about how it evolved: via the gene-by-gene design process called natural selection. No major evolutionary transition was involved.

Human Cooperation. Humans are exceptionally generous, particularly toward non-relatives. We cooperate with strangers when we'd be better off in the short term by competing. We donate anonymously to charities. We accomplish group projects even though all participants surely recognize that they would be better off, at least in the short run, by loafing and letting the others do the work. We share with needy strangers even when we know they will never repay us. We praise generosity, and denounce stinginess, even when the behaviors in question have not affected us directly.

In the past, all of these cooperation-related phenomena spent time on evolutionary scientists' lists of "unsolved puzzles about human cooperation." The good news is that scientists have already succeeded in nudging many of them toward the "solved puzzles" list. The bad news is that some scholars have gone in the opposite direction: They have moved these problems onto the list of "mysteries"—problems so perplexing that we should abandon hope of ever solving them within the standard inclusive-fitness-maximizing view of natural selection. Their mystification has led them, at turns, to invoke evolutionary explanations that are inappropriate for species in which all individuals reproduce, to propose new evolutionary processes that are not evolutionary processes at all (but rather, proximate behavioral patterns that require evolutionary explanations), and to presume without justification that certain quirks of modern social life were selection pressures of our deep evolutionary past. Explaining the exceptional features of human cooperation is challenging enough without muddling the problem space even further with conceptual false starts, questionable historical premises, and labyrinthine evolutionary scenarios.

Human evolutionary exceptionalism is counterproductive for science. It leads to internecine squabbles. Correcting the misconceptions that follow in its wake distracts specialists from more productive work. Finally, it confuses non-specialists who lack the time to sort through these controversies for themselves. It's good to be curious—and, sometimes, even querulous—about how our biologically exceptional traits evolved, but we should resist the idea that evolution made up new rules just for us. 

An obvious truth, which is either being ignored or going unaddressed in cancer research, is that mouse models do not mimic human disease well and are essentially worthless for drug development. We cured acute leukemia in mice in 1977 with drugs that we are still using in exactly the same dosage and duration today in humans—with dreadful results. Imagine the artificiality of taking human tumor cells, growing them in lab dishes, then transferring them to mice whose immune systems have been compromised so they cannot reject the implanted tumors, and then exposing these "xenografts" to drugs whose killing efficiency and toxicity profiles will then be applied to treat human cancers. The inherent pitfalls of such an entirely synthesized unnatural model system have also plagued other disciplines.

A recent scientific paper showed that nearly 150 drugs tested, at the cost of billions of dollars, in human trials of sepsis failed because the drugs had been developed using mice. Unfortunately, what looks like sepsis in mice turned out to be very different from what sepsis is in humans. Coverage of this study by Gina Kolata in the New York Times brought heated response from within the biomedical research community: "There is no basis for leveraging a niche piece of research to imply that mice are useless models for all human diseases. . . . The key is to construct the appropriate mouse models and design the experimental conditions that mirror the human situation."

The problem is that there are no appropriate mouse models which can "mirror the human situation." So why is the cancer research community still dominated by the dysfunctional tradition of employing mouse models to test hypotheses for development of new drugs?

Robert Weinberg of the Whitehead Institute at MIT has provided the best answer. In an interview, he offered two reasons. First, there's no other model with which to replace that poor mouse, and second, the FDA "has created inertia because it continues to recognize these [models] as the gold standard for predicting the utility of drugs."

There is a third reason related more to the frailties of human nature. Too many eminent laboratories and illustrious researchers have devoted entire lives to studying malignant diseases in mouse models, and they are the ones reviewing one another's grants and deciding where the NIH money gets spent. They are not prepared to concede that mouse models are basically valueless for most cancer therapeutics.

One of the main reasons we continue to stick to this archaic ethos is to obtain funding. Here is one example: I decided to study a bone marrow malignant disease called myelodysplastic syndromes (MDS), which frequently evolves to acute leukemia, back in the early 1980s. One early decision I made was to concentrate my research on freshly obtained human cells and not to rely on mice or petri dishes alone. In the last three decades, I have collected over 50,000 bone-marrow biopsies, normal control buccal smear cells, and blood, serum, and plasma samples in a well-annotated tissue repository backed by a computerized bank of clinical, pathologic, and morphologic data. Using these samples, we have identified novel genes involved in causing certain types of MDS, as well as sets of genes related to survival, natural history of the disease, and response to therapy. But when I used bone-marrow cells from treated MDS patients to develop a genomic-expression profile that was startlingly predictive of response and applied for an NIH grant to validate the signature, the main criticism was that before confirming it through a prospective trial in humans, I should first reproduce it in mice.

It's time to let go of the mouse models—at least, as surrogates for bringing drugs to the bedside. Remember what Mark Twain said: "What gets us into trouble is not what we don't know; it's what we know for sure that just ain't so."

Researchers argue about the extent to which people are rational, but the real problem with the concept of the rational individual is that our desires, preferences and decisions are not primarily the result of individual thinking. Because economics and much of cognitive science takes the unit of analysis to be an independent individual, they have difficulty accounting for social phenomena such as financial bubbles, political movements, panics, technology trends, or even the course of scientific progress.

Near the end of the 1700s, philosophers began to declare that humans were rational individuals. People were flattered by being recognized as individuals, and by being called rational, and the idea soon wormed its way into the belief systems of nearly everyone in upper-class Western society. Despite resistance from church and state, this idea of rational individuality replaced the assumption that truth only came from god and king. Over time, the ideas of rationality and individualism changed the entire belief system of Western intellectual society, and today it is doing the same to the belief systems of other cultures.

Recent research data from my lab and other labs are changing this argument, and we are now coming to realize that human behavior is determined as much by social context as by rational thinking or individual desires. Rationality, as economists use the term, means that an individual knows what he or she wants and acts to get it. But this new research shows that in this regard, social network effects often, and perhaps typically, dominate both the desires and the decisions about how individuals act.

Recently, economists have moved toward the idea of "'bounded rationality'," which means that we have biases and cognitive limitations that prevent us from realizing full rationality. Our dependence on social interactions, however, is not simply a bias or a cognitive limitation. Social learning is an important method of enhancing individual decision-making. Similarly, social influence is central to constructing the social norms that enable cooperative behavior. Our ability to survive and prosper is due to social learning and social influence at least as much as it is due to individual rationality.

These data tell us that what we want and value, as well as how we choose to act in order to obtain our desires, are a constantly evolving property of interactions with other people. Our desires and preferences are mostly based on what our peer community agrees is valuable, rather than on rational reflection based directly on our individual biological drives or inborn morals.

For instance, after the Great Recession of 2008, when many houses were suddenly worth less than their mortgages, researchers found that it only took a few people walking away from their houses and mortgages to convince many of their neighbors to do the same thing. A behavior that had previously been thought nearly criminal or immoral, i.e., purposely defaulting on a mortgage, now became common. Using the terminology of economics, in most things we are collectively rational, and only in some areas are we individually rational.

By mathematically modeling the social learning and social pressure between people my colleagues and I have been able to accurately model and predict crowd phenomena such as this cascade of mortgage defaulting. Importantly, we have also found that it is possible to shape real-world crowd behaviors by using social network incentives that alter the connections between people, and that these social incentives are much more effective than standard individual economic incentives. In one particularly striking example we were able to use social network incentives to deflate a 'groupthink' bubble among foreign exchange traders and consequently double the return on investment of the individual traders.

So instead of individual rationality I believe that we have common sense. The collective intelligence of a community comes from the surrounding flow of ideas and examples; we learn from others in our environment, and these others learn from us. Over time, a community with members who actively engage with each other creates a group with shared, integrated habits and beliefs. When the flow of ideas incorporates a constant stream of outside ideas as well, then the individuals in the community make better decisions than they could on their own.

This idea of a collective intelligence that develops within communities is an old one; indeed, it is embedded in the English language. Consider the word "kith," familiar to modern English speakers from the phrase "kith and kin." Derived from old English and old German words for knowledge, kith refer to a more or less cohesive group with common beliefs and customs. These are also the roots for "couth," which means possessing a high degree of sophistication, as well as its more familiar counterpart, uncouth. Thus, our kith is the circle of peers (not just friends) from whom we learn the "correct" habits of action.

Our ancestors understood that our culture and the habits of our society are social contracts, and that both depend primarily upon social learning. As a result, observing the attitudes, actions, and outcomes of peers, rather than by using logic or argument is how we learn most of our public beliefs and habits. Learning and re-enforcing this social contract is what enables a group of people to coordinate their actions effectively. It is time that we dropped the fiction of individuals as the unit of rationality, and recognized that we are embedded in the surrounding social fabric.

From top to bottom of the profession, scientists are forsaking their chosen vocation in greater numbers than ever before, in favour of a more dependable and less stressful source of income. What is the basis of this stress and uncertainty, which so severely depletes the ranks of that indispensable community who seek to further humanity's understanding of nature, and thereby our ability to manipulate nature for the greater good? At the sharp end, it is the members of those ranks—scientists themselves—via the convention of apportioning funding by peer review of grant applications.

Only at the sharp end, of course: I certainly do not lay blame at scientists' feet. In fact, I don't really lay blame anywhere: the issue is that the prevailing system evolved in a different time, and in circumstances to which it was well suited, but has signally failed to adapt—indeed, has shown itself intrinsically non-adaptable—to present conditions. What is needed is a replacement system, which solves the problems that everyone in science agrees exist today but which still distributes funds according to metrics that all constituencies agree is fair.

The basic obstacle to doing this is that the overall merit of the contemporary peer-review system is apparently a local maximum: numerous tweaks have been proposed, but all have resisted adoption because they do more harm than good. But is it a global maximum: is it, as Churchill described democracy, the worst option except for all the others, or could a radical departure rank more highly by all key measures? Here I sketch a possible option. I am not sure it ticks all the boxes (though I do quite like it), but I do claim it shows sufficient promise as a candidate that the scientific community should no longer acquiesce in the current system on the assumption that nothing better is possible.

First, briefly: what's so wrong with peer review of grant applications these days? Two words: pay line. Peer review evolved when the balance between supply and demand of public research funds was such that at least 30% of applications could be funded. It worked well: if you didn't really know how to design a project, or how to communicate its value to your colleagues, or how to perform it economically, these failings would emerge and you would learn how to avoid them until eventually those colleagues would recommend to the government that you be given your chance. But these days, the corresponding percentage is typically in single digits. Does that mean you just have to be really good? I wish.

What it actually means is that you have to be not only really good but also really persistent, and moreover—and this is by far the worst aspect—really, really convincing in your argument that the project will succeed. What's so bad about that? Simply that some projects are (much) easier than others, and the hard ones tend to be those that determine the long-term rate of progress of a discipline, even though they have a significant failure rate. As such, a system that overwhelmingly neglects high-risk high-gain work hugely slows scientific progress, with catastrophic consequences for humanity. Also, cross-disciplinary research—work drawing together ideas not previously combined, which historically has been also exceptionally fruitful—is almost impossible to get funded, simply because no research panel ("study section", in NIH vernacular) has the necessary range of expertise to understand the proposal's full value.

I claim that this would be largely solved by a system based on peer recognition rather than peer review. When a scientist first applies for public research funds, his or her career would be divided into five-year periods, starting with the past five years (period 0), the coming five (period 1), etc. Period 1 is funded at a low, entry-level rate on the basis of simple qualifications (possession of a doctorate, number of years of postdoctoral study, etc), and without the researcher having provided any description of what specific research is to be undertaken. Period 2's funding level is determined, as a percentage of total funds available for the scientist's discipline of choice, again without any description of what work is planned to be performed, but instead on the basis of how well cited was his or her work performed in period 0.

This decision is made at the end of period 1 year 4, based on all citations since period 0 year 2 (so a total of eight years) to papers published in period 0 year 2 through period 1 year 1 (five years, approximating the interval when work done during period 0 will have been published). Citations are weighted according to whether one is a first/senior/middle author; self-citations are not counted; only papers reporting new research that depended on research funds are counted. Consideration is given to seniority and level of funding during the relevant period, according to a formula applied across the board rather than by discretion. Funding for period 3 is determined similarly, at the end of period 2 year 4, on the basis of work performed during period 1, and so on. Flexibility is incorporated concerning front-loading of funds to year 1 of a given period, to allow for large capital expenditures.

This improves on the current system in many ways. Zero time is spent preparing and submitting (and re-submitting…) descriptions of proposed research, and zero money on evaluating such proposals. Bias against high-risk high-gain work is greatly reduced, both by the lack of peer review and also because funding periods exceed the currently-typical three years. Significance of past work is evaluated after an appropriate period of time, not by such "first-impression" measures as the impact factor of journals where one has just published. One can also split one's application across multiple disciplines, with funding level from each discipline proportionated accordingly, removing the bias against cross-disciplinary research. Finally, one has a year at the end of a period to plan what work one will do in the next period, in full knowledge of what resources will be at one's disposal.

Researchers are of course free to seek additional funds from elsewhere (and indeed, some public funds could still be apportioned via the traditional method). Thus, this need not even be a particularly massive dislocation: it could easily be phased in.

Worth considering?

There is no reason to believe, and much reason not to believe, that the measure of a so-called "Intelligence Quotient" in any way reflects some basic cognitive capacity, or "natural kind" of the human mind. The domain-general measure of IQ is not motivated by any recent discovery of cognitive or developmental psychology. It thoroughly confounds domain-specific abilities—distinct mental capacities for, say, geometrical and spatial reasoning about shapes and positions, mechanical reasoning about mass and motion, taxonomic reasoning about biological kinds, social reasoning about other people's beliefs and desires, and so on—which are the only sorts of cognitive abilities for which an evolutionary account seems plausible in terms of natural selection for task-specific competencies.

Nowhere in the animal or plant kingdoms does there ever appear to have been natural selection for a task-general adaptation. An overall measure of intelligence or mental competence is akin an overall measure for "the body," taking no special account of the various and specific bodily organs and functions, such as hearts, lungs, stomach, circulation, respiration, digestion and so on. A doctor or biologist presented with a single measure for "Body Quotient" (BQ) wouldn't be able to make much of it.

IQ is a general measure of socially acceptable categorization and reasoning skills. IQ tests were designed in behaviorism's heyday, when there was little interest cognitive structure. The scoring system was tooled to generate a normal distribution of scores with a mean of 100 and a standard deviation of 15.

In other societies, a normal distribution of some general measure of social intelligence might look very different, in that some "normal" members of our society could well produce a score that is a standard deviation from "normal" members of another society on that other society's test. For example, in forced-choice tasks East Asian students (China, Korea, Japan) tend to favor field-dependent perception over object-salient perception, thematic reasoning over taxonomic reasoning, and exemplar-based categorization over rule-based categorization.

American students generally prefer the opposite. On tests that measure these various categorization and reasoning skills, East Asians average higher on their preferences and Americans average higher on theirs'. There is nothing particularly revealing about these different distributions other than that they reflect some underlying socio- cultural differences.

There is a long history of acrimonious debate over which, if any, aspects of IQ are heritable. The most compelling studies concern twins raised apart and adoptions. Twin studies rarely have large sample populations. Moreover, they often involve twins separated at birth because a parent dies or cannot afford to support both, and one is given over to be raised by relatives, friends or neighbors. This disallows ruling out the effects of social environment and upbringing in producing convergence among the twins. The chief problem with adoption studies is that the mere fact of adoption reliably increases IQ, regardless of any correlation between the IQs of the children and those of their biological parents. Nobody has the slightest causal account of how or why genes, singly or in combination, might affect IQ. I don't think it's because the problem is too hard, but because IQ is a specious rather natural kind.

The concept of ‘module’ in neuroscience (meaning sufficient for a function, given gas-in-the-tank background conditions) invariably causes more confusion than clarity. The problem is that any neuronal business of any significant complexity is underpinned by spatially distributed networks, and not just incidentally but essentially—and not just cortically, but between cortical and subcortical networks. This is true, for example, of motion perception and pattern recognition, as well as motor control and reinforcement learning, not to mention feelings such as mustering courage to face a threat or deciding to hide instead of run. It is true of self-control and moral judgment. It is likely to be true of conscious experience. The output of a network can vary as the activity of the network’s individual neurons varies. What is poorly understood is how nervous systems solve the coordination problem; i.e. how does the brain orchestrate the right pattern of neuronal activation across networks to get the job done?

This is not all that is amiss with ‘module’. Traditionally, modules are supposed to be encapsulated –aka insulated. But even the degree to which an early sensory area, such as primary visual cortex (V1) is encapsulated has been challenged. Visual neurons in V1 double their firing rate if the animal is running, no matter the identity of the visual input across conditions, to take but one example. To add to the ‘module’ mess, it turns out that specialization in an area such as the V1 appears to be dependent to some nontrivial degree on the statistics of the input. Visual cortex is visual largely because it is connected to the retina and not the cochlea, for example. Notice that in blind subjects, the visual cortex is recruited in reading braille, a high-resolution spatial—and somatosensory—task. As would be expected if specialization depends on the statistics of the network’s input, infant brains have much more plasticity in regional specialization than do mature brains. Doris Trauner and Elizabeth Bates discovered that human infants with a left hemispherectomy can learn language quite normally, whereas an adult who undergoes the same surgery will have severe language deficits. 

I think of ‘module’ in the way I think of ‘nervous breakdown’—mildly useful in the old days when we had no clue about what was going on under the skull, but of doubtful explanatory significance these days. 

The Law of Parsimony, also known as Occam's razor, does not warrant a funeral but it does have some problems in its description of reality. This law states that the most simple of two competing theories should be the preferred one, and that entities should not be multiplied needlessly. It maintains a lofty stature in philosophy and science and is often utilized as a literary device. Using the Law of Parsimony is the essence of good detective fiction perhaps none better achieved than by Arthur Conan Doyle, a physician, who sharpened to perfection Occam's razor in the reasoning employed by his renowned creation, Sherlock Holmes. One of Holmes' most noted rules is that "when you have eliminated the impossible, whatever remains, however improbable, must be the truth." 

