2011 : WHAT SCIENTIFIC CONCEPT WOULD IMPROVE EVERYBODY'S COGNITIVE TOOLKIT?

That idea, or bundle of ideas, seems to me the most important revolution in general thinking in the last 150 years. It has given us a whole new sense of who we are, where we fit, and how things work. It has made commonplace and intuitive a type of perception that used to be the province of mystics — the sense of wholeness and interconnectedness.

Beginning with Copernicus, our picture of a semi-divine humankind perfectly located at the centre of The Universe began to falter: we discovered that we live on a small planet circling a medium sized star at the edge of an average galaxy. And then, following Darwin, we stopped being able to locate ourselves at the centre of life. Darwin gave us a matrix upon which we could locate life in all its forms: and the shocking news was that we weren't at the centre of that either — just another species in the innumerable panoply of species, inseparably woven into the whole fabric (and not an indispensable part of it either). We have been cut down to size, but at the same time we have discovered ourselves to be part of the most unimaginably vast and beautiful drama called Life.

Before ''ecology'' we understood the world in the metaphor of a pyramid — a heirarchy with God at the top, Man a close second and, sharply separated, a vast mass of life and matter beneath. In that model, information and intelligence flowed in one direction only — from the intelligent top to the ''base'' bottom — and, as masters of the universe, we felt no misgivings exploiting the lower reaches of the pyramid.

The ecological vision has changed that: we now increasingly view life as a profoundly complex weblike system, with information running in all directions, and instead of a single heirarchy we see an infinity of nested-together and co-dependent heirarchies — and the complexity of all this is such to be in and of itself creative. We no longer need the idea of a superior intelligence outside of the system — the dense field of intersecting intelligences is fertile enough to account for all the incredible beauty of ''creation''.

The ''ecological'' view isn't confined to the organic world. Along with it comes a new understanding of how intelligence itself comes into being. The classical picture saw Great Men with Great Ideas...but now we tend to think more in terms of fertile circumstances where uncountable numbers of minds contribute to a river of innovation. It doesn't mean we cease to admire the most conspicuous of these — but that we understand them as effects as much as causes. This has ramifications for the way we think about societal design, about crime and conflict, education, culture and science.

That in turn leads to a re-evaluation of the various actors in the human drama. When we realise that the cleaners and the bus drivers and the primary school teachers are as much a part of the story as the professors and the celebrities, we will start to accord them the respect they deserve.

Do you need language—including words—for sophisticated thinking or do they merely facilitate thought? This question goes back to a debate between two Victorian scientists Max Mueller and Francis Galton.

A word that has made it into the common vocabulary of both science and pop culture is "paradigm" (and the converse "anomaly") having been introduced by the historian of science Thomas Kuhn. It is now widely used and misused both in science and in other disciplines almost to the point where the original meaning is starting to be diluted. (This often happens to "memes" of human language and culture, which don't enjoy the lawful, particulate transmission of genes.) The word "paradigm" is now often used inappropriately—especially in the US—to mean any experimental procedure such as "the Stroop paradigm" or "a reaction time paradigm" or "fMR paradigm."

However, its appropriate use has shaped our culture in significant ways; even influencing the way scientists work and think. A more prevalent associated word is "skepticism," originating from the name of a Greek school of philosophy. This is used even more frequently and loosely than "anomaly" and "paradigm shift."

One can speak of reigning paradigms; what Kuhn calls normal science—what I cynically refer to as a "mutual admiration club trapped in a cul-de-sac of specialization." The club usually has its Pope(s), hierarchical priesthood, acolytes and a set of guiding assumptions and accepted norms that are zealously guarded almost with religious fervor. (They also fund each other and review each other’s papers and grants and give each other awards.)

This isn't entirely useless; its called "normal science" that grows by progressive accretion, employing the bricklayers rather than architects of science. If a new experimental observation (e.g., bacterial transformation; ulcers cured by antibiotics) threatens to topple the edifice, its called an anomaly and the typical reaction of those who practice normal science is to ignore it or brush it under the carpet—a form of psychological denial surprisingly common among my colleagues.

This is not an unhealthy reaction since most anomalies turn out to be false alarms; the baseline probability of their survival as real anomalies is small and whole careers have been wasted pursuing them (think "poly water" and cold fusion"). Yet even such false anomalies serve the useful purpose of jolting scientists from their slumber by calling into question the basic axioms that drive their particular area of science. Conformist science feels cozy given the gregarious nature of humans and anomalies force periodic reality checks even if the anomaly turns out to be flawed.

More important, though, are genuine anomalies that emerge every now and then, legitimately challenging the status quo, forcing paradigm shifts and leading to scientific revolutions. Conversely, premature skepticism toward anomalies can lead to stagnation of science. One needs to be skeptical of anomalies but equally skeptical of the status quo if science is to progress.

I see an analogy between the process of science and of evolution by natural selection. For evolution, too, is characterized by periods of stasis (= normal science) punctuated by brief periods of accelerated change (= paradigm shifts) based on mutations (= anomalies), most of which are lethal (false theories), but some lead to the budding off of new species and phylogenetic trends (= paradigm shifts).

Since most anomalies are false alarms (spoon bending, telepathy, homeopathy) one can waste a lifetime pursuing them. So how does one decide which anomalies to invest in? Obviously one can do so by trial and error but that can be tedious and time consuming.

Let's take four well-known examples: (1) continental drift; (2) bacterial transformation; (3) cold fusion; (4) telepathy. All of these were anomalies when first discovered because they didn't fit the big picture of normal science at that time. The evidence that all the continents broke off and drifted away from a giant super-continent was staring at peoples faces—as Wagener noted in the early 20th century. (The coastlines coincided almost perfectly; certain fossils found on the east coast of Brazil were exactly the same as the ones on the west coast of Africa, etc.) Yet it took fifty years for he idea to be accepted by the skeptics.

The second anomaly (2)—observed a decade before DNA and the genetic code—was that if you incubate one species of bacterium (pneumococcus A) with another species in a test tube (Pneumococcus B), then bacterium A becomes transformed into B! (Even the DNA—rich juice from B will suffice—leading Avery to suspect that heredity might have a chemical basis.) Others replicated this. It was almost like saying put a pig and donkey into a room and two pigs emerge—yet the discovery was largely ignored for a dozen years. Until Watson and Crick pointed out the mechanism of transformation. The third anomaly—telepathy—is almost certainly a false alarm.

You will see a general rule of thumb emerging here. Anomalies (1) and (2) were not ignored because of lack of empirical evidence. Even a school child can see the fit between continental coastlines or similarity of fossils. It was ignored solely because it didn't fit the big picture—the notion of terra firma or a solid, immovable earth—and there was no conceivable mechanism that would allow continents to drift (until plate tectonics was discovered). Likewise, (2) was repeatedly confirmed but ignored because it challenged the fundamental doctrine of biology—the stability of species. But notice that the third (telepathy) was rejected for two reasons; first, it didn't fit the big picture and second because it was hard to replicate.

This gives us the recipe we are looking for; focus on anomalies that have survived repeated attempts to disprove experimentally, but are ignored by the establishment solely because you cant think of a mechanism. But don't waste time on ones that have not been empirically confirmed despite repeated attempts (or the effect becomes smaller with each attempt—a red flag!).

"Paradigm" and "paradigm shift" have now migrated from science into pop culture (not always with good results) and I suspect many other words and phrases will follow suit—thereby enriching our intellectual and conceptual vocabulary and day-to-day thinking.

Indeed, words themselves are paradigms or stable "species" of sorts that evolve gradually with progressively accumulating penumbras of meaning, or sometimes mutate into new words to denote new concepts. These can then consolidate into chunks with "handles" (names) for juggling ideas around generating novel combinations. As a behavioral neurologist I am tempted to suggest that such crystallization of words and juggling them is unique to humans and it occurs in brain areas in and near the left TPO (temporal-parietal-occipital junction). But that's pure speculation.

"Nothing In Life Is As Important As You Think It Is, While You Are Thinking About It"

Education is an important determinant of income — one of the most important — but it is less important than most people think. If everyone had the same education, the inequality of income would be reduced by less than 10%. When you focus on education you neglect the myriad other factors that determine income. The differences of income among people who have the same education are huge.

Income is an important determinant of people's satisfaction with their lives, but it is far less important than most people think. If everyone had the same income, the differences among people in life satisfaction would be reduced by less than 5%.

Income is even less important as a determinant of emotional happiness. Winning the lottery is a happy event, but the elation does not last. On average, individuals with high income are in a better mood than people with lower income, but the difference is about 1/3 as large as most people expect. When you think of rich and poor people, your thoughts are inevitably focused on circumstances in which their income is important. But happiness depends on other factors more than it depends on income.

Paraplegics are often unhappy, but they are not unhappy all the time because they spend most of the time experiencing and thinking about other things than their disability. When we think of what it is like to be a paraplegic, or blind, or a lottery winner, or a resident of California we focus on the distinctive aspects of each of these conditions. The mismatch in the allocation of attention between thinking about a life condition and actually living it is the cause of the focusing illusion.

Marketers exploit the focusing illusion. When people are induced to believe that they "must have" a good, they greatly exaggerate the difference that the good will make to the quality of their life. The focusing illusion is greater for some goods than for others, depending on the extent to which the goods attract continued attention over time. The focusing illusion is likely to be more significant for leather car seats than for books on tape.

Politicians are almost as good as marketers in causing people to exaggerate the importance of issues on which their attention is focused. People can be made to believe that school uniforms will significantly improve educational outcomes, or that health care reform will hugely change the quality of life in the United States — either for the better or for the worse. Health care reform will make a difference, but the difference will be smaller than it appears when you focus on it.