As an absolute, the Law of Parsimony is floundering. Not because it is aging poorly, but rather because it is being challenged more and more by the complexity of the real world and its need for a valid counterweight. From my vantage point as a physician in the practice of clinical neurology, its usefulness, which has always been a guiding principle for me, can easily lead to blind spots and errors in judgment when rigidly followed.

A recent case in point is a 79 year- old woman who was complaining of difficulty with her balance with several recent falls. This could be dismissed to age. However, she has multiple other factors in her history that need to be taken into account including a diabetic neuropathy making her feet lose sensation as well as compression of her cervical spinal cord producing weakness in her legs. She also has a hearing problem with a long history of intermittent vertigo. In addition, she is of Scandinavian descent, making her somewhat more prone genetically to vitamin B12 deficiency secondary to poor absorption that in her case may be exacerbated by medications used to inhibit acid reflux. This vitamin deficiency by itself can produce neuropathy and degeneration of her spinal cord. It is in this complicated clinical setting that the Law of Parsimony utterly fails and I doubt that even the great Holmes, who has the luxury of being a fictional character, could tie all of these loose ends into a simple knot. To provide the appropriate care to this patient, I needed to utilize Hickam's Dictum, the medical profession's counterargument to Occam's razor. This maxim of Dr. John Hickam, who died in 1970, states very simply "a patient can have as many diagnoses as [she] damn well pleases."

The crucial role that the Law of Parsimony renders in how we reason is beyond question. This law dates back to the Greek philosophers who refined it from their antecedents since I suspect we evolved to seek simplicity over complexity. The desire for unity and singleness is satisfying and very seductive. However, at times it needs to be challenged by Hickam's Dictum, which is a variation of the Principle of Plenitude. This view of reality also dates to ancient Greek philosophy, which postulates that if the universe is to be as perfect as possible it must be as full as possible, in the sense that it contains as many kinds of things as it possibly could contain.

With the complexity, inconsistency, ambiguity and ultimate uncertainty that define our reality, we should not limit ourselves to using only one or the other of these valuable tools of analysis. We need to be more willing to have our own positions challenged, striving to keep an open mind to other arguments, other viewpoints and conflicting data. In order to make the best decisions for the best reasons, we must choose the appropriate heuristics coupled with intellectual honesty to guide our thinking as we grapple with the cunning machinations of the world we inhabit.

In the 17th century, René Descartes proposed that the nervous system worked a bit like the nifty statues in the royal gardens of Saint-Germain, whose moving parts were animated by water that ran through pipes inside of them. Descartes' idea is illustrated in the well-known line drawing that appears in many introductory psychology textbooks that shows a person puzzlingly sticking his foot in a fire, presumably to illustrate Descartes' idea about hydraulic reflexes.

Three centuries on, in the mid-1900's, the detritus of the hydraulic conception of behavior, now known to be luminously wrong, was strewn about here and there. In the scholarly literature, for instance, there were traces in Freud's corpus—catharsis will relieve all that pressure. Among the Folk, hydraulic metaphors were—and still are—used to express mental states. I'm going to blow my top. Having written an essay for Edge today, I feel drained.

There is, to be sure, still plenty of debate about how the mind works. No doubt even on the pages of this year's Question there will be spirited discussion about how well the brain-as-device-that-computes notion is doing to advance psychology. Still, while the computational theory of mind might not have won over everyone, the hydraulic model Descartes proposed is dead and buried.

Well, dead anyway. Buried… maybe not. (And, to be sure, hydraulics is, as it turned out, the right explanation for a pretty important (male) biological function; just not the one Descartes had in mind.) The metaphors that recruit the intuition that the mind is built of fluid-filled pipes, along with junctions, valves, and reservoirs, point to the possibility that Descartes was drawn to the notion of a hydraulic mind not only because of the technology of the day, but also because there is something intuitively compelling about the idea.

And, indeed, Cartesian hydraulics has been revived in at least one incarnation in the scholarly literature, though I doubt it's the only one. For the last decade or so, some researchers have been advancing the notion that there is a "reservoir" of willpower. You can't have an empty reservoir, the theory goes, in order to exert self-control—resisting eating marshmallows, avoiding distractions, etc.—and as the reservoir gets drained, it become harder and harder to exert self-control.

Given how wrong Descartes was about how the mind works, it's pretty clear that this sort of idea just can't be right. There have recently been a number of experimental results that disconfirm predictions made by the model, but that's not why the idea should be abandoned. Or, at least, the data aren't the best reason the idea should be abandoned. The reason the idea should be left to die is the same reason that Descartes' idea should be: Although the mind might not work just like a digital computer—no doubt the mind is different from your basic PC in any number of important ways –we do know that computation of some sort is much, much more likely to be a good explanation for human behavior than hydraulics.

People will disagree about whether Planck was right about the speed of scientific change. Psychology, I would argue, has a couple of handicaps that might make the discipline more susceptible to Planck's worries than some other disciplines.

First, theories in psychology are often driven by—indeed, held captive by—our intuitions. I'm fond of the way that Dan Dennett put it in 1991 when he was talking about the (also luminously wrong) idea of the Cartesian Theater, the dualist idea that there is a "special center in the brain," the epicenter of identity, the One and True Me, the wizard behind the curtain. He thought this notion was "the most tenacious bad idea bedeviling our attempts to think about consciousness." Human intuitions tell us that there's a special "me" in there somewhere, an intuition that serves to resurrect the idea of a special center over and over again.

Second, psychologists are too polite with each other's ideas. (Economists, for example, in my experience, don't frequently commit this particular sin.) In 2013, a prominent journal in psychology published a paper that reported the results of attempts to replicate a previously published finding. The title of the article was, before the colon, the phenomenon in question and then, after the colon: "Real or Elusive Phenomenon?" The pairing of real versus elusive as opposed to nonexistent highlights that it's considered so rude to suggest that a result was a false positive—as opposed to something that's simply hard to replicate—that people in the field won't even say out loud that prior work might have been pointing to something that isn't, really, there.

Of course intuitions interfere with theoretical innovation in other disciplines. No doubt the obviousness of the sun going around the Earth, bending across the sky each day, delayed acceptance of the heliocentric model. Everyone knows the mind isn't a hydraulic shovel, but it does feel like some sort of reservoir of stuff gets used up just as it does feel like the sun is moving while we stay put.

Still, it's time that Cartesian hydrolicism be put to rest in the same way that Cartesian dualism was. 

In today's Freud-influenced popular psychology, repression remains big. People presume, for example, that unburying repressed traumas is therapeutic. But do we routinely exile our painful memories? "Traumatic memories are often repressed," agree 4 in 5 undergraduates and members of the American and British general publics (in recent surveys reported by a University of California, Irvine research team).

Actually, say today's memory researchers, there is little evidence of such repression, and much evidence of its opposite. Traumatic experiences (even witnessing a loved one's murder, being terrorized by a hijacker or a rapist, losing everything in a natural disaster) rarely get banished into the unconscious, like a ghost in a closet. Traumas more commonly get etched on the mind as persistent, haunting memories. Moreover, extreme stress and its associated hormones enhance memory, producing unwanted flashbacks that plague survivors. "You see the babies," said one Holocaust survivor. "You see the screaming mothers. . . . It's something you don't forget."

The scientist-therapist "memory war" lingers, but it is subsiding. Today's psychological scientists appreciate the enormity of unconscious, automatic information processing, even as mainstream therapists and clinical psychologists report increasing skepticism of repressed and recovered memories.

A pervasive idea in fundamental physics and cosmology that should be retired: the notion that we live in a multiverse in which the laws of physics and the properties of the cosmos vary randomly from one patch of space to another. According to this view, the laws and properties within our observable universe cannot be explained or predicted because they are set by chance. Different regions of space too distant to ever be observed have different laws and properties, according to this picture. Over the entire multiverse, there are infinitely many distinct patches. Among these patches, in the words of Alan Guth, "anything that can happen will happen—and it will happen infinitely many times." Hence, I refer to this concept as a Theory of Anything.

Any observation or combination of observations is consistent with a Theory of Anything. No observation or combination of observations can disprove it. Proponents seem to revel in the fact that the Theory cannot be falsified. The rest of the scientific community should be up in arms since an unfalsifiable idea lies beyond the bounds of normal science. Yet, except for a few voices, there has been surprising complacency and, in some cases, grudging acceptance of a Theory of Anything as a logical possibility. The scientific journals are full of papers treating the Theory of Anything seriously. What is going on?

Have experiments revealed that our observable universe and the fundamental laws are too complicated to be explained by normal science? Absolutely not! Quite the opposite! On the macroscopic scale, the latest measurements show our observable universe to be remarkably simple, described by very few parameters, obeying the same physical laws throughout and exhibiting remarkably uniform structure in all directions. On the microscopic scale, the Large Hadron Collider at CERN (European Center for Nuclear Research) has revealed the existence of the Higgs, in accord with what theorists had predicted nearly 50 years ago based on sound scientific reasoning.

A simple outcome calls for a simple explanation for why it had to be so. Why, then, consider a Theory of Anything that allows any possibility, including complicated ones? The motivation is the failure of two favorite theoretical ideas—inflationary cosmology and string theory. Both were thought to produce a unique outcome. Inflationary cosmology was invented to transform the entire cosmos into a smooth universe populated by a scale-invariant distribution of hot spots and cold spots, just as we observe it to be. String theory was supposed to explain why elementary particles could only have the precise masses and forces that they do. After more than 30 years investment in each of these ideas, theorists have found that they are not able to achieve these ambitious goals. Inflation, once started, runs eternally and produces a multiverse of pockets whose properties vary over every conceivable possibility—flat and non-flat; smooth and non-smooth; scale-invariant and not scale-invariant; etc. Despite laudable efforts by many theorists to save the theory, there is no solid reason known today why inflation should cause our observable universe to be in a pocket with the smoothness and other very simple properties we observe. A continuum of other conditions is equally possible.

In string theory, a similar explosion of possibilities has occurred, driven by attempts to explain the 1998 discovery of the accelerated expansion of the universe. The acceleration is thought to be due to positive vacuum energy, an energy associated with empty space. Instead of predicting a unique possibility for the vacuum state of the universe and particles and fields that inhabit it, our current understanding of string theory is that there is a complex landscape of vacuum states corresponding to exponentially different kinds of particles and different physical laws. The set of vacuum space contains so many possibilities that, surely, it is claimed, one will include the right amount of vacuum energy and the right kinds of particles and fields. Mix the inflation and string theory, and the unpredictability multiplies. Now every combination of macrophysical and microphysical possibilities can occur.

I suspect that the theories would never have gained the acceptance they have if these problems had been broadly recognized at the outset. Historically, if a theory failed to achieve its goals, it was improved or retired. In this case, though, the commitment to the theories has become so strong that some prominent proponents have seriously advocated moving the goalposts. They say that we should be prepared to abandon the old-fashioned idea that scientific theories should give definite predictions and to accept a Theory of Anything as the best that can ever be achieved.

I draw the line there. Science is useful insofar as it explains and predicts why things are the way they are and not some other way. The worth of a scientific theory is gauged by the number of do-or-die experimental tests it passes. A Theory of Anything is useless because it does not rule out any possibility and worthless because it submits to no do-or-die tests. (Many papers discuss potential observable consequences, but these are only possibilities, not certainties, so the Theory is never really put at risk.)

A priority for theorists today is to determine if inflation and string theory can be saved from devolving into a Theory of Anything and, if not, seek new ideas to replace them. Because an unfalsifiable Theory of Anything creates unfair competition for real scientific theories, leaders in the field can play an important role by speaking out—making it clear that Anything is not acceptable—to encourage talented young scientists to rise up and meet the challenge. The sooner we can retire the Theory of Anything, the sooner this important science can progress. 

If an idea is not absurd, there is no hope for it," Einstein reportedly said. I would like to broaden the definition of addiction and retire the scientific idea that all addictions are pathological and harmful.

Since the beginning of formal diagnostics over 50 years ago, the compulsive pursuit of gambling, food, and sex (known as non-substance rewards) have not been regarded as addictions; only abuse of alcohol, opioids, cocaine, amphetamines, cannabis, heroin and nicotine have been formally regarded as addictions. This categorization rests largely on the fact that substances activate basic "reward pathways" in the brain associated with craving and obsession, and produce pathological behaviors. Psychiatrists work within this world of psychopathology—that which is abnormal and makes you ill.

As an anthropologist, I feel they are limited by this view. Scientists have now shown that food, sex and gambling compulsions employ many of the same brain pathways activated by substance abuse. Indeed, the 2013 edition of the Diagnostic and Statistical Manual of Mental Disorders (the DSM) has finally acknowledged that at least one form of non-substance abuse can be regarded as an addiction: gambling. The abuse of sex and food have not yet been included. Neither has romantic love. I shall propose that love addiction is just as real as any other addiction, in terms of its behavior patterns and brain mechanisms. Moreover, it's often a positive addiction.

Scientists and laymen have long regarded romantic love as part of the supernatural, or as a social invention of the Troubadours in 12^th century France. Evidence does not support these notions. Love songs, poems, stories, operas, ballets, novels, myths and legends, love magic, love charms, love suicides and homicides: evidence of romantic love has now been found in over 200 societies ranging over thousands of years. Around the world men and women pine for love, live for love, kill for love and die for love. Human romantic love, also known as passionate love or "being in love" is regularly regarded as a human universal.

Moreover, love-besotted men and women show all of the basic symptoms of addiction. Foremost, the lover is stiletto-focused on his/her drug of choice: the love object. They think obsessively about "him" or "her" (intrusive thinking), and often compulsively call, write or appear to stay in touch. Paramount to this experience is intense motivation to win their sweetheart, not unlike the substance abuser fixated on his/her drug. Impassioned lovers also distort reality, change their priorities and daily habits to accommodate the beloved, experience personality changes (affect disturbance), and sometimes do inappropriate or risky things to impress this special other. Many are willing to sacrifice, even die for "him" or "her." The lover craves emotional and physical union with their beloved, too (dependence). And like the addict who suffers when they can't get their drug, the lover suffers when apart from the beloved (separation anxiety). Adversity and social barriers even heighten this longing (frustration attraction).

In fact, besotted lovers express all four of the basic traits of addiction: craving; tolerance; withdrawal; and relapse. They feel a "rush" of exhilaration when with their beloved (intoxication). As their tolerance builds, the lover seeks to interact with the beloved more and more (intensification). If the love object breaks off the relationship, the lover experiences signs of drug withdrawal, including protest, crying spells, lethargy, anxiety, insomnia or hypersomnia, loss of appetite or binge eating, irritability and loneliness. Lovers, like addicts, also often go to extremes, sometimes doing degrading or physically dangerous things to win back the beloved. And lovers relapse the way drug addicts do: long after the relationship is over, events, people, places, songs or other external cues associated with their abandoning sweetheart can trigger memories and renewed craving.

Of the many indications that romantic love is an addiction, however, perhaps none is more convincing than the growing data from neuroscience. Using brain scanning (functional magnetic resonance imaging, or fMRI), several scientists have now shown that feelings of intense romantic love engage regions of the brain's "reward system," specifically dopamine pathways associated with energy, focus, motivation, ecstasy, despair and craving--including primary regions associated with substance (and non-substance) addictions. In fact, our group has found activity in the nucleus accumbens—the core brain factory associated with all addictions—in our rejected lovers. Moreover, some of our newest (unpublished) results suggest correlations between activities of the nucleus accumbens and feelings of romantic passion among lovers who are wildly, happily in love.

Nobel laureate Eric Kandel recently said, "Brain studies will ultimately tell us what it is like to be human." Knowing what we now know about the brain, my brain-scanning partner, Lucy Brown, has suggested that romantic love is a natural addiction; and I have maintained that this natural addiction evolved from mammalian antecedents some 4.4 million years ago among our first hominid ancestors, in conjunction with the evolution of (serial, social) monogamy—a hallmark of humankind. Its purpose: to motivate our forebears to focus their mating time and metabolic energy on a single partner at a time, thus initiating the formation of a pair-bond to rear their young (at least through infancy) together as a team.

The sooner we embrace what brain science is telling us—and use this information to upgrade the concept of addiction—the better we will understand ourselves and all the billions of others on this planet who revel in the ecstasy and struggle with the sorrow of this profoundly powerful, natural, often positive addiction: romantic love.

The notion of standard deviation has confused hordes of scientists; it is time to retire it from common use and replace it with the more effective one of mean deviation. Standard deviation, STD, should be left to mathematicians, physicists and mathematical statisticians deriving limit theorems. There is no scientific reason to use it in statistical investigations in the age of the computer, as it does more harm than good—particularly with the growing class of people in social science mechanistically applying statistical tools to scientific problems.

Say someone just asked you to measure the "average daily variations" for the temperature of your town (or for the stock price of a company, or the blood pressure of your uncle) over the past five days. The five changes are: (-23, 7, -3, 20, -1). How do you do it?

Do you take every observation: square it, average the total, then take the square root? Or do you remove the sign and calculate the average? For there are serious differences between the two methods. The first produces an average of 15.7, the second 10.8. The first is technically called the root mean square deviation. The second is the mean absolute deviation, MAD. It corresponds to "real life" much better than the first—and to reality. In fact, whenever people make decisions after being supplied with the standard deviation number, they act as if it were the expected mean deviation.

It is all due to a historical accident: in 1893, the great Karl Pearson introduced the term "standard deviation" for what had been known as "root mean square error". The confusion started then: people thought it meant mean deviation. The idea stuck: every time a newspaper has attempted to clarify the concept of market "volatility", it defined it verbally as mean deviation yet produced the numerical measure of the (higher) standard deviation.

But it is not just journalists who fall for the mistake: I recall seeing official documents from the department of commerce and the Federal Reserve partaking of the conflation, even regulators in statements on market volatility. What is worse, Goldstein and I found that a high number of data scientists (many with PhDs) also get confused in real life.

It all comes from bad terminology for something non-intuitive. By a psychological bias Danny Kahneman calls attribute substitution, some people mistake MAD for STD because the former is easier to come to mind.

1) MAD is more accurate in sample measurements, and less volatile than STD since it is a natural weight whereas standard deviation uses the observation itself as its own weight, imparting large weights to large observations, thus overweighing tail events.