For far too long we have laboured under a faulty conception of the senses. Ask anyone you know how many senses we have and they will probably say five; unless they start talking to you about a sixth sense. But why pick five? What of the sense of balance provided by the vestibular system, telling you whether you are going up or down in a lift, forwards or backwards on a train, or side to side on a boat? What about proprioception that gives you a firm sense of where your limbs are when you close your eyes? What about feeling pain, hot and cold? Are these just part of touch, like feeling velvet or silk? And why think of sensory experiences like seeing, hearing, tasting, touching and smelling as being produced by a single sense?

Contemporary neuroscientists have postulated two visual systems — one responsible for how things look to us, the other for controlling action — that operate independently of one another. The eye may fall for visual illusions but the hand does not, reaching smoothly for a shape that looks larger than it is to the observer.

And it doesn't stop here. There is good reason to think that we have two senses of smell: an external sense of smell, orthonasal olfaction, produced by inhaling, that enables us to detect things in the environment such food, predators or smoke; and internal sense, retronasal olfaction, produced by exhaling, that enables us to detect the quality of what we have just eaten, allowing us to decide whether we want any more or should expel it.

Associated with each sense of smell is a distinct hedonic response. Orthonasal olfaction gives rise to the pleasure of anticipation. Retronasal olfaction gives rise to the pleasure of reward. Anticipation is not always matched by reward. Have you ever noticed how the enticing aromas of freshly brewed coffee are never quite matched by the taste? There is always a little disappointment. Interestingly, the one food where the intensity of orthonsally and retronasally judged aromas match perfectly is chocolate. We get just what we expected, which may explain why chocolate is such a powerful stimulus.

Besides the proliferation of the senses in contemporary neuroscience, another major change is taking place. We used to study the senses in isolation, with the greatest majority of researchers focusing on vision. Things are rapidly changing. We now know that the senses do not operate in isolation, but combine at both early and late stages of processing to produce our rich perceptual experiences of our surroundings. It is almost never the case that our experience presents us with just sights or sounds. We are always enjoying conscious experiences made up of sights and sounds, smells, the feel of our body, the taste in our mouths; and yet these are not presented as separate sensory parcels. We simply take in the rich and complex scene without giving much thought to how the different contributors produce the whole experience.

We give little thought to how smell provides a background to every conscious waking moment. People who lose their sense of smell can be plunged into depression and show less sign of recovery a year later than people who lose their sight. This is because familiar places no longer smell the same, and people no longer have their reassuring olfactory signature. Also, patients who lose their smell believe they have lost their sense of taste. When tested, they acknowledge that that can taste sweet, sour, salt, bitter savoury, and metallic. But everything else, missing from the taste of what they are eating, is due to retronasal smell.

What we call taste is one of the most fascinating case studies for how inaccurate our view of our senses is: it is not produced by the tongue alone but is always an amalgam of taste, touch and smell. Touch contributes to sauces tasting creamy, and other foods tasting chewy, crisp, or stale. The only difference between potato chips, which "taste" fresh or stale, is a difference in texture. The largest part of what we call "taste" is in fact smell in the form of retronasal olfaction, which is why people who lose their ability to smell say they can no longer taste anything. Taste, touch and smell are not merely combined to produce experiences of foods of liquids, rather the information from the separate sensory channels is fused into a unified experience of that we call taste and food scientists call flavour.

Flavour perception is the result of multi-sensory integration of gustatory, olfactory and oral somatosenory information into a single experience whose components we are unable to distinguish. It is one of the most multi-sensory experiences we have and can be influenced by both sight and sound. The colours of wines and the sounds food make when we bite or chew them can have large impacts on our resulting appreciation and assessment, and irritation of the trigeminal nerve in the face will make chillies feel "hot" and menthol feel "cool" in the mouth without any actual change in temperature.

In sensory perception, multi-sensory integration is the rule not the exception. In audition, we don't just hear with our ears, we use our eyes to locate the apparent sources of sounds in the cinema where we "hear" the voices coming from the actors' mouths on the screen although the sounds are coming from the sides of the theatre. This is known as the ventriloquism effect. Similarly, retronasal odours detected by olfactory receptors in the nose are experienced as tastes in the mouth. The sensations get re-located to the mouth because oral sensations of chewing or swallowing capture our attention, making us think these olfactory experiences are occurring in the same place.

Other surprising collaboration among the senses are due to cross-modal effects, where stimulation of one sense boosts activity in another. Looking at someone's lips across a crowded room can improve our ability to hear what they are saying, and the smell of vanilla can make a liquid we sip "taste" sweeter, and less sour. This is why we say vanilla is sweet smelling, although sweet is a taste, and pure vanilla is not sweet at all. Industrial manufacturers know about these effects and exploit them. Certain aromas in shampoos, for example, can make the hair "feel" softer; and red coloured drinks "taste" sweet, while drinks with a light green colour "taste" sour. In many of these interactions vision will dominate; but not in every case

. For anyone unlucky enough to have disturbance in their vestibular system they will feel the world is spinning although cues from the eyes and the body should be telling them everything is still. Instead, the brain goes with the combined picture and vision and proprioception fall in line. Luckily, our senses cooperate and we get us around the world, and the world we inhabit is not a sensory, but a multisensory world.

Statistically significant difference — It is a simple phrase that is essential to science and that has become common parlance among educated adults. These three words convey a basic understanding of the scientific process, random events, and the laws of probability. The term appears almost everywhere that research is discussed — in newspaper articles, advertisements for "miracle" diets, research publications, and student laboratory reports, to name just a few of the many diverse contexts where the term is used. It is a short hand abstraction for a sequence of events that includes an experiment (or other research design), the specification of a null and alternative hypothesis, (numerical) data collection, statistical analysis, and the probability of an unlikely outcome. That is a lot of science conveyed in a few words.

It would be difficult to understand the outcome from any research without at least a rudimentary understanding of what is meant by the conclusion that the researchers found or did not find evidence of a "statistically significant difference." Unfortunately, the old saying that "a little knowledge is a dangerous thing" applies to the partial understanding of this term. One problem is that "significant" has a different meaning when used in everyday speech than when used to report research findings.

Most of the time, the word "significant" means that something important happened. For example, if a physician told you that you would feel significantly better following surgery, you would correctly infer that your pain would be reduced by a meaningful amount—you would feel less pain. But, when used in "statistically significant difference," the term "significant" means that the results are unlikely to be due to chance (if the null hypothesis were true); the results may or may not be important. In addition, sometimes, the conclusion will be wrong because researcher can only assert their conclusion at some level of probability. "Statistically significant difference" is a core concept in research and statistics, but as anyone who was taught undergraduate statistics or research methods can tell you, it is not an intuitive idea.

Despite the fact that "statistically significant difference" communicates a cluster of ideas that are essential to the scientific process, there are many pundits who would like to see it removed from our vocabulary because it is frequently misunderstood. Its use underscores the marriage of science and probability theory, and despite its popularity, or perhaps because of it, some experts have called for a divorce because the term implies something that it does not, and the public is often misled. In fact, experts are often misled as well. Consider this hypothetical example: In a well-done study that compares the effectiveness of two drugs relative to a placebo, it is possible that Drug X is statistically significantly different from a placebo and Drug Y is not, yet Drugs X and Y might not be statistically significant different from each other. This could result when Drug X is statistically different from placebo at a probability level of p < .04, but Drug Y is statistically significantly different from a placebo only at a probability level of p < .06, which is higher than most a priori levels used to test for statistical significance. If just reading about this makes your head hurt, you are among the masses who believe they understand this critical shorthand phrase which is at the heart of the scientific method, but actually may have a shallow-level of understanding.

There are many critically important ways that findings of "statistically significant difference" can be misleading. But, even though there are real problems with understanding this term, it is firmly entrenched in everyday discussions of research, and for the general public, it shows some knowledge of the process of science.

A better understanding of the pitfalls associated with this term would go a long way toward improving our "cognitive toolkits." If common knowledge of what this term means included the ideas that a) the findings may not be important and b) conclusions based on finding or failure to find statistically significant differences may be wrong, then we would have significantly advanced general knowledge. When people read or use the term "statistically significant difference," it is an affirmation of the scientific process, which, for all of its limitations and misunderstandings, is a significant advance over alternative ways of knowing about the world. If we could just add two more key concepts to the meaning of that phrase, we could improve how the general public thinks about science.

The Dece(i)bo Effect — think portmanteau of Deceive and Placebo — refers to the facile application of constructs, without unpackaging the concept and the assumptions on which it relies, in a fashion that, rather than benefiting thinking, leads reasoning astray.  

Words and phrases enter common parlance, that capture a concept: Occam's razor, placebo, Hawthorne effect. Such phrases and code-words in principle facilitate discourse — and can indeed do so. Deploying the word or catchphrase adds efficiency to the interchange, by obviating the need for pesky review of the principles and assumptions encapsulated in the word.

Unfortunately, bypassing the need to articulate the conditions and assumptions on which validity of the construct rests, may lead to bypassing consideration of whether these conditions and assumptions legitimately apply. Use of the term can then, far from fostering sound discourse, serve to undermine it.

Take, for example, the "placebo," and "placebo effects." Unpackaging the terms, a "placebo" is in principle something that is physiologically "inert" — but believed by the recipient to be active, or possibly so. The term "placebo effect" refers to improvement of a condition when persons have been placed on a placebo, due to effects of expectation/suggestion.

With these terms well ensconced in the vernacular, Dece(i)bo Effects associated with them are much in evidence.  Key presumptions regarding placebos and placebo effects are more typically wrong than not.