2) We often use STD in equations but really end up reconverting it within the process into MAD (say in finance, for option pricing). In the Gaussian world, STD is about ~1.25 time MAD, that is, the square root of (Pi/2). But we adjust with stochastic volatility where STD is often as high as 1.6 times MAD.

3) Many statistical phenomena and processes have "infinite variance" (sa the popular Pareto 80/20 rule) but have finite, and very well behaved, mean deviations. Whenever the mean exists, MAD exists. The reverse (infinite MAD and finite STD) is never true.

4) Many economists have dismissed "infinite variance" models thinking these meant "infinite mean deviation". Sad, but true. When the great Benoit Mandelbrot proposed his infinite variance models fifty years ago, economists freaked out because of the conflation.

It is sad that such a minor point can lead to so much confusion: our scientific tools are way too far ahead of our casual intuitions, which starts to be a problem with science. So I close with a statement by Sir Ronald A. Fisher: 'The statistician cannot evade the responsibility for understanding the process he applies or recommends.' 

And the probability-related problems with social and biological science do not stop there: it has bigger problems with researchers using statistical notions out of a can without understanding them and babbling "n of 1" or "n large", or "this is anecdotal" (for a large Black Swan style deviation), mistaking anecdotes for information and information for anecdote. It was shown that the majority use regression in their papers in "prestigious" journals without quite knowing what it means, and what claims can—and cannot—be made from it. Because of little check from reality and lack of skin-in-the-game, coupled with a fake layer of sophistication, social scientists can make elementary mistakes with probability yet continue to thrive professionally.

Essentialist thinking is the belief that familiar categories—dogs and cats, space and time, emotions and thoughts—each have an underlying essence that makes them what they are. This belief is a key barrier to scientific understanding and progress. In pre-Darwinian biology, for example, scholars believed each species had an underlying essence or physical type, and variation was considered error. Darwin challenged this essentialist view, observing that a species is a conceptual category containing a population of varied individuals, not erroneous variations on one ideal individual. Even as Darwin's ideas became accepted, essentialism held fast, as biologists declared that genes are the essence of all living things, fully accounting for Darwin's variation. Nowadays we know that gene expression is regulated by the environment, a discovery that—after much debate—prompted a paradigm shift in biology.

In physics, before Einstein, scientists thought of space and time as separate physical quantities. Einstein refuted that distinction, unifying space and time and showing that they are relative to the perceiver. Even so, essentialist thinking is still seen every time an undergraduate asks, "If the universe is expanding, what is it expanding into?"

In my field of psychology, essentialist thought still runs rampant. Plenty of psychologists, for example, define emotions as behaviors (e.g., a rat freezes in fear, or attacks in anger), each triggered automatically by its own circuit, so that the circuit for the behavior (freezing, attacking) is the circuit for the emotion (fear, anger). When other scientists showed that, in fact, rats have varied behaviors in fear-evoking situations—sometimes freezing, but other times running away or even attacking—this inconsistency was "solved" by redefining fear to have multiple types, each with its own essence. This technique of creating ever finer categories, each with its own biological essence, is considered scientific progress, rather than abandoning essentialism as Darwin and Einstein did. Fortunately, other approaches to emotion have arisen that do not require essences. Psychological construction, for example, considers an emotion like fear or anger to be a category with diverse instances just as Darwin did with species.

Essentialism can also be seen in studies that scan the human brain, trying to locate the brain tissue that is dedicated to each emotion. At first, scientists assumed that each emotion could be localized to a specific brain region (e.g., fear occurs in the amygdala), but they found that each region is active for a variety of emotions, more than one would expect by chance. Since then, scientists have been searching for the brain essence of each emotion in dedicated brain networks, and in probabilistic patterns across the brain, always with the assumption that each emotion has an essence to be found, rather than abandoning essentialism.

The fact that different brain regions and networks show increased activity during different emotions is not a problem just for emotion research. They also show increased activation during other mental activities such as cognitions and perceptions, and have been implicated in mental illnesses from depression to schizophrenia to autism. This lack of specificity has led to claims (in news stories, blogs, and popular books) that we have learned nothing from brain imaging experiments. This seeming failure is actually a success. The data are screaming out that essentialism is wrong: individual brain regions, circuits, networks and even neurons are not single-purpose. The data are pointing to a new model of how the brain constructs the mind. Scientists understand data through the lens of their assumptions, however. Until these assumptions change, scientific progress will be limited.

Some topics in psychology have advanced beyond essentialist views. Memory, for example, was once thought to be a single process, and later was split into distinct subtypes like semantic memory and episodic memory. Memories are now considered to be constructed within the brain's functional architecture and not to reside in specific brain tissue. One hopes that other areas of psychology and neuroscience will soon follow suit. For example, cognition and emotion are still considered separate processes in the mind and brain, but there is growing evidence that the brain does not respect this division. This means every psychological theory in which emotions and cognitions battle each other, or in which cognitions regulate emotions, is wrong.

Ridding science of essentialism is easier said than done. Consider the simplicity of this essentialist statement from the past: "Gene X causes cancer." It sounds plausible and takes little effort to understand. Compare this to a more recent explanation: "A given individual in a given situation, who interprets that situation as stressful, may experience a change in his sympathetic nervous system that encourages certain genes to be expressed, making him vulnerable to cancer." The latter explanation is more complicated, but more realistic. Most natural phenomena do not have a single root cause. Sciences that are still steeped in essentialism need a better model of cause and effect, new experimental methods, and new statistical procedures to counter essentialist thinking.

This discussion is more than a bunch of metaphysical musings. Adherence to essentialism has serious, practical impacts on national security, the legal system, treatment of mental illness, the toxic effects of stress on physical illness... the list goes on. Essentialism leads to simplistic "single cause" thinking when the world is a complex place. Research suggests that children are born essentialists (what irony!) and must learn to overcome it. It's time for all scientists to overcome it as well.

Yes, humans do some things that other species do not—we are indeed the only species to send probes to outer space to find other forms of life—but the converse is certainly equally true. Other species do things humans find impossible, and many nonhuman species are indeed unique in their abilities. No human can detect temperature changes of a few hundredths of a degree as can some pit vipers, nor can humans best a dog at following faint scents. Dolphins hear at ranges impossible for humans and, along with bats, can use natural sonar. Bees and many birds see in the ultraviolet, and many birds migrate thousands of miles yearly, under their own power with what seems to be some kind of internal GPS. Humans, of course, can and will invent machines to accomplish such feats of nature, unlike our nonhuman brethren—but nonhumans had these abilities first. Clearly I don't contest data that show that humans are unique in many ways, and I certainly favor studying the similarities and differences across species, but think it is time to retire the notion that human uniqueness is a pinnacle of some sort, denied in any shape, way, or form to other creatures.

Another reason for retiring the idea of humaniqueness as the ideal endpoint of some evolutionary process is, of course, that our criteria for uniqueness inevitably need redefinition. Remember when "man, the tool-user" was our definition? At least until along came species like cactus-spike-using Galapagos finches, sponge-wielding dolphins, and now even crocodiles that use sticks to lure birds to their demise. Then it was "man, the tool-maker"…but that fell out of favor when such behavior was seen in a number of other creatures, including species so evolutionary-distant from humans as New Caledonian crows. Learning through imitation? Almost all songbirds do it to some extent vocally, and minor evidence exists for physical aspects in parrots and apes. I realize that current research does demonstrate that apes, for example, are lacking in certain aspects of collaborative abilities seen in humans, but have to wonder if different experimental protocols might provide different data in the future.

The comparative study of behavior needs to be expanded and supported, but not merely to find more data enshrining humans as "special". Finding out what makes us different from other species is a worthy enterprise, but it can also lead us to find out what is "special" about other beings, what incredible things we may need to learn from them. So, for example, we need more studies to determine the extent to which nonhumans show empathy or exhibit various aspects of 'theory of mind", to learn what is needed for survival in both their natural environment and what they can acquire when enculturated into ours. Maybe they have other means of accomplishing the social networking we take as at least a partial requisite for humanness. We need to find out what aspects of human communication skills they can acquire—but we also can't lose sight of the need to uncover the complexities that exist in their own communication systems.

Note Bene: Lest my point be misunderstood: My argument is a different one from that of bestowing personhood on various nonhuman species, and is separate from other arguments for animal rights and even animal welfare—although I can see the possible implications of what I am proposing.

All told, it seems to me that it is time to continue to study all the complexities of behavior in all species, human and nonhuman, to concentrate on similarities as well as differences, and—in many cases—to appreciate the inspiration that our nonhuman compatriots provide in order to develop tools and skills that enhance our own abilities, rather than simply to consign nonhumans to a second-class status.

For reinforcing a perilous social psychological imperialism toward other behavioral sciences and for suggesting that humans are naturally oriented toward others, the strong interpretation of Aristotle's famous aphorism needs to be retired. Certainly sociality is a dominant force that shapes thought, behavior, physiology, and neural activity. However, enthusiasm over the social brain, social hormones, and social cognition must be tempered with evidence that being social is far from easy, automatic, or infinite. This is because our (social) brains, (social) hormones, and (social) cognition on which social processes rely must first be triggered before they do anything for us.

Of course it seems obvious that our world has three space dimensions, as obvious as that the sun orbits the earth. Physics theories typically predict aspects of the world that we do not see. For example, Maxwell’s theory of electromagnetism correctly predicted that the spectrum of light we see was just a part of the full spectrum, which extended into infrared and ultraviolet waves invisible to us.

String theory predicts our world has more than three space dimensions. Contrary to much that is written and said, as I will explain here, string theory is broadly predictive and testable. Before I explain its testability, I will describe why great progress in making a comprehensive underlying theory of our physical world may emerge from formulating theories in more than 3 space dimensions. I’ll call it a "final theory" following Steven Weinberg.

What could we gain by giving up the idea that our world has three space dimensions (3D)? String theory emerged when John Schwarz and Michael Green noticed in summer 1984 that it was possible to write a mathematically consistent quantum theory of gravity only in 10D (10 space-time dimensions). That’s a big gain and clue. For me and some theorists it’s even more important that string theories address all or nearly all of the issues and questions that need answering in order to have a final theory. There has been major progress here in the past decade. The initial excess optimism of string theorists caused an overcompensation, now tempered by increasingly many results. The highly successful and well tested 4D so-called "Standard Models" of particle physics and of cosmology provide powerful accurate and complete (with the discovery of the Higgs boson) descriptions of the world we see, but do not provide explanations and understanding for a number of issues that are addressed by string theory. The success of the Standard Model(s) is strong evidence that sticking with the 4D world gets in the way of going beyond description to explaining and understanding.

To explain our universe, obviously the higher dimension string theories have to be projected onto a 4D universe, a process with the understandable but unfortunate name "compactification" (for historical reasons). Experiments and observations have to be done in our 4D universe, so only compactified theories can be directly tested. Compactified string theories address why the universe is mainly made of matter and not antimatter, what the dark matter is, why quarks and leptons come in three similar families, what the individual quark and lepton masses are, the existence of the Higgs mechanism and how it gives mass to quarks and leptons and force-carrying bosons, cosmological history from the end of inflation to the origins of nuclei (after which the Standard Model takes over), the cause of inflation, and much more. Compactified string theories successfully predicted (before the measurements) the mass and properties of the Higgs boson discovered at CERN in 2012, and make predictions for the existence of "supersymmetric partner particles" some of which should be produced and detected at the upgraded CERN collider in 2015 if it functions as planned. Examples already exist in compactified string theories for all of these. All of this is research in progress, so much still needs to be worked out and understood better, and tested at colliders and in dark matter and other experiments, but we can already see that all these exciting opportunities exist.

In 1995 Edward Witten argued that there was an 11D theory he called M-theory which could give a consistent quantum theory of gravity, and that it could be projected onto several 10D string theories in different ways. They had names like Heterotic or Type II. Those 10 D theories could then be compactified to 4D theories (with 6 small curled up dimensions), and make testable predictions as described above. M-theory can also be compactified directly onto a 7D curled up (G₂) manifold plus four large space-time dimensions. The study of such theories is ongoing. The compactified theories are testable in the traditional way of testing physics theories for four centuries. In fact, they are testable in the same sense as Newton’s second law, F=ma. F=ma is not testable in general, but only for one force at a time – for a given force and mass object one calculates the predicted acceleration and measures it. Similarly, the form the small extra dimensions take for compactified M/string theories leads to calculable and testable predictions.

A nice example of how the string theories may help comes from the Higgs boson mass. In the Standard Model the Higgs boson mass cannot be predicted at all. The extension of the Standard Model to the theory called the supersymmetric Standard Model predicts an upper limit on the Higgs boson mass, but cannot make an accurate prediction of the mass. Compactified M-theory allows a prediction (made by me with students and colleagues) with an accuracy of a few per cent, in 2011 before the CERN measurements, and confirmed by subsequent data.

If we want to understand and explain our world, going beyond even a full mathematical description, we should take seriously and work on 10D string theories or 11D M-theory, compactifying them to our apparent 4D world. People often say that string theories are complicated. Actually, compactified M/string theories seem to be the simplest theories that could encompass and integrate all the phenomena of the physical world into one coherent mathematical theory.

Parallax describes the apparent change in the direction of a moving object caused by alteration in the observer's position. In the graphic work of M.C. Escher, human faculties are similarly deceived and an impossible reality made plausible.

While not strictly a scientific theorem, anthropocentrism, the assessment of reality through an exclusively human perspective, is deeply embedded in science and culture. Improving knowledge requires abandoning anthropocentricity or, at least, acknowledging its existence.

Anthropocentrism's limits derive from the physical constraints of human cognition and specific psychological attitudes. Being human entails specific faculties, intrinsic attitudes, values and belief systems that shape enquiry and understanding.

The human mind has evolved a specific physical structure and bio-chemistry that shapes thought processes. The human cognitive system determines our reasoning and therefore our knowledge. Language, logic, mathematics, abstract thought, cultural beliefs, history and memories create a specific human frame of reference, which may restrict what we can know or understand.

There may be other forms of life and intelligence. The ocean has revealed creatures that live from chemo-synthesis in ecosystems around deep-sea hydrothermal vents, without access to sunlight. Life forms based on materials other than carbon may also be feasible. An entirely radical set of cognitive frameworks and alternative knowledge cannot be discounted.

Like a train that can only run on tracks that determine direction and destination, human knowledge may ultimately be constrained by what evolution has made us.

Knowledge was originally driven by the need to master the natural environment to meet basic biological needs—survival and genetic propagation. It was also needed to deal with the unknown and forces beyond human control. Superstition, religion, science and other belief systems evolved to meets these human needs.

In the eighteenth century, medieval systems of aristocratic and religious authority were supplanted by a new model of scientific method, rational discourse, personal liberty and individual responsibility. But this did not change the basic underlying drivers.

Knowledge is also influenced by human factors—fear and greed, ambition, submission and tribal collusion, altruism and jealousy, as well as complex power relationships and inter-personal group dynamics. Behavioural science illustrates the inherent biases in human thought.

Announcing a boycott of certain "luxury" scientific journals, 2013 Nobel laureate Dr. Randy Schekman argued that to preserve their pre-eminence they acted like "fashion designers who create limited-edition handbags or suits…know[ing] scarcity stokes demand". He argued that science is being distorted by perverse incentives whereby scientists who publish in important journals with a high "impact factor" can expect promotion, pay rises and professional accolades.

Understanding operates within these biological and attitudinal constraints. As Friedrich Nietzsche wrote: "every philosophy hides a philosophy; every opinion is also a hiding place, every word is a mask".

Understanding of fundamental issues remains limited. The cosmological nature and origins of the universe are contested. The physical source and nature of matter and energy are debated. The origins and evolution of biological life remain unresolved.

Resistance to new ideas frequently restricts the development of knowledge. The history of science is a succession of controversies—a non geo-centric universe, continental drift, theory of evolution, quantum mechanics and climate change.

Science, paradoxically, seems to also have inbuilt limits. Like an inexhaustible Russian doll, quantum physics is an endless succession of seemingly infinitely divisible particles. Werner Heisenberg's uncertainty principle posits that human knowledge about the world is always incomplete, uncertain and highly contingent. Kurt Gödel's incompleteness theorems of mathematical logic establish inherent limitations of all but the most trivial axiomatic systems of arithmetic.

Experimental methodology and testing is flawed. Model predictions are often unsatisfactory. As Nassim Nicholas Taleb observed: "You can disguise charlatanism under the weight of equations … there is no such thing as a controlled experiment."

Challenging anthropocentrism does not mean abandoning science or rational thought. It does not mean reversion to primitive religious dogma, messianic phantasms or obscure mysticism.

Transcending anthropocentricity may allow new frames of reference expanding the boundary of human knowledge. It may allow human beings to think more clearly, consider different perspectives and encourage possibilities outside the normal range of experience and thought. It may also allow a greater understanding of our existential place within nature and in the order of things.

As William Shakespeare's Hamlet cautioned a friend: "There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy".

But fundamental biology may not allow the required change of reference framework.

While periodically humbled by the universe, human beings remain enamoured, for the most part, with the proposition that they are the apogee of development. But as Mark Twain observed in Letters from Earth: "He took a pride in man; man was his finest invention; man was his pet, after the housefly."

Writing in The Hitchhikers' Guide to the Galaxy, the late English author Douglas Adams speculated that the earth was a powerful computer and human beings were its biological components designed by hyper-intelligent pan-dimensional beings to answer the ultimate questions about the universe and life. To date, science has not produced a conclusive refutation of this whimsical proposition.

Whether or not we can go beyond anthropocentrism, it is a reminder of our limits. As Martin Rees, Professor of Cosmology and Astrophysics, at Cambridge and Astronomer Royal, noted:

 "Most educated people are aware that we are the outcome of nearly 4 billion years of Darwinian selection, but many tend to think that humans are somehow the culmination. Our sun, however, is less than halfway through its lifespan. It will not be humans who watch the sun's demise, 6 billion years from now. Any creatures that then exist will be as different from us as we are from bacteria or amoebae."  

Did you know that consuming large amounts of olive oil can reduce your mortality risk by 41 percent? Did you know that if you have cataracts and get them operated on your mortality risk is lowered by 40 percent over the next 15 years compared to people with cataracts who don't get them operated on? Did you know that deafness causes dementia?

Those claims and scores like them appear every day in the media.

They are usually based on studies employing multiple regression analyis (MRA). In MRA a number of independent variables are correlated simultaneously with some dependent variable.

The goal is typically to show that variable A influences variable B "net of" the effects of all the other variables. To put that a little differently, the goal is to show that, at every level of variables C, D and E, an association between A and B is found. For exemple, drinking wine is correlated with low incidence of cardiovascular disease, controlling for (net of) the contributions to cardiovascular disease of social class, excess weight, age, etc., etc.

Epidemiologists, medical researchers, sociologists, psychologists and economists are particularly likely to use this technique, though it can be used in almost any scientific field.

The claims—always at least implicit, often explicit – that MRA can reveal causality are simply mistaken. We know that the target independent variable (consumption of olive oil, for example) brings along its correlations with many other variables—measured in some inevitably imperfect way or not at all. And the level on each of these variables is "self-selected." Any one of these variables could be driving the effects on the dependent variable.

Would you think the number of children in a classroom matters for how well school children learn? It seems reasonable that it would. But a number of MRA studies tell us that, net of average family income of families in the school district, size of the school, IQ test performance, city size, geographic location, etc., average class size is uncorrelated with student performance. The implication: We now know we needn't waste money on decreasing the size of classes.

But researchers have assigned kindergartners through third graders, by the flip of a coin, to either small classes (13 to 17) or larger classes (22-25 per class). The classes with smaller size showed more improvement in standardized test performance; the effect on minority children was greater than the effect on white children. This is not merely another study on the effects of class size. It replaces all the multiple regression studies on class size.

This is the case because it is the experimenter who selects the level on the target independent variable. This means that the experimental classrooms have equally good teachers on average, equally able students, equal social class of students, etc. Thus the only thing that differs between experimental and control classrooms is the independent variable of interest, namely class size.

MRA studies that attempt to "control" for other factors such as social class, age, prior state of health, etc. can't get around the self-selection problem. The sorts of people who get treatment differ from those who don't get it in goodness knows how many ways.

Consider social class. If an investigator wishes to see whether social class is associated with some outcome, anything correlated with social class might be producing or suppressing the effects of class per se. We can be fairly sure that the people consuming all that olive oil are richer, better educated, more knowledgeable about health and more concerned about health (with spouses also more concerned about their health, etc.) They are almost surely less likely to smoke or to drink to excess, and they probably live in less toxic environments than people who use corn oil. They are also more likely to be of Italian descent (Italians are relatively long-lived) than African descent (blacks have generally high mortality rates). All of these variables are candidates for being the true cause of the association between social class and mortality, rather than the consumption of olive oil per se.

Even when there is an attempt to control for all possible variables, they are not necessarily well-measured, which means that their contribution to the target dependent variable will be underestimated. For example, there is no unique correct way to measure social class. Education level, income, wealth, and occupational level are all pieces of the pie and there is no canonical way to weight them to come up the same social-class value that God has in mind.

A New York Times Op-Ed writer, a PhD at Harvard, recently expressed the opinion that MRA studies are superior to experiments because MRA studies based on Big Data can have many more subjects.

The error here is the assumption that having a relatively small number of subjects is likely to mislead. This is mistaken. Larger N is always better than smaller N because we are more likely to detect even small effects. But our confidence in studies is based not on the number of cases but on whether we have unbiased estimates of effects and whether the effects are statistically significant. And in fact, if you have a statistically significant effect with a relatively small number of subjects, this means, other things equal, that your effect is bigger than if it had required a larger number of subjects to reach the same level of significance.

Big data is going to be useful for all kinds of purposes, including generating MRA findings that suggest randomized-design experiments which can provide definitive evidence about whether an apparent effect is real. A lovely example of this kind of sequence results from the 2011 finding in MRA research by Becutti and Pannain that low levels of sleep are associated with obesity. That finding taken by itself is next to meaningless. Bad health outcomes are almost all correlated with each other: overweight people have worse cardiovascular haealth, worse psychological health, use more drugs, get less exercise, etc. But following the MRA research, experimenters have done the requisite experiments. They deprived people of sleep and found that they did in fact gain weight. Not only that, but researchers found hormonal and endocrine consequences of sleep disturbances that mediated the weight gain.

Multiple regression, like all statistical techniques based on correlation, has a severe limitation due to the fact that correlation doesn't prove causation. And no amount of measuring of "control" variables can untangle the web of causality. What nature hath joined together, multiple regression cannot put asunder. 

Among virtually all scientists, dualism is dead. Our thoughts and actions are the outputs of a computer made of meat—our brain—a computer that must obey the laws of physics. Our choices, therefore, must also obey those laws. This puts paid to the traditional idea of dualistic or "libertarian" free will: that our lives comprise a series of decisions in which we could have chosen otherwise. We know now that we can never do otherwise, and we know it in two ways.

The first is from scientific experience, which shows no evidence for a mind separate from the physical brain. This means that "I"—whatever "I" means—may have the illusion of choosing, but my choices are in principle predictable by the laws of physics, excepting any quantum indeterminacy that acts in my neurons. In short, the traditional notion of free will—defined by Anthony Cashmore as "a belief that there is a component to biological behavior that is something more than the unavoidable consequences of the genetic and environmental history of the individual and the possible stochastic laws of nature"—is dead on arrival.

Second, recent experiments support the idea that our "decisions" often precede our consciousness of having made them. Increasingly sophisticated studies using brain scanning show that those scans can often predict the choices one will make several seconds before the subject is conscious of having chosen! Indeed, our feeling of "making a choice" may itself be a post hoc confabulation, perhaps an evolved one.

When pressed, nearly all scientists and most philosophers admit this. Determinism and materialism, they agree, win the day. But they're remarkably quiet about it. Instead of spreading the important scientific message that our behaviors are the deterministic results of a physical process, they'd rather invent new "compatibilist" versions of free will: versions that comport with determinism. "Well, when we order strawberry ice cream we really couldn't have ordered vanilla", they say, "but we still have free will in another sense. And it's the only sense that's important."

Unfortunately, what's "important" differs among philosophers. Some say that what's important is that our complex brain evolved to absorb many inputs and run them through complex programs ("ruminations") before giving an output ("decision"). Others say that what's important is that it's our own brain and nobody else's that makes our decisions, even if those decisions are predetermined. Some even argue that we have free will because most of us choose without duress: nobody holds a gun to our head and says "order the strawberry." But of course that's not true: the guns are the electrical signals in our brain.

In the end, there's nothing "free" about compatibilist free will. It's a semantic game in which choice becomes an illusion: something that isn't what it seems. Whether or not we can "choose" is a matter for science, not philosophy, and science tells us that we're complex marionettes dancing to the strings of our genes and environments. Philosophy, watching the show, says, "pay attention to me, for I've changed the game."

So why does the term "free will" still hang around when science has destroyed its conventional meaning? Some compatibilists, perhaps, are impressed by their feeling that they can choose, and must comport this with science. Others have said explicitly that characterizing "free will" as an illusion will hurt society. If people believe they're puppets, well, then maybe they'll be crippled by nihilism, lacking the will to leave their beds. This attitude reminds me of the (probably apocryphal) statement of the Bishop of Worcester's wife when she heard about Darwin's theory: "My dear, descended from the apes! Let us hope it is not true, but if it is, let us pray it will not become generally known."

What puzzles me is why compatibilists spend so much time trying to harmonize determinism with a historically non-deterministic concept instead of tackling the harder but more important task of selling the public on the scientific notions of materialism, naturalism, and their consequence: the mind is produced by the brain.

These consequences of "incompatibilism" mean a complete rethinking of how we punish and reward people. When we realize that the person who kills because of a mental disorder had precisely as much "choice" as someone who murders from childhood abuse or a bad environment, we'll see that everyone deserves the mitigation now given only to those deemed unable to choose between right and wrong. For if our actions are predetermined, none of us can make that choice. Punishment for crimes will still be needed, of course, to deter others, rehabilitate offenders, and remove criminals from society. But now this can be put on a more scientific footing: what interventions can best help both society and the offender? And we lose the useless idea of justice as retribution.

Accepting incompatibilism also dissolves the notion of moral responsibility. Yes, we are responsible for our actions, but only in the sense that they are committed by an identifiable individual. But if you can't really choose to be good or bad—to punch someone or save a drowning child—what do we mean by moral responsibility? Some may argue that getting rid of that idea also jettisons an important social good. I claim the opposite: by rejecting moral responsibility, we are free to judge actions not by some dictate, divine or otherwise, but by their consequences: what is good or bad for society.

Finally, rejecting free will means rejecting the fundamental tenets of the many religions that depend on freely choosing a god or a savior.

The fears motivating some compatibilists—that a version of free will must be maintained lest society collapse—won't be realized. The illusion of agency is so powerful that even strong incompatibilists like myself will always act as if we had choices, even though we know that we don't. We have no choice in this matter. But we can at least ponder why evolution might have bequeathed us such a powerful illusion. 

The idea that things are either true or false should possibly take a rest.

I'm not a scientist, just a lover of science, so I might be speaking out of turn—but like all lovers I think about my beloved a lot. I want her to be free and productive, and not misunderstood.

For me, the trouble with truth is that not only is the notion of eternal, universal truth highly questionable, but simple, local truths are subject to refinement as well. Up is up and down is down, of course. Except under special circumstances. Is the North Pole up and the South Pole down? Is someone standing at one of the poles right-side up or upside-down? Kind of depends on your perspective.

When I studied how to think in school I was taught that the first rule of logic was that a thing can not both be and not be at the same time and in the same respect. That last note, "in the same respect," says a lot. As soon as you change the frame of reference, you've changed the truthiness of a once immutable fact.   

Death seems pretty definite. The body is just a lump. Life is gone. But if you step back a bit, the body is actually in a transitional phase while it slowly turns into compost—capable of living in another way.  

This is not to say that nothing is true or that everything is possible—just that it might not be so helpful for things to be known as true for all time, without a disclaimer. At the moment, the way it's presented to us, astrology is highly unlikely to be true. But if it turns out that organic stuff once bounced off Mars and hit earth with a dose of life, we might have to revise some statements that planets do not influence our lives here on earth.

I wonder, and this is just a modest proposal, if scientific truth should be identified in a way that acknowledges that it's something we know and understand for now – and in a certain way.

One of the major ways the public comes to mistrust science is when they feel that scientists can't make up their minds. One says red wine is good for you, and another says even in small amounts it can be harmful. In turn, some people think science is just another belief system.

Scientists and science writers make a real effort to deal with this all the time. The phrase, "Current research suggests…" warns us that it's not a fact yet. But, from time to time the full blown factualness of something is declared, even though further work could place it within a new frame of reference. And then the public might wonder if the scientists are just arguing for their pet ideas.

Facts, it seems to me are workable units, useful in a given frame or context. They should be as exact and irrefutable as possible, tested by experiment to the fullest extent. When the frame changes, they don't need to be discarded as untrue, but respected as still useful within their domain.  I think most people who work with facts accept this, but I don't think the public fully gets it.

That's why I hope for more wariness about implying we know something to be true or false for all time and for everywhere in the cosmos.

Especially, if we happen to be upside down when we say it.

Computer Science is a curious sort of science, one that implicitly ignores, and even explicitly opposes, the principles of the rest of science.

There are many models of computation: imperative versus declarative versus functional languages, SISD versus SIMD versus MIMD architectures, scalar versus vector versus multicore processors, RISC versus CISC versus VLIW instruction sets. But there is only one underlying physical reality: a patch of space can contain states, which can interact, and take time to transit. Anything else is a fiction.

Heroic efforts are now going into maintaining that fiction. Programming today is a bit like inhabiting the pleasure gardens in Metropolis, confident that the workers in the machine rooms down below will follow your instructions. Interconnect bottlenecks, cache misses, thread concurrency, data center power budgets, and the inefficiency of parallel processors (and programmers) are rumblings of discontent from below.

Software doesn't have physical units like time and space, but the hardware that executes it does. The code for an application program, the executable code that it's compiled to, and the circuits that run it don't look at all like each other. When a map is zoomed there's also a hierarchical structure from city to state to country, but the geometry of the representation is not changed. Why do we do that for software?

I blame two people for this state of affairs: Alan Turing and John von Neumann. They're famous for what were essentially historically important hacks. Turing was interested in the question of what was computable. His namesake machine was meant to be a theoretical model, not an experimental prescription. It had a head that read and wrote symbols stored on a tape. While that might sound straightforward, it's an unphysical distinction: persistence and interaction are both properties of a physical state. This segregation of function was elaborated in the organs of von Neumann's architecture. Even though that underpins most every computer made today, it was not intended to be a universal truth. Rather, it was articulated in an influential report that von Neumann wrote on programming within the very limited confines of an early computer, the EDVAC.

Turing and von Neumann understood the limits of their models; late in life they both studied computing in spatial structures, pattern formation for Turing and self-replication for von Neumann. But their legacy lives on in the instruction pointer in most any processor, the modern descendant of Turing's head reading a tape. All of the other instructions not pointed to consume information processing resources, but don't process information.

In nature, everything happens everywhere all the time. While an industry has developed devices for computation, a much smaller community has studied the physics of computation. Outside of what is traditionally considered to be computer science, they've developed quantum computers that use entanglement and superposition, microfluidic logic that transports material as well as information, analog logic that solves digital problems with continuous device degrees of freedom, and digital fabrication to code construction of programmable materials. Most importantly, programming models are emerging that represent and respect physical resources, rather than viewing them as a can to be kicked to someone else to worry about. It's turning out that this is easier rather than harder to do, because it avoids all of the issues of converting from an unphysical to a physical world.

In the movie The Matrix, Neo is given a choice between a red pill to exit the fictional world he's been inhabiting, or a blue pill to maintain the illusion. What he found when he got out was much messier, but ultimately much more satisfying. There's a similar choice now before the digital world, between avoiding or embracing the physical reality that it inhabits.

Think of Turing's machine and von Neumann's architecture as technological training wheels. They've given us a good ride, but something of a do-over is now needed to introduce physical units into software in order to be able to program the ultimate universal computer, the universe.

Einstein once said: "The question that most interests me is whether God had any choice in the creation of the universe." By 'God', of course, he didn't mean God. What he was referring to was the question that has driven most scientists who, like me, are attempting to unravel the fundamental laws governing the cosmos at its most basic scale. Namely: Is there only one consistent set of physical laws? If we change one fundamental constant, one force law, would the whole edifice tumble?

Most scientists of my generation, like Einstein before us, implicitly assumed that the answer to these questions was, yes. We wanted to uncover the 'One True Theory', the mathematical formulation that explained why there had to be four forces in nature, why the proton is 2000 times heavier than the electron, etc. Historically in recent memory this effort reached its most audacious level in the 1980's, when string theorists argued that they had found the Theory of Everything—that using the postulates of string theory one would be driven to a unique physical theory, with no wiggle room, that would ultimately explain everything we see at a fundamental level.

Needless to say, that grand notion has had to be put aside for now, as string theory has failed, thus far at least, to live up to such lofty promises. In the process, however, in part driven by string theory's lack of success, we have been driven to the opposite alternative: the laws of nature we measure may be totally accidental, local to our environment (namely our Universe), not prescribed with robustness by any universal principle, and by no means generic or required.

String theory, for example, suggests a host of new possible dimensions and to make contact with our observed four-dimensional universe, it requires the other dimensions to be invisible, either by curling up on such small scales they cannot be probed, or by requiring the known forces and particles to be restricted to live on our 4 dimensional 'brane'. But, it appears that there are many, many different ways to hide the extra dimensions, and each one produces a different four-dimensional universe with different laws. It also suggests that four dimensions themselves need not be universal. Perhaps there are 2 dimensional universes, or six dimensional ones.

One does not have to go to such speculative heights to be driven to the possibility that the laws of our universe may have come into existence when our universe did. The theory of Inflation, which is currently the best theory for how our current universe obtained the characteristics it is measured to have on large scales, suggests that at very early times there was a runaway period of expansion. In different places, and perhaps different times, small regions will stop 'inflating', as a cosmic phase transition occurs in those regions, changing the stable configuration of particles and fields. But in this picture, most of the 'metaverse', if you will, is still inflating, and each region that departs from inflation, each universe, can settle into a different state, with different laws, just as ice crystals on a window can form in different directions.

All of this suggests very strongly that there may be nothing fundamental whatsoever about the 'fundamental' laws we measure in our universe. They could simply be accidental. Physics becomes, in this sense, an environmental science.

Now, many people have picked up on this notion to suggest that somehow we can understand our laws because they are selected anthropically—that is, if they were any different, life wouldn't have developed in our universe. However, this idea is full of problems. Not least because we have no idea what possibilities exist, and whether changing a few, or a huge number of fundamental parameters could result in viable habitable universes. We also have no idea if we are 'typical' lifeforms. Most life that evolves or will in our universe in the future might be quite different.

Focusing on anthropics misses the point in any case. The important fact is that we must be willing to give up the idea that the laws of physics in our universe reflect some underlying fundamental order… that the laws are somehow pre-ordained by principles of beauty or symmetry. There is nothing new about this. It was myopic to assume that life on our planet was pre-ordained. We now understand that accidents of natural selection and environmental traumas governed the history of life that led to our existence. It is equally myopic to assume that we are somehow the pinnacles of evolution—that all roads lead to us, or that we will not lead to something completely different in the future.

It is myopic to assume that the universe we now live in will always be this way. It won't be. As several of us have argued, it seems that in the far future all the galaxies we now see will disappear. But it may be much worse. It is myopic to assume our laws are universal in time and space even in our Universe. Current data on the Higgs particle suggests that the Universe could yet again undergo a cosmic phase transition that would change the stable forces and particles, and we and everything we see might disappear.

We have come to accept the notion that life is not preordained. We need to equally give up the quaint notion that the laws of physics are. Cosmic accidents are everywhere. It is quite possible that our entire universe is just another one. 

When two hypotheses are equally adequate to the data, and equal in predictive power, extra-theoretical criteria for choosing between them might come into play. They include not just questions about best fit with other hypotheses or theories already predicated to enquiry, but the aesthetic qualities of the competing hypotheses themselves—which is more pleasing, more elegant, more beautiful?—and of course the question of which of them is simpler.