1. When hearing the word "placebo," scientists often presume "inert" - without stopping to ask: what is that allegedly physiologically inert substance? Indeed, even in principle, what could it be??

There isn't anything known to be physiologically inert. There are no regulations about what constitute placebos; and their composition — commonly determined by the manufacturer of the drug under study — is typically undisclosed.  Among the uncommon cases where placebo composition has been noted, there are documented instances in which the placebo composition apparently produced spurious effects. Two studies used corn oil and olive oil placebos for cholesterol-lowering drugs: one noted that the "unexpectedly" low rate of heart attacks in the control group may have contributed to failure to see a benefit from the cholesterol drug. Another study noted "unexpected" benefit of a drug to gastrointestinal symptoms in cancer patients. But cancer patients bear increased likelihood of lactose intolerance — and the placebo was lactose, a "sugar pill." When the term "placebo" substitutes for actual ingredients, any thinking about how the composition of the control agent may have influenced the study is circumvented.

2. Because there are many settings in which persons with a problem, given placebo, report sizeable improvement on average when they are re-queried (see 3), many scientists have accepted that "placebo effects" — of suggestion — are both large in magnitude and widespread in the scope of what they benefit.

The Danish researcher Asbjørn Hróbjartsson conducted a systematic review of studies that compared a placebo to no treatment. He found that the placebo generally does: nothing. In most instances, there is no placebo effect. Mild "placebo effects" are seen, in the short term, for pain and anxiety. Placebo effects for pain are reported to be blocked by naloxone, an opiate antagonist — specifically implicating endogenous opiates in pain placebo effects, which would not be expected to benefit every possible outcome that might be measured.

3. When hearing that persons with a problem placed on a "placebo" report improvement, scientists commonly presume this must be due to the "placebo effect" - the effect of expectation/suggestion.

However, the effects are usually something else entirely. For instance: natural history of the disease, and regression to the mean. Consider a distribution, such as a bell-shape. Whether the outcome of interest is pain, blood pressure, cholesterol, or other, persons are classically selected for treatment if they are at one end of the distribution - say, the high end. But these outcomes are quantities that vary (for instance from physiological variation, natural history, measurement error...), and on average the high values will vary back down — a phenomenon termed "regression to the mean" that operates, placebo or no. (Hence, Hróbjartsson's findings.)

A different dece(i)bo problem beset Ted Kaptchuk's recent Harvard study in which researchers gave a "placebo," or nothing, to people afflicted with irritable bowel syndrome. They administered the placebo in a bottle boldly labeled "Placebo," and advised patients they were receiving placebos, which were known to be potent. The thesis was that one might harness the effects of expectation honestly, without deception, by telling subjects how powerful placebos in fact were - and by developing a close relationship with subjects. Researchers met repeatedly with subjects, gained subjects' appreciation for their concern and listening (as the researchers made clear), and repeatedly told subjects that placebos are powerful. Those placed on placebo obliged the researchers by telling them they had gotten better, moreso than those on nothing. The scientists attributed this to a placebo effect.

But what's to say patients weren't simply telling the scientists what they thought the scientists wished to hear? Such desire to please (a form, perhaps, of "social approval" reporting bias) had fertile grounds in which to operate and create what was interpreted as a placebo effect — which implies actual subjective benefit to symptoms. One wonders if so great an error of presumption would operate were there not an existing term, "placebo effect," to signify the interpretation the Harvard group chose.

Another explanation consistent with these results is specific physiological benefit. The study used a nonabsorbed fiber — microcrystalline cellulose — as the "Placebo" that subjects were told would be effective. The authors are applauded for disclosing its composition. But other nonabsorbed fibers benefit both constipation and diarrhea — symptoms of irritable bowel — and are prescribed for that purpose; psyllium is an example. Thus, specific physiological benefit of the "Placebo" to symptoms cannot be excluded.

Together these points illustrate that the term "placebo" cannot be presumed to imply "inert" (and generally does not); and that when studies see large benefit to symptoms in patients treated with "placebo" (expected from distribution considerations alone), one cannot infer these arose from large benefits of suggestion to symptoms (which evidence indicates may seldom operate).

Thus, rather than facilitating sound reasoning, evidence suggests that in many cases, including high stakes settings in which inferences may propagate to medical practice, substitution of a term — here, "placebo," "placebo effect" — for the concepts they are intended to convey, may actually thwart or bypass critical thinking about key issues, with implications to fundamental concerns for us all.

Physicists created the term "phase transition" to describe a change of state in a physical system, such as liquid to gas. The concept has since been applied in a variety of academic circles to describe other types of systems, from social transformations (think hunter-gatherer to farmer) to statistics (think abrupt changes in algorithm performance as parameters change), but has not yet emerged as part of the common lexicon.

One interesting aspect of the concept of the phrase transition is that it describes a shift to a state seemingly unrelated to the previous one, and hence provides a model for phenomena that challenge our intuition. With only knowledge of water as a liquid, who would have imagined a conversion to gas with the application of heat? The mathematical definition of a phase transition in the physical context is well-defined, but even without this precision I argue this idea can be usefully extrapolated to describe a much broader class of phenomena today, particularly those that change abruptly and unexpectedly with an increase in scale.

Imagine points in 2 dimensions — a spray of dots on a sheet of paper. Now imagine a point cloud in three dimensions, say, dots hovering in the interior of a cube. Even if we could imagine points in four dimensions would we have guessed that all these points lie on the convex hull of this point cloud? In dimensions greater than three they always do. There hasn't been a phase transition in the mathematical sense, but as dimension is scaled up the system shifts in a way we don't intuitively expect.

I call these types of changes "scale transitions:" unexpected outcomes resulting from increases in scale. For example, increases in the number of people interacting in a system can produce unforeseen outcomes: the operation of markets at large scales is often counterintuitive, think of the restrictive effect rent control laws can have on the supply of affordable rental housing or how minimum wage laws can reduce the availability of low wage jobs (James Flynn gives  "markets" as an example of a "shorthand abstraction," here I am interested in the often counterintuitive operation of a market system at large scale); the serendipitous effects of enhanced communication, for example collaboration and interpersonal connection generating unexpected new ideas and innovation; or the counterintuitive effect of massive computation in science reducing experimental reproducibility as data and code have proved harder to share than their descriptions. The concept of the scale transition is purposefully loose, designed as a framework for understanding when our natural intuition leads us astray in large scale situations.

This contrasts from Merton's concept of "unanticipated consequences" in that a scale transition refers both to a system, rather than individual purposeful behavior, and is directly tied to the notion of changes due to scale increases. Our intuition regularly seems to break down with scale and we need a way of conceptualizing the resulting counterintuitive shifts in the world around us. Perhaps the most salient feature of the digital age is its facilitation of massive increases in scale, in data storage, processing power, connectivity, thus permitting us to address an unparalleled number of problems on an unparalleled scale. As technology becomes increasingly pervasive I believe scale transitions will become commonplace.

We need to extend our time-horizons. Especially, we need deeper and wider awareness that far more time lies ahead than has elapsed up till now.

Our present biosphere is the outcome of more than four billion years of evolution; and we can trace cosmic history right back to a "big bang" that happened about 13.7 billion years ago. The stupendous time-spans of the evolutionary past are now part of common culture and understanding — even though the concept may not yet have percolated all parts of Kansas, and Alaska.

But the immense time-horizons that stretch ahead — though familiar to every astronomer — haven't permeated our culture to the same extent. Our Sun is less than half way through its life. It formed 4.5 billion years ago, but it's got 6 billion more before the fuel runs out. It will then flare up, engulfing the inner planets and vaporising any life that might then remain on Earth. But even after the Sun's demise, the expanding universe will continue — perhaps for ever — destined to become ever colder, ever emptier. That, at least, is the best long range forecast that cosmologists can offer, though few would lay firm odds on what may happen beyond a few tens of billions of years.

Awareness of the "deep time" lying ahead is still not pervasive. Indeed, most people — and not only those for whom this view is enshrined in religious beliefs —envisage humans as in some sense the culmination of evolution. But no astronomer could believe this; on the contrary, it would be equally plausible to surmise that we are not even at the halfway stage. There is abundant time for posthuman evolution, here on Earth or far beyond, organic or inorganic, to give rise to far more diversity, and even greater qualitative changes, than those that have led from single-celled organisms to humans. Indeed this conclusion is strengthened when we realise that future evolution will proceed not on the million-year timescale characteristic of Darwinian selection, but at the much accelerated rate allowed by genetic modification and the advance of machine intelligence (and forced by the drastic environmental pressures that would confront any humans who were to construct habitats beyond the Earth.

Darwin himself realised that "No living species will preserve its unaltered likeness into a distant futurity". We now know that "futurity" extends far further, and alterations can occur far faster — than Darwin envisioned. And we know that the cosmos, through which life could spread, is far more extensive and varied than he envisaged. So humans are surely not the terminal branch of an evolutionary tree, but a species that emerged early in cosmic history, with special promise for diverse evolution. But this is not to diminish their status. We humans are entitled to feel uniquely important as the first known species with the power to mould its evolutionary legacy.

To sustain progress on a finite planet that is increasingly under human sway, but also full of surprises, what is needed is a strong dose ofanthropophilia. I propose this word as shorthand for a rigorous and dispassionate kind of self regard, even self appreciation, to be employed when individuals or communities face consequential decisions attended by substantial uncertainty and polarizing disagreement.
 
The term is an intentional echo of Ed Wilson's valuable effort to nurturebiophilia, the part of humanness that values and cares for the facets of the non-human world we call nature. What's been missing too long is an effort to fully consider, even embrace, the human role within nature and — perhaps more important still — to consider our own inner nature, as well.
 
Historically, many efforts to propel a durable human approach to advancement were shaped around two organizing ideas: "woe is me" and "shame on us," with a good dose of "shame on you" thrown in.
 
The problem?
 
Woe is paralytic, while blame is both divisive and often misses the real target. (Who's the bad guy, BP or those of us who drive and heat with oil?)
 
Discourse framed around those concepts too often produces policy debates that someone once described to me, in the context of climate, as "blah, blah, blah bang." The same phenomenon can as easily be seen in the unheeded warnings leading to the most recent financial implosion and the attack on the World Trade Center.
 
More fully considering our nature — both the "divine and felonious" sides, as Bill Bryson has summed us up — could help identify certain kinds of challenges that we know we'll tend to get wrong.
 
The simple act of recognizing such tendencies could help refine how choices are made — at least giving slightly better odds of getting things a little less wrong the next time.  At the personal level, I know when I cruise into the kitchen tonight I'll tend to prefer to reach for a cookie instead of an apple. By pre-considering that trait, I might have a slightly better chance of avoiding a couple of hundred unnecessary calories.
 
Here are a few instances where this concept is relevant on larger scales.
 
There's a persistent human pattern of not taking broad lessons from localized disasters. When China's Sichuan province was rocked by a severe earthquake, tens of thousands of students (and their teachers) died in collapsed schools. Yet the American state of Oregon, where more than a thousand schools are already known to be similarly vulnerable when the great Cascadia fault off the Northwest Coast next heaves, still lags terribly in speeding investments in retrofitting.
 
Sociologists understand with quite a bit of empirical backing why this disconnect exists even though the example was horrifying and the risk in Oregon is about as clear as any scientific assessment can be. But does that knowledge of human biases toward the "near and now" get taken seriously in the realms where policies are shaped and the money to carry them out is authorized? Rarely, it seems.
 
Social scientists also know, with decent rigor, that the fight over human-driven global warming — both over the science and policy choices — is largely cultural. As in many other disputes (consider health care) the battle is between two quite fundamental subsets of human communities — communitarians (aka, liberals) and individualists (aka, libertarians). In such situations, a compelling body of research has emerged showing how information is fairly meaningless. Each group selects information to reinforce a position and there are scant instances where information ends up shifting a position.
 
That's why no one should expect the next review of climate science from the Intergovernmental Panel on Climate Change to suddenly create a harmonious path forward.
 
The more such realities are recognized, the more likely it is that innovative approaches to negotiation can build from the middle, instead of arguing endlessly from the edge. The same body of research on climate attitudes, for example, shows far less disagreement on the need for advancing the world's limited menu of affordable energy choices.
 
Murray Gell-Mann has spoken often of the need, when faced with multi-dimensional problems, to take a "crude look at the whole" — a process he has even given an acronym, CLAW. It's imperative, where possible, for that look to include an honest analysis of the species doing the looking, as well.
 
There will never be a way to invent a replacement for, say, the United Nations or the House of Representatives. But there is a ripe opportunity to try new approaches to constructive discourse and problem solving, with the first step being an acceptance of our humanness, for better and worse.
 
That's anthropophilia.

Since different visiting teachers had promoted contradictory philosophies, the villagers asked the Buddha whom they should believe. The Buddha advised: “When you know for yourselves ... these things, when performed and undertaken, conduce to well-being and happiness — then live and act accordingly.” Such empirical advice might sound surprising coming from a religious leader, but not from a scientist.

“See for yourself” is an unspoken credo of science. It is not enough to run an experiment and report the findings. Others who repeat that experiment must find the same thing. Repeatable experiments are called “replicable.” Although scientists implicitly respect replicability, they do not typically explicitly reward it.

To some extent, ignoring replicability comes naturally. Human nervous systems are designed to respond to rapid changes, ranging from subtle visual flickers to pounding rushes of ecstasy. Fixating on fast change makes adaptive sense — why spend limited energy on opportunities or threats that have already passed? But in the face of slowly growing problems, “change fixation” can prove disastrous (think of lobsters in the cooking pot or people under greenhouse gases).

Cultures can also promote change fixation. In science, some high profile journals and even entire fields emphasize novelty, consigning replications to the dustbin of the unremarkable and unpublishable. More formally, scientists are often judged based on their work’s novelty rather than replicability. The increasingly popular “h-index” quantifies impact by assigning a number (h) which indicates that an investigator has published h papers that have been cited h or more times (so, Joe Blow has an h-index of 5 if he has published 5 papers, each of which others have cited 5 or more times). While impact factors correlate with eminence in some fields (e.g., physics), problems can arise. For instance, Doctor Blow might boost his impact factor by publishing controversial (thus, cited) but unreplicable findings.

Why not construct a replicability (or “r”) index to complement impact factors? As with h, r could indicate that a scientist has originally documented r separate effects that independently replicate r or more times (so, Susie Sharp has an r-index of 5 if she has published 5 independent effects, each of which others have replicated 5 or more times). Replication indices would necessarily be lower than citation indices, since effects have to first be published before they can be replicated, but might provide distinct information about research quality. As with citation indices, replication indices might even apply to journals and fields, providing a measure that can combat biases against publishing and publicizing replications.

A replicability index might prove even more useful to nonscientists. Most investigators who have spent significant time in the salt mines of the laboratory already intuit that most ideas don’t pan out, and those that do sometimes result from chance or charitable interpretations. Conversely, they also recognize that replicability means they’re really on to something. Not so for the general public, who instead encounter scientific advances one cataclysmic media-filtered study at a time. As a result, laypeople and journalists are repeatedly surprised to find the latest counterintuitive finding overturned by new results. Measures of replicability could help channel attention towards cumulative contributions. Along these lines, it is interesting to consider applying replicability criteria to public policy interventions designed to improve health, enhance education, or curb violence. Individuals might even benefit from using replicability criteria to optimize their personal habits (e.g., more effectively dieting, exercising, working, etc.).

Replication should be celebrated rather than denigrated. Often taken for granted, replicability may be the exception rather than the rule. As running water resolves rock from mud, so can replicability highlight the most reliable findings, investigators, journals, and even fields. More broadly, replicability may provide an indispensable tool for evaluating both personal and public policies. As suggested in the Kalama Sutta, replicability might even help us decide whom to believe.

The ability to reason clearly in the face of uncertainty.  

If everybody could learn to deal better with the unknown, then it would improve not only their individual cognitive toolkit (to be placed in a slot right next to the ability to operate a remote control, perhaps), but the chances for humanity as a whole.

A well-developed scientific method for dealing with the unknown has existed for many years — the mathematical theory of probability. Probabilities are numbers whose values reflect how likely different events are to take place. People are bad at assessing probabilities. They are bad at it not just because they are bad at addition and multiplication.  Rather, people are bad at probability in a deep, intuitive level: they overestimate the probability of rare but shocking events -- a burglar breaking into your bedroom while you're asleep, say. Conversely, they underestimate the probability of common, but quiet and insidious events   — the slow accretion of globules of fat on the walls of an artery, or another ton of carbon dioxide pumped into the atmosphere.  

I can't say that I'm very optimistic about the odds that people will learn to understand the science of odds. When it comes to understanding probability, people basically suck. Consider the following example, based on a true story, and reported by Joel Cohen of Rockefeller University. A group of graduate students note that women have an significantly lower chance of admission than men to the graduate programs at a major university. The data are unambiguous: women applicants are only two thirds as likely as male applicants to be admitted. The graduate students file suit against the university, alleging discrimination on the basis of gender.  When admissions data are examined on a department by department basis, however, a strange fact emerges: within each department, women are MORE likely to be admitted than men. How can this possibly be?  

The answer turns out to be simple, if counterintuitive. More women are applying to departments that have few positions. These departments admit only a small percentage of applicants, men or women. Men, by contrast, are applying to departments that have more positions and that admit a higher percentage of applicants. Within each department, women have a better chance of admission than men — it's just that few women apply to the departments that are easy to get into.  

This counterintuitive result indicates that the admissions committees in the different departments are not discriminating against women. That doesn't mean that bias is absent. The number of graduate fellowships available in a particular field is determined largely by the federal government, which chooses how to allocate reserach funds to different fields. It is not university that is guilty of sexual discrimination, but the society as a whole, which chose to devote more resources — and so more graduate fellowships — to the fields preferred by men.

Of course, some people are good at probability. A car insurance company that can't accurately determine the probabilities of accidents will go broke. In effect, when we pay out premiums to insure ourselves against a rare event, we are buying into the insurance company's estimate of just how likely that event is. Driving a car is one of those common but dangerous processes where human beings habitually understimate the odds of something bad happening, however. Accordingly, some are disinclined to obtain car insurance (perhaps not suprising when the considerable majority of people rate themselves as better than average drivers). When a state government requires its citizens to buy car insurance, it does so because it figures, rightly, that people are underestimating the odds of an accident.  

Let's consider the debate over whether health insurance should be required by law. Living, like driving, is a common but dangerous process where people habitually underestimate risk, despite that fact that, with probability equal to one, living is fatal.

Like others whose early life experiences were punctuated with trauma, my memory has holes. Some of those holes are as wide as years. Others, just big enough to swallow painful incidents that lasted moments, but reverberated for decades.

The brain-record of those experiences sometimes submerges, then resurfaces, sometimes submerging again over time. As I grow older, stronger, and more capable of contending with memory, I become more aware of how different my own internal record may be from others who lived the identical moment.