Simplicity is a desideratum in science, and the quest for it is a driver in the task of effecting reductions of complex phenomena to their components. It lies behind the assumption that there must be a single force in nature, of which the gravitational, electroweak and strong nuclear forces are merely manifestations; and this assumption in turn is an instance of the general view that there might ultimately be a single kind of thing (or stuff or field or as-yet-undreamt phenomenon) out of which variety springs by means of principles themselves fundamental and simple.

Compelling as the idea of simplicity is, there is no guarantee that nature itself has as much interest in simplicity as those attempting to describe it. If the idea of emergent properties still has purchase, biological entities cannot be fully explained except in terms of them, which means in their full complexity, even though considerations of structure and composition are indispensable.

Two measures of complexity are: the length of the message required to describe a given phenomenon, and the length of the evolutionary history of that phenomenon. On a certain view, that makes a Jackson Pollock painting complex by the first measure, simple by the second; while a smooth pebble on a beach is simple by the first and complex by the second. The simplicity sought in science might be thought to be what is achieved by reducing the length of the descriptive message: encapsulation in an equation, for example. But: could there be an inverse relationship between the degree of simplicity achieved and the degree of approximation that results?

Of course it would be nice if everything in the end turned out to be simple, or could be made amenable to simple description. But some things might be better or more adequately explained in their complexity—biological systems again come to mind. Resisting too dissipative a form of reductionism there might ward off those silly kinds of criticism claiming that science aims to see nothing in the pearl but the disease of the oyster.

In my field of fundamental physics and cosmology the idea most ready for retirement is that the big bang was the first moment of time.

In popular parlance the big bang has two meanings. First, big bang cosmology is the hypothesis that our universe has been expanding for 13.7 billion years from an extremely hot and dense primordial state-more extreme than the centre of a star or indeed anywhere now existing. This I have no quarrel with-it is established scientific fact which has been elaborated into a detailed story which narrates the expansion of the universe from an extremely uniform and dense hot plasma to the beautifully varied and complex world that is our home. We have detailed theories which pass numerous observational tests which explain the origins of all the structures we see from the elements to galaxies, stars, planets and the molecular building blocks of life itself. As in any good scientific theory there are questions still to be answered, such as the precise nature of the dark matter and dark energy which are prominent actors in the story, or the very interesting question of whether there was a very early phase of inflationary exponential expansion, but these do not suggest the basic picture could be wrong.

What concerns me is the other meaning of the big bang, which is the further hypothesis that the ultimate origin of our universe was a first moment of time at which our universe was launched from a state of infinite density and temperature. According to this idea, all that exists or has ever existed is 13.7 billion years old. It makes no sense to ask what was before that because, before that, there was not even time.

The main problem with this second meaning of big bang is that it is not very successful as a scientific hypothesis because it leaves big questions about the universe unanswered. It turns out that our universe has to have started off in an extraordinarily special state for the universe to evolve to anything like our universe. The hypothesis that there was a first moment of time turns out to be remarkably generic and unconstraining as it is consistent with an infinite number of possible states in which the universe might have started out. This is due to a theorem proved by Hawking and Penrose, that almost any expanding universe described by general relativity has such a first moment of time. Compared to almost all of these, our own early universe was extraordinarily homogeneous and symmetric. Why? If the big bang was the first moment of time there can be no scientific answer because there was no before on which to base an explanation. At this point theologians see their opening and indeed have been lining up at the gates of science to impose their kind of explanation-that god made the universe and made it so.

Similarly, if the big bang was the first moment of time there can be no scientific answer to the question of what chose the laws of nature. This leaves the field open to explanations such as the anthropic multiverse which are unscientific because they call on unobservable collections of other universes and make no predictions by which their hypotheses might be tested and falsified.

There is however a chance for science to answer these questions, which is if the big bang was not the first moment of time, but rather a transition from an earlier era of the universe-an era that can be investigated scientifically because processes acting then, through the time before the big bang, gave rise to our world.

For there to have been a time before the big bang, the Hawking-Penrose theorem must fail. But there is a simple reason to think it must, which is that general relativity is incomplete as a description of nature because it leaves out quantum phenomena. Unifying quantum physics with general relativity has been a major challenge for fundamental physics, one on which there has been much progress in the last thirty years. In spite of the absence of a still definitive solution to the problem, there is robust evidence from quantum cosmology models that the infinite singularities that force time to stop in general relativity are eliminated, turning the big bang-in the sense of a first moment of time-into a big bounce, which allows time to continue to exist before the big bang, deep into the past. Detailed models of quantum universes show a prior era ending with a collapse, where the density increases to very high values but, before the universe becomes infinitely dense, quantum processes take over which bounce the collapse into an expansion-launching a new era that could be our expanding universe.

There are presently several scenarios under study for what happened in the era before the big bang and how it transitioned to our expanding universes. Two hypothesize a quantum bounce-and go under the name of loop quantum cosmology and geometrogenesis. Two others-due respectively to Roger Penrose and Paul Steinhardt with Neil Turok, describe cyclic scenarios in which universes die giving rise to new universes. A fifth posits that new universes are launched when quantum effects bounce black hole singularities These scenarios offer insights as to how the laws of nature that govern our universe might have been chosen, and may explain also how the initial conditions of our universe evolved from the previous universe. The important thing is that each of these hypotheses make predictions for real, doable observations by which they might be falsified, and distinguished from each other.

During the Twentieth Century we learned a great deal about the first three minutes of our expanding universe (in Steven Weinberg's phrase). During this century we can look forward to gaining scientific evidence of the last three minutes of the era before ours, and learning how physics before the big bang gave rise to the birth of our universe. 

There are any numbers of scientific theories that ought to bite the dust; that's what happens when you work at the frontiers of human ignorance. But most of them are at worst minor distractions or intellectual detours that barely escape the cloisters of academe. A scientific misconception that truly deserves a bullet in the back of its head would be one that has escaped into the real world to do real damage there. Perhaps the best current example is the notion of nature versus nurture.

It is a beguiling concept: highly intuitive and expressible through an alliterative, almost poetic moniker. Francis Galton, who was the founder of eugenics, a polymath and the cousin of Charles Darwin, coined the term. Unfortunately, like Galton's other monumentally bad idea, "nature versus nurture" creates a corrosive blend of conceptual falsehood and political potency.

The most elementary error that people make in interpreting the effects of genes versus those of the environment is to assume that you can truly separate one from the other. Donald Hebb, the brilliant Canadian neuropsychologist, when asked whether nature or nurture contribute more to human personality, reportedly said, "Which contributes more to the area of a rectangle, its length or its width?"

This was a clever reply, but unfortunately only reinforced the highly misleading idea that genetics and environment are orthogonal concepts, like Newtonian space and time. In fact they're more like Einsteinian spacetime: deeply intertwined and with complex interactions that can give rise to counterintuitive results.

Of course, the experts already know this. They realise, for example, that most children inherit from their parents not only genes but also their environment. Hence studies of separated monozygotic twins (who share most of their genes but not their environments). In addition, the idea of the extended phenotype—in which organisms, driven by their genes, act to modify their environments—has been well understood for over 30 years. And the science of epigenetics, though still very much in progress, has already demonstrated a wide variety of ways in which a gene's effects can be altered by factors other than its nucleotide sequence, and shown that these are determined in large part by the gene's environment (which, of course, consists in part of other genes, both in the same organism and beyond).

Unfortunately most of this is lost on the people, such as journalists and politicians, who seek to shape our society. Almost all of them seem to retain a naive 'Newtonian' view of nature and nurture, and this leads them into all sorts of intellectual fallacies.

A case in point is the brouhaha that accompanied the release in October 2013 of a lengthy screed on education policy written by Dominic Cummings, then advisor to the UK's centre-right education minister. Among other things, he pointed out (correctly) that academic performance is highly heritable. This led many commentators, especially on the left, to equate his statement with the belief that education doesn't matter. In their 'Newtonian' nature-versus-nurture universes, the heritability of a trait is an immutable law that can leave people—worse still, children—as prisoners of their genes.

This is nonsense. Inheritability is not the inverse of mutability, and to say that the heritability of a trait is high is not to say that the environment has no effect because heritability scores are themselves affected by the environment. Take the case of height. In the rich world, the heritability of height is something like 80 per cent. But this is only because our nutrition is universally quite good. In places where malnutrition or starvation are common, environmental factors predominate and the heritability of height is much lower.

Similarly, a high heritability of academic performance is not necessarily a sign that education matters little. On the contrary, it is at least in part a product of modern universal schooling. Indeed, if every child received an identical education then the heritability of academic performance would necessarily rise to 100 per cent (because any differences could only be explained by genes). Looked at in this way, a high heritability of academic performance is not a right-wing belief but rather a left-wing aim. But try explaining that to a newspaper columnist on a deadline or a politician with an axe to grind. Ironically, a central thrust of Cummings' paper was to argue that the British education system has produced an inept political elite and commentariat that is oblivious to such technical subtleties. In criticising his comments they have merely proved him right.

Thus the misguided concept of 'nature versus nurture' causes apparently intelligent people to confuse egalitarianism for fascism, to misunderstand the consequences of their own policies, and hence to arrive at unfounded beliefs regarding the education of our children. The only form of evolutionary manipulation that makes sense here is a concerted effort to eliminate this outdated and misleading idea from the meme pool. 

We were taught that the fertilized egg divides to ultimately yield a human being---recently estimated to have ~37 trillion cells—each with the same, authentic copy of one's genome. Unfortunately that simple, seemingly immutable archetype just got mutated.

While there started to be questioning of the classical teaching—one genome per individual—decades ago, it was only recently through our newfound capability of performing single cell sequencing and high-resolution array genomic hybridization that this was unequivocally debunked. For example, in 2012 it was reported that the brain cells from 59 women had Y-chromosomes in 63% of them. Many found that hard to accept. But recently researchers at the Salk Institute did single cell sequencing of post-mortem human brain neurons and found that a striking proportion of the cells (ranging up to 41%) had structural DNA variants. This level of so-called mosaicism in the brain was far greater than anticipated and brought up the question as to whether our single cell sequencing technology might have some flaws that account for the observation. That doesn't appear to be the case, however, as too many independent studies have come up with a similar finding, whether it is in the brain or other organs, such as skin, blood or the heart. This year, a group at Yale found that a high fraction of kids with congenital heart disease carried mutations not present in either parent, perhaps accounting for 10% of severe heart disease birth defects.

These spontaneous "de novo" mutations of cells in the course of one's life are a curve ball for geneticists who thought that heritability was a generation passed down story. More reports of sporadic disease keep popping up, attributable to these de novo mutations, such as amyotrophic lateral sclerosis (Lou Gehrig's disease), autism, and schizophrenia. The mutations can occur at many time points along the human lifespan. A sample of 14 aborted human embryos in development showed that 70% had major structural variations, even though this would not be representative of live births. At the other end of the time continuum, in 6 people who died, unrelated to cancer, there was extensive mosaicism across all organs assessed, including liver, small intestine and pancreas.

But we still don't know if this is merely of academic interest or has important disease-inducing impact. For sure the mosaicism that occurs later in life, in "terminally differentiated" cells, is known to be important in the development of cancer. And the mosaicism of immune cells, particularly lymphocytes, appears to be part of a healthy, competent immune system. Beyond this, it largely remains unclear as to the functional significance of each of us carrying multiple genomes.

The implications are potentially big. When we do use a blood sample to evaluate a person's genome, we have no clue about the potential mosaicism that exists throughout the individual's body. So a lot more work needs to be done to sort this out, and now that we have the technology to do it, we'll undoubtedly better understand our remarkable heterogeneous genomic selves in the years ahead.

Its commonplace, in both scientific and popular writing to talk about innate human traits, "hard-wired" behaviors or "genes for" everything from alcoholism to intelligence. Sometimes these traits are supposed to be general features of human cognition—sometimes they are supposed to be individual features of particular people. The nature/nurture distinction continues to dominate thinking about development. But its time for innateness to go.

Of course, for a long time, people have pointed out that nature and nurture must interact for a particular trait to develop. But several recent scientific developments challenge the idea of innate traits in a deeper way. It isn't just that it's a little of both, some mix of nurture and nature, but that the distinction itself is fundamentally misconceived.

One development is the very important new work exploring what are called epigenetic accounts of development, and the new empirical evidence for those epigenetic processes. These studies show the many complex ways that gene expression, which is what ultimately leads to traits, is itself governed by the environment.

Take the maternal mice. Meaney and colleagues took two different but genetically identical strains of mice which normally develop different degrees of intelligence and cross-fostered them—the smart mice mothers raised the dumb mice pups. The result was that the dumb mice developed problem-solving abilities similar to those of the smart ones and this was even passed on to the next generation. So were the mice innately dumb or innately smart? The very question doesn't make sense.

Here's a similar human example. There is increasing evidence for an early temperament difference between "orchids" and "dandelions". Children with some genetic and physiological profiles appear to be more influenced by the environment, both for good and bad. For example, a recent study looked at the level of Respiratory Sinus Arrhythmia, basically the relation between heartrate and breathing, in at-risk poor children. They discovered that children with high RSA who had secure relationships with their parents had fewer behavior problems later than low RSA children. But the relationship was reversed for the children who had difficult relationships—they actually had more problems. So were the children innately more or less difficult or troubled?

The increasingly influential Bayesian models of human learning, models that have come to dominate recent accounts of human cognition, also challenge the idea of innateness in a different way. At least since Chomsky, there have been debates about whether we have innate knowledge. The Bayesian picture characterizes knowledge in terms of a set of potential hypotheses about the world. We initially believe that some hypotheses are less probable and others are more so. As we collect new evidence we can rationally update the probability of these hypotheses. We can discard what initially looked very likely and eventually accepting ideas that started out as longshots.

If this picture is right there is some sense in which everything we will ever think is potentially there from the start. But it is also true that everything we think is subject to revision and change with increasing evidence. From this probabilistic perspective it also isn't at all clear what it would mean to talk about whether knowledge is innate or learned. You might say instead that some hypotheses initially have a very low or very high probability of being confirmed by further evidence. But the hypotheses and evidence are inextricably intertwined.

The third development is increasing evidence for a new picture of the evolution of human cognition. The old "Swiss Army Knife" picture of capital E capital P "Evolutionary Psychology" with the evolution of myriad different constrained "modules" looks increasingly implausible. Instead, the more recent and more biologically plausible picture is that the developments involved more general developmental changes. These included an increase in the Bayesian learning abilities I just described, increased cultural transmission, wider parental investment, longer developmental trajectories, and greater capacities for counterfactual thinking. All this led to feedback loops that rapidly transformed human behavior.

The evolutionary theorist Eva Jablonka has described the evolution of human cognition as more like the evolution of a hand—a multipurpose flexible tool capable of performing unprecedented behaviors and solving unprecedented problems—than like the construction of a Swiss Army Knife.

In particular, a number of theorists have argued that the difference between the early emergence of "anatomically modern" humans and the much later emergence of "behaviorally modern" ones is due to these feedback loops rather than to some genetic change.

For example, small changes in the capacity for cultural learning and the period of protected childhood in which that learning can take place, could initially lead to small changes in behavior. But the "cultural ratchet" effect could lead to the rapid and accelerating transformation of behavior over generations, especially as there was more and more interaction within groups of early humans.

Combining cultural transmission with Bayesian learning means that each generation of children can integrate the cumulative information of early generations. As a result they can imagine alternative ways that the social and physical environment might be structured and can implement those changes. But this means that each successive generation of children will also grow up shaped by a new social and physical environment, unlike the ones that have gone before, and that in turn will lead them to make new discoveries, reshape the environment again and so on, in an accelerating process of cognitive and behavioral transformation.

All three of these scientific developments suggest that almost everything we do is not just the result of the interaction of nature and nurture, it is both simultaneously. Nurture is our nature and learning and culture are our most important and distinctive evolutionary inheritance.

Since the 1930s when Benjamin Lee Whorf was mesmerizing audiences with the idea that the Hopi people's language channeled them into a cyclical sense of time, the media and university classrooms have been often abuzz with the idea that the way your language works gives you a particular worldview.

You just want this to be true, but it isn't—at least in a way that anyone would be interested in it outside of a psychology laboratory (or academic journal). It's high time thinking people let go of the idea, ever heralded as a possibility but never actually demonstrated, that different languages represent different ways of experiencing life.

Different cultures represent different ways of experiencing life, to be sure. And part of a culture is having words and expressions to express it, to be sure. Cell phone. Inshallah. Feng shui. But this isn't what Whorfianism, as it is often called, is on to. The idea is that quiet things in a language's very structural architecture—how its grammar works, how its vocabulary happens to cut up space—channel how the speaker experiences life.

And in fact, psychologists have indeed shown that such things do influence thought—in tiny ways elicitable via fascinatingly peculiar experiments. So, Russian has different words for dark and light blue and no one word that just means blue, and it has been shown that Russians are, indeed, 124 milliseconds faster at identifying grades of dark blue to other ones and grades of light blue to other ones. Or, it has been shown that people whose languages divide nouns into masculine and feminine categories are more likely, if asked, to imagine those things talking in the appropriately sexed voice if they were cartoon characters, or to associate them with gendered traits.

This kind of thing is neat—but the question is whether the quiet background flutterings of awareness they document can be treated as a worldview. The temptation is endless to suppose that it does. Plus we are always reminded that no one has said that language prevents a speaker from thinking anything, but rather that it makes it more likely that the speaker will.

But we still run up against the fact that languages tell us what we don't want to hear as much as they tell us what is cool, such as Russian blues and tables talking like ladies.

Example—in Mandarin Chinese, the same sentence can mean If you see my sister, you know she's pregnant, If you saw my sister, you'd know she's pregnant, and If you had seen my sister, you'd have known she was pregnant. That is, Chinese leaves hypotheticality to context much more than English does. In the early eighties, psychologist Alfred Bloom, following the Whorfian line, did an experiment suggesting that Chinese makes its speakers somewhat less adept at processing hypothetical scenarios than English speakers.