Each of us commit our experiences to memory and permanence differently. Time and human experience are not linear, nor is there one and only one neutral record of each lived moment. Human beings are impossibly complex tarballs of muscle, blood, bone, breath, and electrical pulses that travel through nerves and neurons; we are bundles of electrical pulses carrying payloads, pings hitting servers. And our identities are inextricably connected to our environments: no story can be told without a setting.

My generation is the last generation of human beings who were born into a pre-internet world, but who matured in tandem with that great, networked hive-mind. In the course of my work online, committing new memories to network mind each day, I have come to understand that our shared memory of events, of truths, of biography, and of fact-- all of this shifts and ebbs and flows, just as our most personal memories do.

Ever-edited Wikipedia replaces paper encyclopedias. The chatter of Twitter eclipses fixed-form and hierarchical communication. The news flow we remember from our childhoods, a single voice of authority on one of three channels, is replaced by something hyper-evolving, chaotic, and less easily defined. Even the formal histories of State may be rewritten by the likes of Wikileaks, and its yet-unlaunched children.

Facts are more fluid than in the days of our grandfathers. In our networked mind, the very act of observation--reporting or tweeting or amplifying some piece of experience--changes the story. The trajectory of information, the velocity of this knowledge on the network, changes the very nature of what is remembered, who remembers it, and for how long it remains part of our shared archive. There are no fixed states.

So must our notion of memory and record evolve.

The history we are creating now is alive. Let us find new ways of recording memory, new ways of telling the story, that reflect life. Let us embrace this infinite complexity as we commit new history to record.

Let us redefine what it means to remember.

The concept of cause and effect is better understood as the flow of information between two connected events, from the earlier event to the later one. Saying "A causes B" sounds precise, but is actually very vague. I would specify much more by saying "with the information that A has happened, I can compute with almost total confidence* that B will happen." The latter rules out the possibility that other factors could prevent B even if A does happen, but allows the possibility that other factors could cause B even if A doesn't happen.

As shorthand, we can say that one set of information "specifies" another if the latter can be deduced or computed from the former.  Note that this doesn't only apply to one-bit sets of information, like the occurrence of a specific event. It can also apply to symbolic variables (given the state of the Web, the results you get from a search engine are specified by your query), numeric variables (the number read off a precise thermometer is specified by the temperature of the sensor), or even behavioral variables (the behavior of a computer is specified by the bits loaded in its memory).

But let's take a closer look at the assumptions we're making. Astute readers may have noticed that in one of my examples, I assumed that the entire state of the Web was a constant. How ridiculous! In mathematical parlance, assumptions are known as "priors," and in a certain widespread school of statistical thought, they are considered the most important aspect of any process involving information. What we really want to know is if, given a set of existing priors, adding one piece of information (A) would allow us to update our estimate of the likelihood of another piece of information (B). Of course, this depends on the priors — for instance, if our priors include absolute knowledge of B, then an update will not be possible.

If, for most reasonable sets of priors, information about A would allow us to update our estimate of B, then it would seem there is some sort of causal connection between the two. But the form of the causal connection is unspecified — a principle often called "correlation does not imply causation." The reason for this is that the essence of causation as a concept rests on our tendency to have information about earlier events before we have information about later events. (The full implications of this concept on human consciousness, the second law of thermodynamics, and the nature of time are interesting, but sadly outside the scope of this essay.)

If information about all events always came in the order they occurred, then correlation would indeed imply causation. But, in the real world, not only are we limited to observing events in the past, but we may also discover information about those events out of order.  Thus, the correlations we observe could be reverse causes (information about A allows us to update our estimate of B, although B happened first and thus was the cause of A) or even more complex situations (e.g. information about A allows us to update our estimate of B, but is also giving us information about C, which happened before either A or B and caused both).

Information flow is symmetric: if information about A were to allow us to update our estimate of B, then information about B would allow us to update our estimate of A. But since we cannot change the past or know the future, these constraints are only useful to us when contextualized temporally and arranged in order of occurrence. Information flow is always from the past to the future, but in our minds, some of the arrows may be reversed. Resolving this ambiguity is essentially the problem that science was designed to solve. If you can master the technique of visualizing all information flow and keeping track of your priors, then the full power of the scientific method — and more — is yours to wield from your personal cognitive toolkit.

* In our universe, too many things are interconnected for absolute statements of any kind, so we usually relax our criteria; for instance, "total confidence" might be relaxed from a 0% chance of being wrong to, say, a 1 in 3 quadrillion chance of being wrong — about the chance that, as you finish this sentence, all of humanity will be wiped out by a meteor.

We humans are terrible at dealing with probability. We are not merely bad at it, but seem hardwired to be incompetent, in spite of the fact that we encounter innumerable circumstances every day which depend on accurate probabilistic calculations for our wellbeing. This incompetence is reflected in our language, in which the common words used to convey likelihood are "probably" and "usually" — vaguely implying a 50% to 100% chance. Going beyond crude expression requires awkwardly geeky phrasing, such as "with 70% certainty," likely only to raise the eyebrow of a casual listener bemused by the unexpected precision. This blind spot in our collective consciousness — the inability to deal with probability — may seem insignificant, but it has dire practical consequences. We are afraid of the wrong things, and we are making bad decisions.

Imagine the typical emotional reaction to seeing a spider: fear, ranging from minor trepidation to terror. But what is the likelihood of dying from a spider bite? Fewer than four people a year (on average) die from spider bites, establishing the expected risk of death-by-spider at lower than one in a hundred million. This risk is so minuscule that it is actually counterproductive to worry about it! Millions of people die each year from stress-related illnesses.

The startling implication is that the risk of being bitten and killed by a spider is less than the risk that being afraid of spiders will kill you from increased stress. Our irrational fears and inclinations are costly. The typical reaction to seeing a sugary donut is the desire to consume it. But, given the potential negative impact of that donut, including the increased risk of heart disease and reduction in overall health, our reaction should rationally be one of fear and revulsion. It may seem absurd to fear a donut — or, even more dangerous, a cigarette — but this reaction rationally reflects the potential negative impact on our lives.

We are especially ill-equipped to manage risk when dealing with small likelihoods of major events. This is evidenced by the success of lotteries and casinos at taking peoples' money, but there are many other examples. The likelihood of being killed by terrorism is extremely low, yet we have instituted actions to counter terrorism that significantly reduce our quality of life. As a recent example, x-ray body scanners could increase the risk of cancer to a degree greater than the risk from terrorism — the same sort of counterproductive overreaction as the one to spiders. This does not imply we should let spiders, or terrorists, crawl all over us — but the risks need to be managed rationally.

Socially, the act of expressing uncertainty is a display of weakness. But our lives are awash in uncertainty, and rational consideration of contingencies and likelihoods is the only sound basis for good decisions. As another example, a federal judge recently issued an injunction blocking stem cell research funding. The shallowly viewed implication is that some scientists won't be getting money; but what is really at stake is much more important. The probability that stem cell research will quickly lead to life saving medicine is low, but, if successful, the positive impact could be huge. If one considers outcomes and approximates the probabilities, the conclusion is that the judge's decision destroyed the lives of thousands of people, based on probabilistic expectation.

How do we make rational decisions based on contingencies? That judge didn't actually cause thousands of people to die... or did he? If we follow the "many worlds" interpretation of quantum physics — the most direct interpretation of its mathematical description — then our universe is continually branching into all possible contingencies, and there is a world in which stem cell research saves millions of lives, and another world in which people die because of the judge's decision. Using the "frequentist" method of calculating probability, we have to add the probabilities of the worlds in which an event occurs to obtain the probability of that event. 

Quantum mechanics dictates that the world we experience will happen according to this probability — the likelihood of the event. In this bizarre way, quantum mechanics reconciles the frequentist and "Bayesian" points of view, equating the frequency of an event over many possible worlds with its likelihood. An "expectation value," such as the expected number of people killed by the judge's decision, is the number of people killed in the various contingencies, weighted by their probabilities. This expected value is not necessarily likely to happen, but is the weighted average of the expected outcomes — useful information when making decisions. In order to make good decisions about risk we need to become better at these mental gymnastics, improve our language, and retrain our intuition.

Perhaps the best arena for honing our skills and making precise probabilistic assessments would be a betting market — an open site for betting on the outcomes of many quantifiable and socially significant events. In making good bets, all the tools and shorthand abstractions of Bayesian inference come into play — translating directly to the ability to make good decisions. With these skills, the risks we face in everyday life would become clearer, and we would develop more rational intuitive responses to uncalculated risks, based on collective rational assessment and social conditioning.

We might get over our excessive fear of spiders, and develop a healthy aversion to donuts, cigarettes, television, and stressful full-time employment. We would become more aware of the low cost compared to probable rewards of research, including research into improving the quality and duration of human life. And, more subtly, as we became more aware and apprehensive of ubiquitous vague language, such as "probably" and "usually," our standards of probabilistic description would improve.

Making good decisions requires concentrated mental effort; and if we overdo it we run the risk of being counterproductive through increased stress and wasted time. So it's best to balance, and play, and take healthy risks — as the greatest risk is that we'll get to the end of our lives having never risked them on anything.

The Gibbs Landscape

Biology is rarely wasteful. Sure, on the individual organism level there is plenty of waste involved with reproduction and other activities (think of all the fruit on a tree or the millions of sperm that loose out in the race to the egg). But on the ecosystem level one bug's trash is another bug's treasure — provided that some useful energy can still be extracted by reacting that trash with something else in the environment. The food chain is not a simple linear staircase of predator-prey relationships; it is a complex fabric of organisms large, small, and microscopic interacting with each other and with the environment to tap every possible energetic niche.

Geobiologists and astrobiologists can measure and map this energy —  referred to as the Gibbs free energy. Doing so is useful for assessing the energetic limits of life on Earth and for assessing potentially habitable regions on other worlds. In an ecosystem Gibbs free energy —  named for it's discoverer, the late 19th century scientist J. Willard Gibbs —  is the energy in a biochemical reaction that is available to do work. It's the energy left over after producing some requisite waste heat and a dollop or two of entropy. This energy to do work is harnessed by biological systems for activities like making repairs, growing, and reproducing. For a given metabolic pathway used by life, e.g. reacting carbohydrates with oxygen, we can measure how many Joules are available to do work per mole of reactants. Humans and essentially all the animals you know and love typically harness a couple thousand kiloJoules per mole by burning food with oxygen. Microbes have figured out all sorts of ways to harness the Gibbs free energy by combining various gases, liquids, and rocks. Measurements by Tori Hoehler and colleagues at NASA Ames Research Center on methane-generating and sulfate-eating microbes indicate that the limit for life may be about 10 kiloJoules per mole. Within a given environment there may be many chemical pathways in operation and if there is an open energetic niche, chances are life will find a way to fill it. Biological ecosystems can be mapped as a landscape of reactions and pathways for harnessing energy; this is the Gibbs landscape.

Civilizations and the rise of industrial and technological ecosystems bring a new challenge to our understanding of the dynamic between energy needs and energy resources. The Gibbs landscape provides a short-hand abstraction for conceptualizing this dynamic. We can imagine any given city, country, or continent overlain with a map of energy available to do work. This includes, but extends beyond the chemical energy framework used in the context of biological ecosystems. For instance, automobiles with internal combustion engines metabolize gasoline with air. Buildings eat the electricity supplied by power plants or rooftop solar panels. Every component in modern industrial society occupies some niche in the landscape.

But importantly, many of the Gibbs landscapes in place today are ripe with unoccupied niches. The systems we have designed and built are inefficient and incomplete in the utilization of energy to do the work of civilization's ecosystems. Much of what we have designed excels at producing waste heat with little concern for optimizing work output. From lights that remain on all night to landfills that contain discarded resources, the Gibbs landscapes of today offer much room for technological innovation and evolution. The Gibbs landscape also provides a way for visualizing untapped capacity to do work — wind, solar, hydroelectric, tides and geothermal, these are just a few of the layers. Taken together, all of these layers show us where and how we can work to close the loops and connect the dangling threads of our nascent technological civilization.

When you start to view the world around you with Gibbsian eyes you see the untapped potential in so many of our modern technological and industrial ecosystems. It's disturbing at first because we've done such a poor job, but the marriage between civilization and technology is young. The landscape provides much reason for hope as we continue to innovate and strive to reach the balance and continuity that has served complex biological ecosystems so well for billions of years on Earth.

We are shockingly ignorant of the causes of our own behavior. The explanations that we provide are sometimes wholly fabricated, and certainly never complete. Yet, that is not how it feels. Instead it feels like we know exactly what we're doing and why. This is confabulation: Guessing at plausible explanations for our behavior, and then regarding those guesses as introspective certainties. Every year psychologists use dramatic examples to entertain their undergraduate audiences. Confabulation is funny, but there is a serious side, too. Understanding it can help us act better and think better in everyday life.

Some of the most famous examples of confabulation come "split-brain" patients, whose left and right brain hemispheres have been surgically disconnected for medical treatment. Neuroscientists have devised clever experiments in which information is provided to the right hemisphere (for instance, pictures of naked people), causing a change in behavior (embarrassed giggling). Split-brain individuals are then asked to explain their behavior verbally, which relies on the left hemisphere. Realizing that their body is laughing, but unaware of the nude images, the left hemisphere will confabulate an excuse for the body's behavior ("I keep laughing because you ask such funny questions, Doc!").

Wholesale confabulations in neurological patients can be jaw-dropping, but in part that is because they do not reflect ordinary experience. Most of the behaviors that you or I perform are not induced by crafty neuroscientists planting subliminal suggestions in our right hemisphere. When we are outside the laboratory — and when our brains have all the usual connections — most behaviors that we perform are the product of some combination of deliberate thinking and automatic action.

Ironically, that is exactly what makes confabulation so dangerous. If we routinely got the explanation for our behavior totally wrong — as completely wrong as split-brain patients sometimes do — we would probably be much more aware that there are pervasive, unseen influences on our behavior. The problem is that we get all of our explanations partly right, correctly identifying the conscious and deliberate causes of our behavior. Unfortunately, we mistake "party right" for "completely right", and thereby fail to recognize the equal influence of the unconscious, or to guard against it.

A choice of job, for instance, depends partly on careful deliberation about career interests, location, income, and hours. At the same time, research reveals that choice to be influenced by a host of factors of which we are unaware. People named Dennis or Denise are more likely to be dentists, while people named Virginia are more likely to locate to (you guessed it) Virginia. Less endearingly, research suggests that on average people will take a job with fewer benefits, a longer commute and a smaller income if it allows them to avoid having a female boss. Surely most people do not want to choose a job based on the sound of their name, nor do they want to sacrifice job quality in order to perpetuate old gender norms. Indeed, most people have no awareness that these factors influence their own choices. When you ask them why they took the job, they are likely to reference their conscious thought processes: "I've always loved making ravioli, the Lira is on the rebound and Rome is for lovers…"  That answer is partly right, but it is also partly wrong, because it misses the deep reach of automatic processes on human behavior.

People make harsher moral judgments in foul-smelling rooms, reflecting the role of disgust as a moral emotion. Women are less likely to call their fathers (but equally likely to call their mothers) during the fertile phase of their menstrual cycle, reflecting a means of incest avoidance. Students indicate greater political conservatism when polled near a hand-sanitizing station during a flu epidemic, reflecting the influence of a threatening environment on ideology. They also indicate a closer bond to their mother when holding hot coffee versus iced coffee, reflecting the metaphor of a "warm" relationship.

Automatic behaviors can be remarkably organized, and even goal-driven. For example, research shows that people tend to cheat just as much as they can without realizing that they are cheating. This is a remarkable phenomenon: Part of you is deciding how much to cheat, calibrated at just the level that keeps another part of you from realizing it.

One of the ways that people pull off this trick is with innocent confabulations: When self-grading an exam, students think, "Oh, I was going to circle e, I really knew that answer!" This isn't a lie, any more than it's a lie to say you have always loved your mother (latte in hand), but don't have time to call your dad during this busy time of the month. These are just incomplete explanations, confabulations that reflect our conscious thoughts while ignoring the unconscious ones.

This brings me to the central point, the part that makes confabulation an important concept in ordinary life and not just a trick pony for college lectures. Perhaps you have noticed that people have an easier time sniffing out unseemly motivations for other's behavior than recognizing the same motivations for their own behavior. Others avoided female bosses (sexist) and inflated their grades (cheaters), while we chose Rome and really meant to say that Anne was the third Brontë. There is a double tragedy in this double standard.

First, we jump to the conclusion that others' behaviors reflect their bad motives and poor judgment, attributing conscious choice to behaviors that may have been influenced unconsciously. Second, we assume that our own choices were guided solely by the conscious explanations that we conjure, and reject or ignore the possibility of our own unconscious biases.

By understanding confabulation we can begin to remedy both faults. We can hold others responsible for their behavior without necessarily impugning their conscious motivations. And, we can hold ourselves more responsible by inspecting our own behavior for its unconscious influences, as unseen as they are unwanted.

It's been over 150 years since Charles Darwin published the Origin of Species, but we still have trouble appreciating the simple, brilliant insight at its core. That is, life's diversity does not exist because it is necessary for living things. Birds did not get wings so that they could fly. We do not have eyes so that we can read. Instead, eyes, wings, and the rest of life's wonder has come about as a side effect of life itself. Living things struggle to survive, they reproduce, and they don't do a perfect job of replicating themselves. Evolution spins off of that loop, like heat coming off an engine. We're so used to seeing agents behind everything that we struggle to recognize life as a side effect. I think everyone would do well do overcome that urge to see agents where there are none. It would even help us to understand why we are so eager to see agents in the first place.

We could become far more intelligent than we are by adding to our stock of concepts, and by forcing ourselves to use them even when we don't like what they are telling us. This will be nearly always, because they generally tell us that our self-evidently superior selves and ingroups are error-besotted. We all start from radical ignorance in a world that is endlessly strange, vast, complex, intricate, and surprising. Deliverance from ignorance lies in good concepts — inference fountains that geyser out insights that organize and increase the scope of our understanding. We are drawn to them by the fascination of the discoveries they afford, but resist using them well and freely because they would reveal too many of our apparent achievements to be embarrassing or tragic failures. Those of us who are non-mythical lack the spine that Oedipus had — the obsidian resolve that drove him to piece together shattering realizations despite portents warning him off. Because of our weakness, "to see what is in front of one's nose needs a constant struggle" as Orwell says. So why struggle? Better instead to have one's nose and what lies beyond shift out of focus — to make oneself hysterically blind as convenience dictates, rather than to risk ending up like Oedipus, literally blinding oneself in horror at the harvest of an exhausting, successful struggle to discover what is true.