Whoops—nobody wanted to hear that. There was long train of rebuttals, ending in an exhausted draw. But there are all kinds of experiments one could do that would lead to the same kind of place. Lots of languages in New Guinea have only one word for eating, drinking, and smoking. Does that make them slightly less sensitive to the culinary than other people? Or, Swedish doesn't have a word for wipe—you have to erase, take off, etc. But who's ready to tell the Swedes they don't wipe?

In cases like this our natural inclination is to say that such things are just accidents, and that whatever wisp of thought difference an experimenter could elicit on their basis hardly has anything to do with what the language's speakers are like—or what their worldview is. But then, we have to admit the same thing about the wisps that happen to tickle our fancies.

What creates a worldview is culture—i.e., a worldview. And no, it won't work to say that culture and language create a worldview together holistically. Remember, that would mean that Chinese speakers are—holistically—a little dim when it comes to thinking beyond reality.

Who wants to go there? Especially when even starting to, decade after decade, leads us down blind alleys? Hopi, it turned out, has plenty of markers of good old-fashioned European-style time. Or, Yale economist Keith Chen's recent idea that not having a future tense makes a language's speaker more thrifty—pause to wrap your head around that: it's not having a future that makes you save money!—has intrigued the media for years now. But if four Slavic languages like Russian and Polish all do not have a future tense, and yet savings rates among their countries are vastly different, then the whole idea is out the window.

The idea that language is a lens on life should be treated as what it is—something that pans out in terms of quiet results in intense psychological studies but has nothing to do with any humanistic perspective on what it means to be a human being. An awkward aspect of this is that people engaged in trying to document or save the hundreds of languages worldwide that are threatened with extinction tend to say the languages must survive because they represent ways of looking at the world. But if they don't, we have to formulate new justifications for those rescue efforts. Hopefully, linguists and anthropologists can embrace saving languages simply because they are, in so many ways, magnificent in their own right?

What it comes down to is this. Let's ask how English makes a worldview. Our answer requires that the worldview be one shared by Betty White, William McKinley, Amy Winehouse, Jerry Seinfeld, Kanye West, Elizabeth Cady Stanton, Gary Coleman, Virginia Woolf and and Bono.

Let's face it, what worldview would that be? Sure, a lab test could likely tease out some infinitesimal squeak of a perceptive predilection shared by all of those people. But none of us would even begin to think of it as a way of perceiving the world or reflecting a culture. Or, if anyone would, then we are on to an entirely new academic paradigm indeed.

I grew up among physicists, whose modus operandi is to observe the world, experiment with it, develop hypotheses and theories and models, suggest further experiments, and use statistics to analyze the results, thereby comparing mental imaginings with actual events. Statistics is simply their tool for confirmation or denial. 

But nowadays the world, and especially the world of the social sciences, is increasingly in love with statistics and data science as a source of knowledge and truth itself. Some people have even claimed that computer-aided statistical analysis of patterns will replace our traditional methods of discovering the truth, not only in the social sciences and medicine, but in the natural sciences too.

I believe we must be careful not to get too enamored of statistics and data science and thereby abandon the classical methods of discovering the great truths about nature (and man is nature too). A good example of the classical power is Kepler's 17th Century discovery of his second law of planetary motion, which is in fact less a law than the recognition and description of a pattern. Kepler's second law states that the line between the sun and a moving planet sweeps out equal areas in equal times. This deep symmetry of planetary motion implies that the closer a planet is to the sun, the more rapidly it moves along its orbit. But notice that there is no line between a planet and the sun. Kepler's still astonishing insight required examining Tycho Brahe's data, a long mental struggle, a burst of intuition—use an invisible line!—and then checking his hypothesis. Data, intuition, hypothesis, and finally comparison with data is the time-honored process.

Kepler's second law is in fact a statement of the conservation of angular momentum that followed later from Newton's theories of motion and gravitation. Newton's theories were so readily and immediately accepted because Kepler's three verified laws could be derived from them. John Maynard Keynes wrote of Newton three hundred years later: "I fancy his pre-eminence is due to his muscles of intuition being the strongest and most enduring with which a man has ever been gifted."

Statistics—the field itself—is a kind of Caliban, sired somewhere on an island in the region between mathematics and the natural sciences. It is neither purely a language nor purely a science of the natural world, but rather a collection of techniques to be applied, I believe, to test hypotheses. Statistics in isolation can seek only to find past tendencies and correlations, and assume that they will persist. But in a famous unattributed phrase, correlation is not causation.

Science is a battle to find causes and explanations amidst the confusion of data. Let us not get too enamored of data science, whose great triumphs so far are mainly in advertising and persuasion. Data alone has no voice. There is no "raw" data, as Kepler's saga shows. Choosing what data to collect and how to think about it takes insight into the invisible; making good sense of the data collected requires the classic conservative methods: intuition, modeling, theorizing, and then, finally, statistics.

The history of science is much more variegated than assumed in the Edge Question about the abandonment and burial of old ideas. While the view that new ideas triumph by replacing old ones fits some scientific developments, in many other cases new ideas take over a vacuum formerly occupied by no well-articulated idea at all. That happens for either of two reasons: new ideas responding to new information made possible by new measurements, or else responding to new "outlooks." (Among historians of science, the term used rather than the inadequate English term "outlook" is the German word Fragestellung –literally, the posing of a question, but more broadly meaning a world view from which that question can arise). I'll give two or three examples illustrating each of those two reasons.

The most familiar modern example of a new idea made possible by new measurements is Watson's and Crick's double helix model of DNA's structure. Their model didn't replace a previous established model whose opponents gradually died out without abandoning their error. Instead, the model was made possible by two recent sets of measurements: analyses of the chemical composition of DNA (revealing equivalent amounts of the bases adenine and thymine, and of cytosine and guanine); and X-ray crystallographic evidence. As is well known, two models of DNA structure were then proposed nearly simultaneously, by Pauling and by Watson and Crick. It almost immediately became obvious that the former model was wrong, and that the latter model did account for all of the evidence. Hence the Watson and Crick model became rapidly accepted, replacing a vacuum rather than a previous wrong theory.

My other example of an idea made possible by new measurements concerns the origins of animal electricity. Our nerve and muscle membranes operate by conducting electrical impulses, arising from a change in transmembrane voltage between active and inactive membrane regions. In the absence of direct measurements of transmembrane voltage, it was impossible to propose a quantitative theory for how that voltage could change. That problem was solved between 1939 and 1952 by two developments: the anatomist J.Z. Young discovered giant nerves in squid, and physiologists developed microelectrodes small enough to insert into squid giant nerves without damaging them. Between 1945 and 1952 the physiologists Alan Hodgkin and Andrew Huxley took advantage of that anatomical discovery and that technical development to measure the electric currents moving across squid nerve as a combined function of voltage and time, and thereby to reconstruct quantitatively and in detail how a nerve impulse arises from changes in nerve membrane permeability to the positively charged ions sodium and potassium. The Hodgkin-Huxley theory was rapidly accepted because it was so convincingly correct, and because it had no serious competitors. When I was a physiology student in the 1950's and 1960's, the only resistance to the theory that I recall involved some concern by non-physiologists about whether microelectrodes were causing damage to nerve membranes (a concern answered by several types of control experiments), and a non-quantitative proposal that nerve membranes and synapse or junction membranes undergo the same permeability changes (it turned out that they don't).

As for new ideas made possible by a new Fragestellung, consider first the foundation of the several modern sciences constituting population biology: i.e., taxonomy/systematics, evolutionary biology, biogeography, ecology, animal behavior, and genetics. At least until recently, most research in all of those fields except genetics involved observations, counting, and measurements requiring no equipment. Most of that research could have been done by Aristotle, Herodotus, and their contemporaries in classical-age Greece over 2000 years earlier. The Greeks were eminently capable of patient, accurate, quantitative observations of planets and other features of the natural world. Aristotle could similarly have examined Greek animals and plants and arrived at Linnaeus's hierarchical taxonomy; Herodotus could have compared the species of the Black Sea with those of Egypt and thereby founded biogeography; and any ancient Greek could have grown and counted pea varieties as did Gregor Mendel in the 1860's, noticed the differences between Willow Warblers and Chiffchaffs (a related warbler species) as did Gilbert White in 1789, watched young geese as did Konrad Lorenz after 1935, and thereby founded genetics, ecology, and animal behavior. But ancient Greeks lacked the necessary Fragestellung that lent interest to counting pea varieties and scrutinizing warblers and young geese. The rise of those branches of population biology from the 1700's onwards was due to a modern Fragestellung that generated data (without the need for invention of microelectrodes or X-ray crystallography), which in turn generated ideas, in areas where previously there had been neither data nor detailed ideas.

Without going into specifics, I'll mention two other examples of important broad fields that arose only in recent centuries without any need for specialized technology, and that the ancients could have developed but didn't because they lacked a relevant Fragestellung. The Greeks and Romans were in contact with speakers of Indo-European and Semitic and other languages, could have discovered the groupings of languages in those language families, and could thereby have developed the ideas of historical linguistics—but they didn't even bother to record words of their Egyptian, Gaulish, and other subjects. In all of classical Greek and Roman literature I am not aware of a single wordlist recorded for any "barbarian" language, in contrast to the wordlists that European travelers began routinely to gather among non-European peoples from the 1600's onwards. The Greeks and Romans could equally well have noticed the observational evidence used by Freud to explore the unconscious within us—but they didn't.

All of this is not to say that the view underlying this year's Edge Question is always wrong. Examples in the fields in which I work myself include: the replacement of biogeographic theories assuming a static Earth by the acceptance of continental drift, from the 1960's onwards; the rise of the taxonomic approach turned cladistics at the expense of previous taxonomic approaches, also from the 1960's onwards; and the rise in the 1960's and 1970's, followed by the virtual disappearance, of attempts to make use of irreversible (non-equilibrium) thermodynamics in the fields of population biology and of cell physiology. Instead, my main point is that the development of science follows much more diverse courses than only or predominantly the course of abandoning old ideas.

We will continue to use geometry as a useful branch of mathematics, but is time to abandon the longstanding idea of geometry as the description of physical space. The idea that geometry is the description of physical space is engrained in us, and might sound hard to get rid of it, but it is unavoidable; it is just a matter of time. Better get rid of it soon.

Geometry developed at first as a description of the properties of parcels of agricultural land. In the hands of ancient Greeks it became a powerful tool for dealing with abstract triangles, lines, circles, and similar, and was applied to describe paths of light and movements of celestial bodies with very great efficacy. In the modern age, with Newton, it became the mathematics of physical space. This geometrization of physical space appeared to be further vindicated by Einstein, who described space (actually, spacetime) in terms of the curved geometry of Riemann. But in fact this was the beginning of the end. Einstein discovered that the Newtonian space described by geometry is in fact a field like the electromagnetic field, and fields are nicely continuous and smooth only if measured at large scales. In reality, they are quantum entities that are discrete and fluctuating. Therefore the physical space in which we are immersed is in reality a quantum dynamical entity, which shares very little with what we call "geometry". It is a pullulating process of finite interacting quanta. We can still use expressions like "quantum geometry" to describe it, but reality is that a quantum geometry is not much of a geometry anymore.

Better getting soon rid of the idea that our spacial intuition is always reliable. The world is far more complicated (and beautiful) than a "geometrical space" and things moving in it. 

Any first-hand experience of how scientific institutions actually operate drives home an excruciating realization: Science progresses more slowly by orders of magnitude than it could or should. Our species could have science at the speed of thought—science at the speed of inference. But too often we run into Planck's demographic limit on the speed of science—funeral by funeral, with each tock of advancement clocked to the half-century tick of gatekeepers' professional lifespans.

In contrast, the natural clock rate of science at the speed of thought is the flash rate at which individual minds, voluntarily woven into mutually invigorating communities by intense curiosity, can draw and share sequences of strong inferences from data. Indeed, Planck was a giddy optimist, because scientists—like other humans—form coalitional group identities where adherence to group-celebrating beliefs (e.g., we have it basically right) are strongly moralized.

So, the choice is frequently between being "moral" or thinking clearly. Because the bearers of reigning orthodoxies educate and self-select their next generation replacements, mistakes not only propagate down generations, but can grow to Grand Canyon sizes. When this happens, data sets become embedded so deeply into a matrix of mistaken interpretations (as in the human sciences) that they can no longer be seen independently of their obscuring frameworks. So the sociological speed of science can end up being slower even than Planck's glacial demographic speed. 

Worst of all, the flow of discoveries and better theories through institutional choke points is clogged by ideas that are so muddled that they are—in Paul Dirac's telling phrase—not even wrong. Two of the worst offenders are learning, and its partner in crime, culture, a pair of deeply established, infectiously misleading, yet (seemingly) self-evidently true theories.

What alternative to them could there be except an easily falsified, robotic genetic determinism?

Yet countless obviously true scientific beliefs have had to be discarded—a stationary earth, (absolute) space, the solidity of objects, no action at a distance, etc. Like these others, learning and culture seem so compelling because they map closely to automatic, built-in features of how our minds evolved to interpret the world (e.g., learning is a built-in concept in the theory of mind system). But learning and culture are not scientific explanations for anything. Instead, they are phenomena that themselves require explanation.

All "learning" operationally means is that something about the organism's interaction with the environment caused a change in the information states of the brain, by mechanisms unexplained. All "culture" means is that some information states in one person's brain somehow cause, by mechanisms unexplained, "similar" information states to be reconstructed in another's brain. The assumption is that because supposed instances of "culture" (or equally, "learning") are referred to with the same name, they are the same kind of thing. Instead, each masks an enormous array of thoroughly dissimilar things. Attempting to construct a science built around culture (or learning) as a unitary concept is as misguided as attempting to develop a robust science of white things (egg shells, clouds, O-type stars, Pat Boone, human scleras, bones, first generation MacBooks, dandelion sap, lilies…). 

Consider buildings and the things that allow them to influence each other: roads, power lines, water lines, sewage lines, mail, roads, phone landlines, sound, wireless phone service, cable, insect vectors, cats, rodents, termites, dog to dog barking, fire spread, odors, line of sight communication with neighbors, cars and delivery trucks, trash service, door to door salesmen, heating oil delivery, and so on. A science whose core concept was building-to-building influence ("building-culture") would be largely gibberish, just as our "science" of culture as person to person influence has turned out to be.

Culture is the functional equivalent of protoplasm, the supposed (and "observed") substance that by mechanisms unknown carried out vital processes. Now we recognize that protoplasm was magician's misdirection—a black box placeholder for ignorance, eclipsing the bilipid layers, ribosomes, Golgi bodies, proteasomes, mitochondria, centrosomes, cilia, vesicles, spliceosomes, vacuoles, microtubules, lamellipodia, cisternae, etc. that were actually carrying out cellular processes.

Like protoplasm, culture and learning are black boxes, imputed with impossible properties, and masquerading as explanations. They need to be replaced with maps of the diverse cognitive and motivational "organelles" (neural programs) that actually do the work now attributed to learning and culture. They are the La Brea tar pits of the social and behavioral science.  After a century of wrong turns, our scientific vehicles continue to sink ever deeper into these tar pits, and yet we celebrate because these conceptual tars have poured in to fill all explanatory gaps in the human sciences. They unfalsifiably "solve" all apparent problems by stickily obscuring the actual causal specificity that in each case needs to be discovered and mapped. 

We over-attribute our mental content to culture, because the sole supposed alternative is genes. Instead, evolved, self-extracting AI-like expert systems, in interaction with environmental inputs, neurally develop to populate our minds with immense, subtle bodies of content, only some of which are sourced from others. Rather than humans as passive receptacles haplessly filled by "culture", these self-extracting systems make humans active agents robustly building their worlds. Some neural programs, in order to better carry out their particular functions, evolved to supplement their own self-generated content with low-cost, useful information drawn from others ("culture").

But like buildings, humans are linked with many causally distinct pathways built to perform distinct functions. Each brain is bristling with many independent "tubes" that propagate many distinct kinds of stuff to and from a diversity of brain mechanisms in others. So there is fear-of-snakes culture (living "inside" the snake phobia system), grammar culture (living "inside" the language acquisition device), food-preference culture, group identity culture, disgust culture, sharing culture, aggression culture, etc.

Radically different kinds of "culture" live inside distinct computational habitats—that is, habitats built out of different evolved mental programs, and their combinations. What really ties humans together is an encompassing meta-culture—our species' universal cognitive and emotion programs, and the implicit (and hence invisible) universally shared world of meaning they give rise to. Because the adaptive logics of these evolved neural programs can now be mapped, the prospect of a rigorous natural science of humans is open to us. If we could pension off learning and culture, that would remove two obstacles to the human sciences advancing at the speed of thought. 

Impressed by the growing explanatory power of the natural sciences of his time, the philosopher David Hume called upon his colleagues to abandon the armchair, turn their attention to empirical evidence, and "hearken to no arguments but those which are derived from experience… [to] reject every system of ethics, however subtle or ingenious, which is not founded on fact and observation." This was over two hundred years ago, and unfortunately not much changed in academic philosophy until about the last decade or two. Pushing past a barrier also associated with Hume—the infamous is-ought or fact-value distinction—a growing number of philosophers have finally begun arguing that our theories should be informed by our best current empirical accounts of how the human minds works, and that an ethical system that posits or requires an impossible psychology should be treated with suspicion.

One of the more robust and relevant bits of knowledge about human psychology that has emerged from the cognitive sciences is that we are not rational minds housed in irrational, emotional bodies. Metaphors like that of Plato's rational charioteer bravely struggling to control his irrational, passionate horses appeal to us because they map well onto our intuitive psychology, but they turn out to be ultimately misleading. A more empirically accurate image would be that of a centaur: rider and horse are one. To the best of our knowledge, there is no ghost in the machine. We are thoroughly embodied creatures, embedded in a complex social and culturally-shaped environment, primarily guided in our daily lives not by cold calculation but hot emotion; not conscious choice but automatic, spontaneous processes; not rational concepts descended from the realm of Forms but rather modal, analogical images.