Alternatively, even modest individual-level improvements in our conceptual toolkit can have transformative effects on our collective intelligence, promoting incandescent intellectual chain reactions among multitudes of interacting individuals. If this promise of intelligence-amplification through conceptual tools seems like hyperbole, consider that the least inspired modern engineer, equipped with the conceptual tools of calculus, can understand, plan and build things far beyond what da Vinci or the mathematics-revering Plato could have achieved without it. We owe a lot to the infinitesimal, Newton's counterintuitive conceptual hack — something greater than zero but less than any finite magnitude. Far simpler conceptual innovations than calculus have had even more far reaching effects — the experiment (a danger to authority), zero, entropy, Boyle's atom, mathematical proof, natural selection, randomness, particulate inheritance, Dalton's element, distribution, formal logic, culture, Shannon's definition of information, the quantum…

Here are three simple conceptual tools that might help us see in front of our noses: nexus causality, moral warfare, and misattribution arbitrage. Causality itself is an evolved conceptual tool that simplifies, schematizes, and focuses our representation of situations. This cognitive machinery guides us to think in terms of the cause — of an outcome having a single cause. Yet for enlarged understanding, it is more accurate to represent outcomes as caused by an intersection or nexus of factors (including the absence of precluding conditions). InWar and Peace, Tolstoy asks "When an apple ripens and falls, why does it fall? Because of its attraction to the earth, because its stem withers, because it is dried by the sun, because it grows heavier, because the wind shakes it….?" — with little effort any modern scientist could extend Tolstoy's list endlessly. We evolved, however, as cognitively improvisational tool-users, dependent on identifying actions we could take that lead to immediate payoffs. So, our minds evolved to represent situations in a way that highlighted the element in the nexus that we could manipulate to bring about a favored outcome. Elements in the situation that remained stable and that we could not change (like gravity or human nature) were left out of our representation of causes. Similarly, variable factors in the nexus (like the wind blowing) that we could not control, but that predicted an outcome (the apple falling), were also useful to represent as causes, in order to prepare ourselves to exploit opportunities or avoid dangers. So the reality of the causal nexus is cognitively ignored in favor of the cartoon of single causes. While useful for a forager, this machinery impoverishes our scientific understanding, rendering discussions (whether elite, scientific, or public) of the "causes" — of cancer, war, violence, mental disorders, infidelity, unemployment, climate, poverty, and so on — ridiculous.

Similarly, as players of evolved social games, we are designed to represent others' behavior and associated outcomes as caused by free will (by intentions). That is, we evolved to view "man" as Aristotle put it, as "the originator of his own actions." Given an outcome we dislike, we ignore the nexus, and trace "the" causal chain back to a person. We typically represent the backward chain as ending in — and the outcome as originating in — the person. Locating the "cause" (blame) in one or more persons allows us to punitively motivate others to avoid causing outcomes we don't like (or to incentivize outcomes we do like). More despicably, if something happens that many regard as a bad outcome, this gives us the opportunity to sift through the causal nexus for the one thread that colorably leads back to our rivals (where the blame obviously lies). Lamentably, much of our species' moral psychology evolved for moral warfare, a ruthless zero-sum game. Offensive play typically involves recruiting others to disadvantage or eliminate our rivals by publically sourcing them as the cause of bad outcomes. Defensive play involves giving our rivals no ammunition to mobilize others against us.

The moral game of blame attribution is only one subtype of misattribution arbitrage. For example, epidemiologists estimate that it was not until 1905 that you were better off going to a physician. (Semelweiss noticed that doctors doubled the mortality rate of mothers at delivery). For thousands of years, the role of the physician pre-existed its rational function, so why were there physicians? Economists, forecasters, and professional portfolio managers typically do no better than chance, yet command immense salaries for their services. Food prices are driven up to starvation levels in underdeveloped countries, based on climate models that cannot successfully retrodict known climate history. Liability lawyers win huge sums for plaintiffs who get diseases at no higher rates than others not exposed to "the" supposed cause. What is going on? The complexity and noise permeating any real causal nexus generates a fog of uncertainty. Slight biases in causal attribution, or in blameworthiness (e.g., sins of commission are worse than sins of omission) allow a stable niche for extracting undeserved credit or targeting undeserved blame. If the patient recovers, it was due to my heroic efforts; if not, the underlying disease was too severe. If it weren't for my macroeconomic policy, the economy would be even worse. The abandonment of moral warfare, and a wider appreciation of nexus causality and misattribution arbitrage would help us all shed at least some of the destructive delusions that cost humanity so much.

From the dawn of civilisation until 2003, Eric Schmidt is fond of saying, humankind generated five exabytes of data. Now we produce five exabytes every two days — and the pace is accelerating. In our post-privacy world of pervasive social-media sharing, GPS tracking, cellphone-tower triangulation, wireless sensor monitoring, browser-cookie targeting, face-recognition detecting, consumer-intention profiling, and endless other means by which our personal presence is logged in databases far beyond our reach, citizens are largely failing to benefit from the power of all this data to help them make smarter decisions. It's time to reclaim the concept of data mining from the marketing industry's microtargeting of consumers, the credit-card companies' anti-fraud profiling, the intrusive surveillance of state-sponsored Total Information Awareness. We need to think more about mining our own output to extract patterns that turn our raw personal datastream into predictive, actionable information. All of us would benefit if the idea of personal data mining were to enter popular discourse.

Microsoft saw the potential back in September 2006, when it filed United States Patent application number 20,080,082,393 for a system of "personal data mining". Having been fed personal data provided by users themselves or gathered by third parties, the technology would then analyse it to "enable identification of opportunities and/or provisioning of recommendations to increase user productivity and/or improve quality of life". You can decide for yourself whether you trust Redmond with your lifelog, but it's hard to fault the premise: the personal data mine, the patent states, would be a way "to identify relevant information that otherwise would likely remain undiscovered".

Both I as a citizen and society as a whole would gain if individuals' personal datastreams could be mined to extract patterns upon which we could act. Such mining would turn my raw data into predictive information that can anticipate my mood and improve my efficiency, make me healthier and more emotionally intuitive, reveal my scholastic weaknesses and my creative strengths. I want to find the hidden meanings, the unexpected correlations that reveal trends and risk factors of which I had been unaware. In an era of oversharing, we need to think more about data-driven self-discovery.

A small but fast-growing self-tracking movement is already showing the potential of such thinking, inspired by Kevin Kelly's quantified self and Gary Wolf's data-driven life. With its mobile sensors and apps and visualisations, this movement is tracking and measuring exercise, sleep, alertness, productivity, pharmaceutical responses, DNA, heartbeat, diet, financial expenditure — and then sharing and displaying its findings for greater collective understanding. It is using its tools for clustering, classifying and discovering rules in raw data, but mostly is simply quantifying that data to extract signals — information — from the noise.

The cumulative rewards of such thinking will be altruistic rather than narcissistic, whether in pooling personal data for greater scientific understanding (23andMe) or in propagating user-submitted data to motivate behaviour change in others (Traineo). Indeed, as the work of Daniel Kahneman, Daniel Gilbert, and Christakis and Fowler demonstrate so powerfully, accurate individual-level data-tracking is key to understanding how human happiness can be quantified, how our social networks affect our behaviour, how diseases spread through groups.

The data is already out there. We just need to encourage people to tap it, share it, and corral it into knowledge.

Depth is what you do not see immediately at the surface of things. Depth is what is below that surface: a body of water below the surface of a lake, the rich life of a soil below the dirt or the spectacular line of reasoning behind a simple statement.

Depth is a straightforward aspect of the physical world. Gravity stacks stuff and not everything can be at the top. Below there is more and you can dig for it.

Depth acquired a particular meaning with the rise of complexity science a quarter of a century ago: What is characteristic of something complex? Very orderly things like crystals are not complex. They are simple. Very messy things like a pile of litter are very difficult to describe: They hold a lot of information. Information is a measure of how difficult something is to describe. Disorder has a high information content and order has a low one. All the interesting stuff in life is in-between: Living creatures, thoughts and conversations. Not a lot of information, but neither a little. So information content does not lead us to what is interesting or complex. The marker is rather the information that is not there, but was somehow involved in creating the object of interest. The history of the object is more relevant than the object itself, if we want to pin-point what is interesting to us.

It is not the informational surface of the thing, but its informational depth that attracts our curiosity. It took a lot to bring it here, before our eyes. It is not what is there, but what used to be there, that matters. Depth is about that.

The concept of depth in complexity science was expressed in different ways: You could talk about the actual amount of physical information that was involved in bringing about something — the thermodynamic depth — or the amount of computation it took to arrive at a result— the logical depth. Both express the notion that the process behind is more important than the eventual product.

This idea can also be applied to human communication.

When you say "yes" at a wedding it (hopefully) re-presents a huge amount of conversation, coexistence and fun that you have had with that other person present. And a lot of reflection upon it. There is not a lot of information in the "yes" (one bit, actually), but the statement has depth. Most conversational statements have some kind of depth: There is more than meets the ear, something that happened between the ears of the person talking — before a statement was made. When you understand the statement, the meaning of what is being said, you "dig it", you get the depth, what is below and behind. What is not said, but meant — the exformation content, information processed and thrown away before the actual production of explicit information.

2 + 2 = 4. This is a simple computation. The result, 4, hold less information than the problem, 2 + 2 (essentially because the problem could also have been 3 + 1 and yet the result would still be 4). Computation is wonderful as a method for throwing away information, getting rid of it. You do computations to ignore all the details, to get an overview, an abstraction, a result.

What you want is a way to distinguish between a very deep "yes" and a very shallow one: Did the guy actually think about what he said? Was the result 4 actually the result of a meaningful calculation? Is there in fact water below that surface? Does it have depth?

Most human interaction is about that question: Is this bluff or for real? Is there sincere depth in the affection? Does the result stem from intense analysis or is it just an estimate? Is there anything between the lines?

Signaling is all about this question: fake or depth? In biology the past few decades have seen the rise of studies of how animals prove to each other that there is depth behind the signal. The handicap principle of sexual selection is about a way to prove that you signal has depth: If a peacock has long, spectacular feathers it proves that it can survive its predators even though the fancy plumage represents a disadvantage, a handicap. Hence, the peahen can know that the individual displaying the huge tail is a strong one, or else it could not survive with that extreme tail.