So, the ironic result of adopting a scientific stance toward human morality is to lay bare the impossibility of a purely scientific morality. The thoroughly rational, evidence-guided utilitarian is as much of a myth as the elusive Homo economicus, and equally as worthy of our disdain. Evolution may be utilitarian, guided solely by considerations of costs and benefits, but the ruthlessly utilitarian process of bio-cultural evolution has produced organisms that are, at a proximate level, incapable of functioning in a completely utilitarian fashion, and for very good design reasons. Because of rational evolutionary considerations, we cannot help but react irrationally to unfair offers in the Ultimatum Game, challenges to our honor, or perceived threat to our loved ones or cherished ideals. We are culturally-infused animals guided largely by automatic habits, barely conscious hunches, profoundly motivating emotions, and wholehearted commitment to spooky, non-empirical entities ranging from human rights to the Word of God to the coming proletarian Utopia.

Science, of course, is so powerful and important because it represents a set of institutional practices and thinking tools that allow us to, qua scientists or intellectuals, bootstrap ourselves out of our immediate perceptions and proximate psychology. We can understand that the earth goes around the sun, that wonderful design can be the product of a blind watchmaker, or that the human mind is, in an important sense, reducible to biological processes. This gives us some helpful leverage over our evolved psychology, and I join many in thinking that this more accurate knowledge about ourselves and our world might allow us to devise—and maybe come to embrace—novel ethical commitments that could lead to more satisfying lives and a more just world. But let's not lose sight of the fact that science cannot bootstrap us out of our evolved minds themselves. The desire to bring about a more equitable, fair and peaceful world is itself an emotion, an ultimately irrational drive grounded in commitment to ideals like human dignity, freedom and well-being that we've inherited—in stripped-down, theologically minimalistic form—from the cultural-religious traditions into which we've been born. In their latest, liberal iterations, these ideals are rather odd—very few cultures have embraced diversity and tolerance as ethical desiderata, for instance—and are far from being universally embraced even in our contemporary world.

So, the myth that we secular liberals have emerged into a neutral place where we stand freed of all belief and superstition, guided solely by rationality, evidence and clearly-perceived self-interest, is something that needs to be retired. It is simply not the case that secular liberalism, grounded in materialist utilitarianism, is the inevitable and default worldview of anyone who is not stupid, brainwashed or uneducated, and thinking so seriously impedes our ability to understand people in earlier historical periods, from other cultures, and even ourselves.

Acknowledging this does not entail wallowing in postmodern relativism or blindly marching to a fundamentalist beat. Scientific inquiry, in its broad sense, is so wildly effective at giving us reliable information about the world that to seriously defend any other method of inquiry as superior—or even equal—is simply perverse. There is also arguably a pragmatic case to be made that secular liberalism is the best worldview that humans have ever come up with, or at least that individuals given a choice tend to preferentially gravitate toward it. In any case, it is our value system, and the very nature of evolved human psychology makes it impossible for us not to want to defend human dignity or women's rights and, when appropriate, impose them on others. But recognizing the limitations of reason allows us to articulate and defend such values in a more effective way. It also allows us to better understand, scientifically, problems such as the causes of religious violence, the roots of persistent international conflicts, or moral challenges such as balancing our folk intuitions about personal responsibility with a neuroscientific understanding of free will. The science of morality requires us to, in the end, get beyond the myth of a perfectly objective scientific morality.

The distinction between Mind and Matter is embedded in everyday language and thinking, and even more deeply in philosophy and theology The great philosopher/theologian George Berkeley, who famously grounded Matter in the Mind of God, summed it up in a witticism:

What is mind? No matter.
What is matter? Never mind.

Science has long found it useful to accept this duality, as a methodology if not as a doctrine. In modern physics, matter obeys its own mathematical laws, independent of what anyone—even, or maybe especially, God—thinks.

But the distinction is doomed, and its passing will change our view of everything—everything, that is, which is mind/matter.

Already the walls of separation are crumbling. Three developments have irreversibly undermined them:

• We have learned what matter is. And our new matter, informed over the course of the twentieth century by the revelations of relativity, quantum mechanics, and transformational symmetry, is far stranger and richer in potential than anything our ancestors could have dreamed of. It can dance in intricate, dynamic patterns; it can exploit environmental resources, to self-organize and export entropy.
   
• We have learned, theoretically through Turing's vision, and practically through the rise of ubiquitous computing, that many accomplishments once viewed as prerogatives of Mind—from playing chess, to planning itineraries, to suggesting friends and sharing interests—are things that machines (whose design hides no secrets), by pure computation, can do quite well.
   
• We have learned a lot about how the human mind works, as a special capacity of matter. We now know that many aspects of perception begin as specific molecular events. Great challenges remain to bring understanding of memory, emotion, and ultimately creative thought to the same level; but there is every reason to think they too will come into focus. At least, no show-stoppers have  yet appeared. 

The eternal, ever vague "problems" of free will and consciousness will be retired, with due respect, as mechanistic understanding of how human minds actually work brings in more powerful, less nebulous concepts (as has already happened for computation).

More interesting is the question of consequences. Here is a relevant thought experiment: Imagine an artificial intelligence, with human-like insight, contemplating her own blueprint. What would she make of it? I think it's overwhelmingly likely that among her first thoughts would be how to begin making improvements. This processor could be faster, that memory more capacious—and, above all, the reward system more rewarding!

Our heroine would surely be inspired, as I am, by William Blake's prophecy

If the doors of perception were cleansed
Man would see things as they are, Infinite

In bad science fiction, androids are sometimes horrified to learn that they are "mere machines". Following the instruction of the Delphic oracle, to "Know Thyself", we find ourselves making a similar discovery. The wise and mature reaction to the realization that mind and matter are mind/matter, is to take joy in what a wonderful thing mind/matter can be, and is.

Would you say that the behavior of your computer or smartphone is determined by an interaction between its inherent design and the way it is influenced by the environment? It's unlikely—such a statement would not be false, but it would be obtuse. Complex adaptive systems have a nonrandom organization, and they have inputs. But speaking of inputs as "shaping" the system's behavior, or pitting its design against its input, would lead to no insight as to how the system works. The human brain is far more complex, and processes its input in more complex ways, than human-made devices, yet many people analyze it in ways that are too simplistic for our far simpler toys. Every term in the equation is suspect.

Behavior: More than half a century after the cognitive revolution, people still ask whether a behavior is genetically or environmentally determined. Yet neither the genes nor the environment can control the muscles directly. The cause of behavior is the brain. While it is sensible to ask how emotions, motives or learning mechanisms have been influenced by the genes, it makes no sense to ask this of behavior itself.

Genes: Molecular biologists have appropriated the term "gene" to refer to stretches of DNA that code for a protein. Unfortunately, this sense differs from the one used in population genetics, behavioral genetics, and evolutionary theory, namely any information carrier that is transmissible across generations and has sustained effects on the phenotype. This includes any aspect of DNA that can affect gene expression, and is closer to what is meant by "innate" than genes in the molecular biologists' narrow sense. The confusion between the two leads to innumerable red herrings in discussions of our makeup, such as the banality that the expression of genes (in the sense of protein-coding stretches of DNA) is regulated by signals from the environment. How else could it be? The alternative is that every cell synthesizes every protein all the time! The epigenetics bubble inflated by the science media is based on a similar confusion.

Environment: This term for the inputs to an organism is also misleading. Of all the energy impinging on an organism, only a subset, processed and transformed in complex ways, has an effect on its subsequent information processing. Which information is taken in, how it is transformed, and how it affects the organism (that is, the way that the organism learns) all depend on the organism's innate organization. To speak of the environment "determining" or "shaping" behavior is unperspicuous.

Even the technical sense of "environment" used in quantitative behavioral genetics is perversely confusing. Now, there is nothing wrong with partitioning phenotypic variance into components that correlate with genetic variation (heritability) and with variation among families ("shared environment"). The problem comes from the so-called "nonshared" or "unique environmental influences." This consists of all the variance that is attributable neither to genetic nor familiar variation. In most studies, it's calculated as 1 – (heritability + shared environment). Practically, you can think of it as the differences between identical twins who grow up in the same home. They share their genes, parents, older and younger siblings, home, school, peers, and neighborhood. So what could make them different? Under the assumption that behavior is a product of genes plus environment, it must be something in the environment of one that is not in the environment of the other.

But this category really should be called "miscellaneous/unknown," because it has nothing necessarily to do with any measurable aspect of the environment, such as one sibling getting the top bunk bed and the other the bottom, or a parent unpredictably favoring one child, or one sibling getting chased by a dog, coming down with a virus, or being favored by a teacher. These influences are purely conjectural, and studies looking for them have failed to find them. The alternative is that this component actually consists of the effects of chance – new mutations, quirky prenatal effects, noise in brain development, and events in life with unpredictable effects.

Stochastic effects in development are increasingly being recognized by epidemiologists, frustrated by such recalcitrant phenomena such as nonagenarian pack-a-day smokers and identical twins discordant for schizophrenia, homosexuality, and disease outcomes. They are increasingly forced to acknowledge that God plays dice with our traits. Developmental biologists have come to similar conclusions. The bad habit of assuming that anything not classically genetic must be "environmental" has blinkered behavioral geneticists (and those who interpret their findings) into the fool's errand of looking for environmental effects for what may be randomness in developmental processes.

A final confusion in the equation is the seemingly sophisticated add-on of "gene-environment interactions." This is also designed to confuse. Gene-environment interactions do not refer to the fact that the environment is necessary for genes to do their thing (which is true of all genes). It refers to a flipflop effect in which genes affect a person one way in one environment but another way in another environment, whereas an alternative genes has a different pattern. For example, if you inherit allele 1, you are vulnerable: a stressor makes you neurotic. If you inherit allele 2, you are resilient: a stressor leaves you normal. With either gene, if you are never stressed, you're normal.

Gene-environment interactions in this technical sense, confusingly, go into the "unique environmental" component, because they are not the same (on average) in siblings growing up in the same family. Just as confusingly, "interactions" in the common-sense sense, namely that a person with a given genotype is predictably affected by the environment, goes into the "heritability" component, because quantitative genetics measures only correlations. This confound is behind the finding that the heritability of intelligence increases, and the effects of shared environment decrease, over a person's lifetime. One explanation is that genes have effects late in life, but another is that people with a given genotype place themselves in environments that indulge their inborn tastes and talents. The "environment" increasingly depends on the genes, rather than being an exogenous cause of behavior. 

Today computationalists and cognitive scientists—those researchers who see digital computing as a model for human thought and the mind—are nearly unanimous in believing the Grand Analogy and teaching it to their students. And whether you accept it or not, the analogy is milestone of modern intellectual history. It partly explains why a solid majority of contemporary computationalists and cognitive scientists believe that eventually, you will be able to give your laptop a (real not simulated) mind by downloading and executing the right software app. Whereupon if you tell the machine, "imagine a rose," it will conjure one up in its mind, just as you do. Tell it to "recall an embarrassing moment" and it will recall something and feel embarrassed, just as you might. In this view, embarrassed computers are just around the corner.

But no such software will ever exist, and the analogy is false and has slowed our progress in grasping the actual phenomenology of mind. We have barely begun to understand the mind from inside. But what's wrong with this suggestive, provocative analogy? My first reason is old; the other three are new.

1. The software-computer system relates to the world in a fundamentally different way from the mind-brain system. Software moves easily among digital computers, but each human mind is (so far) wedded permanently to one brain. The relationship between software and the world at large is arbitrary, determined by the programmer; the relationship between mind and world is an expression of personality and human nature, and no one can re-arrange it.

There are computers without software, but no brains without minds. Software is transparent. I can read off the precise state of the entire program at any time. Minds are opaque—there is no way I can know what you are thinking unless you tell me. Computers can be erased; minds cannot. Computers can be made to operate precisely as we choose; minds cannot. And so on. Everywhere we look we see fundamental differences.

2. The Grand Analogy presupposes that minds are machines, or virtual machines—but a mind has two equally-important functions, doing and being; a machine is only for doing. We build machines to act for us. Minds are different: yours might be wholly quiet, doing ("computing") nothing; yet you might be feeling miserable or exalted—or you might merely be conscious.

Emotions in particular are not actions, they are ways to be. And emotions—states of being—play an important part in the mind's cognitive work. They allow you, for instance, to feel your way to a cognitive goal. ("He walked to the window to recollect himself, and feel how he ought to behave." Jane Austen, Persuasion.) Thoughts contain information, but feelings (mild wistfulness, say, on a warm summer morning) contain none. Wistfulness is merely a way to be.

Until we understand how to make digital computers feel (or experience phenomenal consciousness), we have no business talking up a supposed analogy between mind:brain and software:computer.

(Those who note that computers-that-can-feel are incredible are sometimes told: "You assert that many billions of tiny, meaningless computer instructions, each unable to feel, could never create a system that feels. Yet neurons are also tiny, "meaningless" and feel nothing--but a hundred billion of those yields a brain that does feel." Which is irrelevant: 100 billion neurons yield a brain that supports a mind, but a hundred billion sandgrains or used tires yields nothing. You need billions of the right article arranged in the right way to get feeling.)

3. The process of growing up is innate to the idea of human being. Social interactions and body structure change over time, and the two sets of changes are intimately connected. A toddler who can walk is treated differently from an infant who can't. No robot could acquire a human-like mind unless it could grow and change physically, interacting with society as it did.

But even if we focus on static, snapshot minds, a human mind requires a human body. Bodily sensations create mind-states that cause physical changes that create further mind-changes. A feedback loop. You are embarrassed; you blush; feeling yourself blush, your embarrassment increases. Your blush deepens.

We don't think with our brains only. We think with our brains and bodies together. We might build simulated bodies out of software—but simulated bodies can't interact in human ways with human beings. And we must interact with other people to become thinking persons.

4. Software is inherently recursive; recursive structure is innate to the idea of software. The mind is not and cannot be recursive.

A recursive structure incorporates smaller versions of itself: an electronic circuit made of smaller circuits, an algebraic expression built of smaller expressions.

Software is a digital computer realized by another digital computer. (You can find plenty of definitions of digital computer.) "Realized by" means made-real-by or embodied-by. The software you build is capable of exactly the same computations as the hardware on which it executes. Hardware is a digital computer realized by electronics (or some equivalent medium).

Suppose you design a digital computer; you embody it using electronics. So you've got an ordinary computer, with no software. Now you design another digital computer: an operating system, like Unix. Unix has a distinctive interface—and, ultimately, the exact same computing power as the machine it runs on. You run your new computer (Unix) on your hardware computer. Now you build a word processor (yet another dressed up digital computer), to run on Unix. And so on, ad infinitum. The same structure (a digital computer) keeps recurring. Software is inherently recursive.

The mind is not and cannot be. You cannot "run" another mind on yours, and a third mind on that, and a fourth atop the third.

In conclusion: much has been gained by mind science's obsession with computing. Computation has been a useful lens to focus scientific and philosophic thinking on the essence of mind. The last generation has seen, for example, a much clearer view of the nature of consciousness. But we have always known ourselves poorly. We still do. Your mind is a room with a view, and we still know the view (objective reality) a lot better than the room (subjective reality). Today subjectivism is re-emerging among those who see through the Grand Analogy. Computers are fine, but it's time to return to the mind itself, and stop pretending we have computers for brains; we'd be unfeeling, unconscious zombies if we had.  

The scientific idea that a trait or characteristic of an organism that is hard-wire means that it is a permanent feature should be retired. Case in point: God and religion.

Ever since Charles Darwin theorized in his 1871 book The Descent of Man that "a belief in all-pervading spiritual agencies seems to be universal" and therefore an evolved characteristic of our species that is hardwired into our brains, scientists have been running experiments and conducting surveys to show why God won't go away. Anthropologists have found such human universals as specific supernatural beliefs about death and the afterlife, fortune and misfortune, and especially magic, myths, rituals, divination and folklore. Behavior geneticists report from twin studies—most notably twins separated at birth and raised in different environments—that 40-50% of the variance of God beliefs and religiosity are genetic. Some scientists have even claimed to have found a "God gene" (or more precisely, a "God gene complex") that leads humans to have a need for spiritual transcendence and belief in a higher power of some kind. Even specific elements of religious stories—such as a destructive flood, a virgin birth, miracles, a resurrection from the dead—seem to appear independently of one another over and over again throughout history in a wide variety of cultures, implying that there is a hard-wired component to religion and God beliefs. I have held this theory myself. Until now.

If and when we establish a permanent colony on Mars, if its members consist of nonbelieving scientists with a purely secular worldview it would be interesting to check in 10 (or 100) generations to see if God has returned. Until that experiment is conducted, however, we have to consider the results of natural experiments run here on Earth. In the Western world, for example, a 2013 survey of 14,000 people in 13 nations (Germany, France, Sweden, Spain, Switzerland, Turkey, Israel, Canada, Brazil, India, South Korean, and the UK and US) conducted by the German pollster Bertelsmann Stiftung for their Religion Monitor found that most of these countries showed a declining trend in religiosity and belief in God, especially among the youth. In Spain, for example, 85% of respondents over the age of 45 report being moderately to very religious, but only 58% of those under 29 years of age so report. In Europe in general, only 30-50% said that religion is important in their own lives, and in many European countries less than a third say that they believe in God.

Even in the über religious United States, the pollsters found that 31% of Americans say they are "not religious or not very religious." This finding confirms those of a 2012 Pew Forum survey that found that the fastest growing religious cohort in America are the "Nones" (those with no religious affiliation) at 20% (33% of adults under 30), broken down into atheists and agnostics at 6% and the unaffiliated at 14%. The raw numbers are stunning: with the U.S. adult population (age 18 and over) at 240 million, this translates into 48 million Nones, or 14.4 million atheists/agnostics and 33.6 million unaffiliated. There were also generational differences that reveal a significant trend toward unbelief, with the "Greatest" generation (born 1913-1927) at 5%, the "Silent" generation (born 1928-1945) at 9%, the "Boomers" (born 1946-1964) at 15%, the "GenXers" (born 1965-1980) at 21%, the "Older Millennials" (born 1981-1989) at 30%, and the "Younger Millennials" (born 1990-1994) at 34%.

At this rate I project that the Nones will reach 100% in the year 2220.

It is time for scientists to retire the theory that God and religion are hardwired in our brains. Like everyone else, scientists are subject to cognitive biases that tilt their thinking toward trying to explain common beliefs, so it is good for us to take the long-view perspective and compare today to, say, half a millennia ago when God beliefs were virtually 100%, or to the hunter-gatherer tribes of our Paleolithic ancestors who, while employing any number of superstitious rituals, did not believe in a God or practice a religion that even remotely resembles the deities or religions of modern peoples.

This indicates that religious faith and belief in God is a byproduct of other cognitive processes (e.g., agency detection) and cultural propensities (the need to affiliate) that, while hard-wired, can be expunged through reason and science in the same manner as any number of other superstitious rituals and supernatural beliefs once held by the most learned scholars and scientists of Europe five centuries ago. For example, at that time the prevailing theory to explain crop failures, weather anomalies, diseases, and various other maladies and misfortunes was witchcraft, and the solution was to strap women to pyres and torch them to death. Today, no one in their right mind believes this. With the advent of a scientific understanding of agriculture, climate, disease, and other causal vectors—including the role of chance—the witch theory of causality fell into disuse.

So it has been and will continue to be with other forms of the hard-wired=permanent idea, such as violence. We may be hard-wired for violence, but we can attenuate it considerably through scientifically tested methods. Thus, for my test case here, I predict that in another 500 years the God-theory of causality will have fallen into disuse, and the 21st-century scientific theory that God is hardwired into our brains as a permanent feature of our species will be retired.

We don't need to credit an all-seeing God with the creation of life and matter to suspect that something wonderfully strange is going on in the dimension we call reality. Most of us living in it feel invested with a sense of purpose. Whether this directionality is a genuine, pre-existing condition of the universe, an illusion perpetrated by DNA, or something that will one day emerge from social interaction, has yet to be determined. At the very least, this means our experience and expectations of life can no longer be dismissed as impediments to proper observation and analysis.

But science's unearned commitment to materialism has led us into convoluted assumptions about the origins of space-time, in which time itself simply must be accepted as a byproduct of the big bang, and consciousness (if it even exists) as a byproduct of matter. Such narratives follow information on its continuing evolution toward complexity, the singularity, and robot consciousness—a saga no less apocalyptic than the most literal interpretations of Biblical prophecy.

It's entirely more rational—and less steeped in storybook logic—to work with the possibility that time predates matter, and that consciousness is less the consequence of a physical, cause-and-effect reality than a precursor.

By starting with Godlessness as a foundational principle of scientific reasoning, we make ourselves unnecessarily resistant to the novelty of human consciousness, its potential continuity over time, and the possibility that it has purpose.
 

In a world where scientific theories often sound bizarre and counter to everyday intuition, and where a wide variety of nonsense aspires to be recognized as "scientific," it's important to be able to separate science from non-science—what philosophers call "the demarcation problem." Karl Popper famously suggested the criterion of "falsifiability"—a theory is scientific if it makes clear predictions that can be unambiguously falsified.

It's a well-meaning idea, but far from the complete story. Popper was concerned with theories such as Freudian psychoanalysis and Marxist economics, which he considered non-scientific. No matter what actually happens to people or societies, Popper claimed, theories like these will always be able to tell a story in which the data are compatible with the theoretical framework. He contrasted this with Einstein's relativity, which made specific quantitative predictions ahead of time. (One prediction of general relativity was that the universe should be expanding or contracting, leading Einstein to modify the theory because he thought the universe was actually static. So even in this example the falsifiability criterion is not as unambiguous as it seems.)

Modern physics stretches into realms far removed from everyday experience, and sometimes the connection to experiment becomes tenuous at best. String theory and other approaches to quantum gravity involve phenomena that are likely to manifest themselves only at energies enormously higher than anything we have access to here on Earth. The cosmological multiverse and the many-worlds interpretation of quantum mechanics posit other realms that are impossible for us to access directly. Some scientists, leaning on Popper, have suggested that these theories are non-scientific because they are not falsifiable.

The truth is the opposite. Whether or not we can observe them directly, the entities involved in these theories are either real or they are not. Refusing to contemplate their possible existence on the grounds of some a priori principle, even though they might play a crucial role in how the world works, is as non-scientific as it gets.

The falsifiability criterion gestures toward something true and important about science, but it is a blunt instrument in a situation that calls for subtlety and precision. It is better to emphasize two more central features of good scientific theories: they are definite, and they are empirical. By "definite" we simply mean that they say something clear and unambiguous about how reality functions. String theory says that, in certain regions of parameter space, ordinary particles behave as loops or segments of one-dimensional strings. The relevant parameter space might be inaccessible to us, but it is part of the theory that cannot be avoided. In the cosmological multiverse, regions unlike our own are unambiguously there, even if we can't reach them. This is what distinguishes these theories from the approaches Popper was trying to classify as non-scientific. (Popper himself understood that theories should be falsifiable "in principle," but that modifier is often forgotten in contemporary discussions.)

It's the "empirical" criterion that requires some care. At face value it might be mistaken for "makes falsifiable predictions." But in the real world, the interplay between theory and experiment isn't so cut and dried. A scientific theory is ultimately judged by its ability to account for the data—but the steps along the way to that accounting can be quite indirect.

Consider the multiverse. It is often invoked as a potential solution to some of the fine-tuning problems of contemporary cosmology. For example, we believe there is a small but nonzero vacuum energy inherent in empty space itself. This is the leading theory to explain the observed acceleration of the universe, for which the 2011 Nobel Prize was awarded. The problem for theorists is not that vacuum energy is hard to explain; it's that the predicted value is enormously larger than what we observe.

If the universe we see around us is the only one there is, the vacuum energy is a unique constant of nature, and we are faced with the problem of explaining it. If, on the other hand, we live in a multiverse, the vacuum energy could be completely different in different regions, and an explanation suggests itself immediately: in regions where the vacuum energy is much larger, conditions are inhospitable to the existence of life. There is therefore a selection effect, and we should predict a small value of the vacuum energy. Indeed, using this precise reasoning, Steven Weinberg did predict the value of the vacuum energy, long before the acceleration of the universe was discovered.

We can't (as far as we know) observe other parts of the multiverse directly. But their existence has a dramatic effect on how we account for the data in the part of the multiverse we do observe. It's in that sense that the success or failure of the idea is ultimately empirical: its virtue is not that it's a neat idea or fulfills some nebulous principle of reasoning, it's that it helps us account for the data. Even if we will never visit those other universes.

Science is not merely armchair theorizing; it's about explaining the world we see, developing models that fit the data. But fitting models to data is a complex and multifaceted process, involving a give-and-take between theory and experiment, as well as the gradual development of theoretical understanding in its own right. In complicated situations, fortune-cookie-sized mottos like "theories should be falsifiable" are no substitute for careful thinking about how science works. Fortunately, science marches on, largely heedless of amateur philosophizing. If string theory and multiverse theories help us understand the world, they will grow in acceptance. If they prove ultimately too nebulous, or better theories come along, they will be discarded. The process might be messy, but nature is the ultimate guide.

Homo economicus is an old idea and a wrong idea, deserving a burial of pomp and circumstance but a burial nonetheless. People can be individualistic and selfish, yes, and under some circumstances narrowly focused on economic wellbeing. But, even those most closely associated with the concept never fully believed it. Hobbes argued that people prefer to act according to the golden rule but that their circumstances often made it difficult. Without rule of law and in a world of theft and predation, people act with defensive selfishness. Adam Smith, whose invisible hand required individual pursuit of narrow interest, recognized that individuals have emotions, sentiments, and morals that influence their thinking. Even Milton Friedman was not sure if narrowly selfish individualism was a correct assumption about human behavior; he didn't care if the supposition was right or wrong but only cared if it was useful. It no longer is.

The theories and models derived from the assumption of homo economicus generally depend on a second, equally problematic assumption: full rationality. Related but distinct sets of scientific findings make suspect each piece of the pairing of narrowly selfish motivation with rational action. Philosophers, such as Nietzsche, and psychoanalytic theorists, such as Sigmund Freud, argued that people acted in a whole variety of ways that were explicable perhaps but were closer to animal instincts than calculative instrumentality. Herbert Simon and certainly Daniel Kahneman and Amos Tversky revealed the extent to which cognitive limitations undermine rational calculations.

Even if individuals can do no better than "satisfice," that wonderful Simon term, they might still be narrowly self-interested, albeit—because of cognitive limitations—ineffective in achieving their ends. This perspective, which is at the heart of homo economicus, must also be laid to rest. Darwin and those influenced by him long recognized that our species, like others, is altruistic at least in the narrow sense of acting to preserve one's gene pool by protecting one's young. Most people do much more than that. The overwhelming finding of experimental research confounds the presumption that, given the opportunity, individuals usually free ride. Indeed, most act according to norms of fairness and reciprocity. Many will make small sacrifices or forego larger returns, and some will even engage in costly action (up to a point) to "do the right thing." Anthropologists and biologists have long provided evidence of the human animal as a social animal. The understanding that individuals are in social networks and communities opens the door to more complex models of reciprocity and ethical obligation. Consequently, social scientists can now account for aggregate outcomes they otherwise could not: large-scale volunteering for the military in times of war, protest behavior, and contributions to public good provision.

The rejection of homo economicus does not mean a total absence of conditions under which narrow self-interest dominates. Experiments suggest very different socializations can produce quite distinct reasoning: economic graduate students are far more likely to free ride than other students. At least two sets of circumstances can induce individualistic selfishness and significantly narrow a person's community of fate, that is, those with whom one feels interdependent and to whom one feels an obligation to help. The first is extreme poverty and the second extreme competition. Those suffering hunger and deprivation tend to focus on meeting their needs. As the growing number of dystopian novels suggest, the result may be theft and murder in the interest of obtaining food, shelter, and security. The classic experiments with rats come to the same conclusions.

Extreme competition, at the least, narrows focus to the goal at hand. In some forms, however, striving to be king of the hill and sometimes literally to be king does provoke something akin to a Hobbesian world. Shakespeare, as he often does, captures the power of circumstance and ambition; his version of the War of the Roses is a testament to narrow self-interested instrumentality dressed in the rhetoric of serving the country. Or witness the recent revelations about business ethics (or, rather, lack of ethics).

That people are often—perhaps more often than not—motivated to act beyond narrow self-interest is fully compatible with the importance of material incentives in motivating behavior. We are all susceptible to rewards, and we all fear punishment. Ceteris paribus, we prefer the first and wish to avoid the second. However, ethics, morality, and the obligations of reciprocity can affect our decisions even when there is considerable money at stake or serious threats to wellbeing. Few are willing to sacrifice everything for a cause or principle, but most of us are willing to sacrifice something.

The reliance on homo economicus as the basis of human motivation has given rise to a grand body of theory and research over the past two hundred years. As an underlying assumption, it has generated some of the best work in economics. As a foil, it has generated findings about cognitive limitations, the role of social interactions, and ethically based motivations. The power of the concept of homo economicus was once great, but its power has now waned, to be succeeded by new and better paradigms and approaches grounded in more realistic and scientific understandings of the sources of human action.

In the beginning, there was dualism. Descartes famously posited two kinds of substance, non-physical mind and material body; Leibniz differentiated mental and physical realms. But dualism faced a challenge: explaining how mind and body interact. The mind executes an intention to raise a finger, and behold, it rises! The body brushes against something sharp, and the mind registers pain.

We now know, of course, that mind and brain are intimately connected. Injuries to the brain can alter perceptual experience, cognitive abilities, and personality. Changes in brain chemistry can do the same. There's no "mental substance" that appears along some phylogenetic branch of our evolutionary history, nor a point in ontogeny during which we receive a non-physical infusion of mind-stuff. We've come a long way from Ambrose Bierce's formulation of the mind in The Devil's Dictionary as "a mysterious form of matter secreted by the brain."

In fact, it appears the mind is just the brain. Or perhaps, to quote Marvin Minsky, "the mind is what the brain does." If we want to understand the mind, we should look to neuroscience and the brain for the real answers.

Or maybe not.

In our enthusiasm to find a scientifically-acceptable alternative to dualism, some of us have gone too far the other way, adopting a stark reductionism. Understanding the mind is not just a matter of understanding the brain. But then, what is it?

It doesn't help that many alternatives to the "mind=brain" equation seem counterintuitive or spooky. For example, some suggest that the mind extends beyond the brain to encompass the whole body or even parts of the environment, or that the mind is not subject to the laws of physics.

Are there other options? Indeed there are. But given that mind and brain are pretty heady matters, it helps to think about a more concrete—and tastier—example. Consider baking.

I'm an antireductionist about baking. It's not that I believe in a "cake substance" that's materially distinct from flour and sugar and leavening. And it's not that I think cakes have some magical metaphysical property (though the best ones sort of do). The tenets of baking antireductionism are far less controversial, and they stem from what we want our "theory of baking" to provide. We want to understand why some cakes turn out better than others, and what we can do to achieve better baked goods in the future. Should we change an ingredient? Mix the batter less vigorously?

Answering these questions can appeal to chemistry and physics. But a theory of baking wouldn't be very useful if it were formulated in terms of molecules and atoms. As bakers, we want to understand the relationship between—for example—mixing and texture, not between kinetic energy and protein hydration. The relationships between the variables we can tweak and the outcomes that we care about happen to be mediated by chemistry and physics, but it would be a mistake to adopt "cake reductionism" and replace the study of baking with the study of physical and chemical interactions among cake components.

Of course, you could decide that you're not interested in baking, and thus reject the theoretical constructs of my "baking theory" in favor of chemistry and physics. But if you are interested in the project of explaining, predicting, and controlling the quality of your baked goods, then you'll need something like a baking theory to work with.

Now consider the mind. Most of us are interested in a theory of the mind because we want to explain, predict, and control behaviors, mental states, and experiences. Given that mental phenomena are physically realized in the brain, just as cake properties are physically realized by their ingredients and their interactions, it's no surprise that understanding the brain is incredibly useful. But if we want to know—for instance—how to influence minds to achieve particular behaviors, it would be a mistake to look for explanations solely at the level of the brain.

These reflections won't be news to many philosophers, but they're worth repeating. Rejecting the mind in an effort to achieve scientific legitimacy—a trend we've seen with both behaviorism and some popular manifestations of neuroscience—is unnecessary and unresponsive to the aims of scientific psychology. Understanding the mind isn't the same as understanding the brain. Fortunately, though, we can achieve such understanding without abandoning scientific rigor. Or, to adopt another baking analogy, we can have our cake and eat it, too.

The bigger an animal's brain, the greater its intelligence. You may think the connection is obvious. Just look at the evolutionary lineage of human beings: humans have bigger brains—and are cleverer—than chimpanzees, and chimpanzees have bigger brains—and are cleverer—than monkeys. Or, as an analogy, look at the history of computing machines in the 20th century. The bigger the machines, the greater their number-crunching powers. In the 1970's the new computer at my university department took up a whole room.

From the phrenology of the 19th century, to the brain-scan sciences of the 21st, it has indeed been widely assumed that brain volume determines cognitive capacity. In particular, you'll find the idea repeated in every modern textbook that the brain size of different primate species is causally related to their social intelligence. I admit I'm partly responsible for this, having championed the idea back in the 1970's. Yet, for a good many years now, I've had a hunch that the idea is wrong.

There are too many awkward facts that don't fit in. For a start, we know that modern humans can be born with only two thirds the normal volume of brain tissue, and show next to no cognitive deficit as adults. We know that, during normal human brain development, the brain actually shrinks as cognitive performance improves (a notable example being changes in the "social brain" during adolescence, where the cortical grey matter decreases in volume by about 15% between age 10 and 20). And most surprising of all, we know that there are nonhuman animals, such as honey bees or parrots, that can emulate many feats of human intelligence with brains that are only a millionth (bee) or a thousandth (parrot) the size of a human's.

The key, of course, is programming: What really matters to cognitive performance is not so much the brain's hardware as its onboard software. And smarter software certainly does not require a bigger hardware base (in fact, as the shrinkage of the cortex during adolescence shows, it may actually require a smaller—tidier—one). It's true that programs to deliver superior performance may require a lot of designing, either by natural selection or learning. But the fact is that, once they've been invented, they will likely make less demands on hardware than the older versions. To take the special case of social intelligence, I'd say it's quite possible that the algorithm for solving "theory of mind" problems could be written on the back of a postcard and could be implemented on an iPhone. In which case, the widely touted suggestion that the human brain had to double in size for humans to be capable of "second-order mind-reading", makes little sense.

Then why did the human brain double in size? Why is it much bigger than you might think it needs to be, to underpin our level of intelligence? There's no question that big brains are costly to build and maintain. So, if we are to retire the "obvious theory", what can we put in its place? The answer I'd suggest lies in the advantage of having a large amount of cognitive reserve. Big brains have spare capacity that can be called on if and when working-parts get damaged or wear out. From adulthood onwards humans—like other mammals—begin to lose a significant amount of brain tissue to accidents, haemorrhages and degeneration. But because humans can draw on this extra reserve, the loss doesn't have to show. This means humans can retain their mental powers into relative old age, long after their smaller brained ancestors would have become incapacitated. (And as a matter of fact the unfortunate individual born with an unusually small brain is much more likely to succumb to senile dementia in his forties).

True, many of us die for other reasons with unused brain power to spare. But some of us live considerably longer than we might have done if our brains were half the size. So, what evolutionary advantage does longevity bring, even the post-reproductive longevity typical of humans? The answer surely is that humans can benefit—as no other species could do—from the presence of mentally-sound grandparents and great-grandparents, whose role in caretaking and teaching has been key to the success of human culture.

The phrase "science and technology" presumes an inseparability that may not be as secure as we think. There can be science without technology, and there can be technology without science. 

Pure mathematics is one example—from the Pythagoreans to Japanese temple geometry—of a science flourishing without technology. Imperial China developed sophisticated technologies while neglecting science, and it is all too easy to imagine a society that embraces technology but represses science, until only technology remains. Or, one particular species of technology might achieve such dominance that it halts the advance of science in order to preserve itself.

That science has brought us technology does not mean that technology will always bring us science. Science could go into retirement at any time. Retiring the assumption that as long as technology flourishes, so will science, might help us avoid this mistake.