Amongst humans you have what economists call costly signals: Ways to show that you have something of value. The phenomenon conspicuous consumption was observed by sociologist Thorstein Veblen already in 1899: If you want to prove that you have a lot of money, you have to waste them. That is: Use them in a way that is absurd and idiotic, because only the rich guy can do so. But do it conspicuously, so that other people will know. Waste is a costly signal of the depth of pile of money. Poor people have to use their money in functional way.

Handicaps, costly signals, intense eye contact and rhetorical gestures are all about proving that what seems so simple really has a lot of depth.

That is also the point with abstractions: We want them to be shorthand for a lot of information that was digested in the process leading to the use of the abstraction, but is not present when we use it. Such abstractions have depth. We love them. Other abstraction have no depth. They are shallow and just used to impress the other guy. They do not help us. We hate them.

Intellectual life is very much about the ability to distinguish between the shallow and the deep abstractions. You need to know if there is any depth before you make that headlong dive and jump into it.

There's an invidious rhetorical strategy that we've all seen — and I'm afraid that most of us have inflicted it on others as well. I call it the Veeck effect (of the first kind) — it occurs whenever someone adjusts the standards of evidence in order to favor a preferred outcome.

Why Veeck? Bill Veeck was a flamboyant baseball owner and promoter.
In his autobiography — (Veeck — As in Wreck) he described installing a flexible fence in the right field of the Milwaukee Brewers. At first he only put the fence up when facing a team full of power hitters, but eventually he took it to the limit, moving the fence up when the visitors were at bat and down when his team was.

The history of science is littered with flexible fences. The phlogiston theory predicted that phlogiston would be released when magnesium burned. It looked bad for that theory when experiments showed that burning magnesium became heavier — but its supporters happily explained that phlogiston had negative weight.

Consider Kepler. He came up with the idea that the distances of the six (known) planets could be explained by nesting the five Platonic solids. It almost worked for Earth, Mars, and Venus, but clearly failed for Jupiter. He dismissed the trouble with Jupiter, saying "nobody will wonder at it, considering the great distance". The theory certainly wouldn't have worked with any extra planets, but fortunately for Kepler's peace of mind, Uranus was discovered well after his death.

The Veeckian urge is strong in every field, but it truly flourishes in the human and historical sciences, where the definitive experiments that would quash such nonsense are often impossible, impractical, or illegal. Nowhere is this tendency stronger than among cultural anthropologists, who at times seem to have no reason for being other than refurbishing the reputations of cannibals.

Sometimes this has meant denying a particular case of cannibalism, for example among the Anasazi in the American Southwest. Evidence there has piled up and up -archaeologists have found piles of human bones with muscles scraped off, split open for marrow, polished by stirring in pots. They have even found human feces with traces of digested human tissue. But that's not good enough. For one thing, this implication of ancient cannibalism among the Anasazi is offensive to their Pueblo descendants, and that somehow trumps mounds of bloody evidence. You would think that the same principle would cause cultural anthropologists to embrace the face-saving falsehoods of other ethnic groups - didn't the South really secede over the tariff? But that doesn't seem to happen.

Some anthropologists have carried the effort further, denying that any culture was ever cannibalistic. They don't just deny Anasazi archaeology — they deny every kind of evidence, from archaeology to historical accounts, even reports from people alive today. When Álvaro de Mendaña discovered the Solomon Islands, he reported that a friendly chieftain threw a feast and offered him a quarter of a boy. Made up, surely. The conquistadors described the Aztecs as a cannibal kingdom — can't be right, even if the archeology supports it. When Papuans in Port Moresby volunteered to have a picnic in the morgue — to attract tourists, of course — they were just showing public spirit.

The Quaternary mass extinction, which wiped out much of the world's megafauna, offers paleontologists a chance to crank up their own fences. The large marsupials, flightless birds and reptiles of Australia disappeared shortly after humans arrived, about 50,000 years ago. The large mammals of North and South America disappeared about 10,000 years ago — again, just after humans showed up. Moas disappear within two centuries after Polynesian colonization in New Zealand, while giant flightless birds and lemurs disappeared from Madagascar shortly after humans arrived. What does this pattern suggest as the cause? Why, climate change, of course. Couldn't be human hunters — that's unpossible!

The Veeck effect is even more common in everyday life than it is in science. It's just that we expect more from scientists. But scientific examples are clear-cut, easy to see, and understanding the strategy helps you avoid succumbing to it.

Whenever some Administration official says that absence of evidence is not evidence of absence — whenever a psychiatrist argues that Freudian psychotherapy works for some people, even if proven useless on average — Bill Veeck's spirit goes marching on.

*If you're wondering about the second Veeck effect, it's the intellectual equivalent of putting a midget up to bat. And that's another essay.

In the northeast Bronx, I walk through a neighborhood that I once feared going into, this time with a big smile on my face. This is because I can quell the bullies with a new slang word in our dictionary "dual". As I approach the 2-train stop on East 225st , the bullies await me. I say, "Yo, whats the dual?" The bullies embrace me with a pound followed by a high five. I make my train.

In physics one of the most beautiful yet underappreciated ideas is that of duality. A duality allows us to describe a physical phenomenon from two different perspectives; often a flash of creative insight is needed to find both. However the power of the duality goes beyond the apparent redundancy of description. After all, why do I need more than one way to describe the same thing? There are examples in physics where either description of the phenomena fails to capture its entirety. Properties of the system 'beyond' the individual descriptions 'emerge'. I will provide two beautiful examples of how dualities manage to yield 'emergent' properties and, end with a speculation.

Most of us know about the famous wave-particle duality in quantum mechanics, which allowes the photon (and the electron) to attain their magical properties to explain all of the wonders of atomic physics and chemical bonding. The duality states that matter (such as the electron) has both wave-like and particle like properties depending on the context. What's weird is how quantum mechanics manifests the wave-particle duality. According to the traditional Copenhagen interpretation, the wave is a travelling oscillation of possibility that the electron can be realized omewhere as a particle.

Life gets strange in the example of quantum tunneling where the electron can penetrate a barrier only because of its 'wave-like' property. Classical physics tells us that an object will not surmount a barrier (like a hill) if its total kinetic energy is less than the potential energy of the barrier. However quantum mechanics predicts that particles can penetrate (or tunnel) through a barrier even when the kinetic energy is less than the potential energy of the barrier. This effect is used every time you use a flash drive or a CD player!

Most people assume that the conduction of electrons in a metal, is a well understood property of classical physics. But when we look deeper we realize that conduction happens because of the wave-like nature of the electrons. We call the collective electron waves that move through the periodic lattice of a metal a Bloch-wave. Qualitatively, when the electron's Bloch waves constructively interfere we get conduction. Moreover, the wave-particle duality takes us further to predict superconductivity, how it is that electrons (and other spin ½ particles like quarks) can conduct without resistance.

Nowadays in my field of quantum gravity and relativistic cosmology, theorists are exploiting another type of duality to address unresolved questions. This holographic duality was pioneered by Leonard Susskind and Gerhard 't Hooft, and later it found a home in the form of the AdS/CFT duality by Juan Maldacena.

This posits that the phenomenon of quantum gravity is described on one hand by a ordinary gravitational theory (a beefed up version of Einstein's general relativity). On the other hand a dual description of quantum gravity is described by a non-gravitational physics with a space-time of one lower dimension. We are left to wonder in the spirit of the wave-particle duality, what new physics we would glean from this type of duality.

The holographic duality also seems to persist in other approaches of quantum gravity, such as Loop Quantum Gravity, and researchers are still in exploring the true meaning behind holography and potential predictions for experiments.

Dualities seem to allow us to understand and make use of properties in physics that go beyond a singular lense of analysis. Might we wonder if duality can transcend its role in physics and into other fields? The dual of time will tell.

There's a lot of stuff in the world: trees, cars, galaxies, benzene, the Baths of Caracalla, your pancreas, Ottawa, ennui, Walter Mondale. How does it all fit together? In a word… Supervenience. (Pronounced soo-per-VEEN-yence. The verb form is to supervene.)

Supervenience is a shorthand abstraction, native to Anglo-American philosophy, that provides a general framework for thinking about how everything relates to everything else. The technical definition of supervenience is somewhat awkward:

Supervenience is a relationship between two sets of properties. Call them Set A and Set B. The Set A properties supervene on the Set B properties if and only if no two things can differ in their A properties without also differing in their B properties.

This definition, while admirably precise, makes it hard to see what supervenience is really about, which is the relationships among different levels of reality. Take, for example, a computer screen displaying a picture. At a high level, at the level of images, a screen may depict an image of a dog sitting in a rowboat, curled up next to a life vest. The screen's content can also be described as an arrangement of pixels, a set of locations and corresponding colors. The image supervenes on the pixels. This is because a screen's image-level properties (its dogginess, its rowboatness) cannot differ from another screen's image-level properties unless the two screens also differ in their pixel-level properties.

The pixels and the image are, in a very real sense, the same thing. But — and this is key — their relationship is asymmetrical. The image supervenes on the pixels, but the pixels do not supervene on the image. This is because screens can differ in their pixel-level properties without differing in their image-level properties. For example, the same image may be displayed at two different sizes or resolutions. And if you knock out a few pixels, it's still the same image. (Changing a few pixels will not protect you from charges of copyright infringement.) Perhaps the easiest way to think about the asymmetry of supervenience is in terms of what determines what. Determining the pixels completely determines the image, but determining the image does not completely determine the pixels.

The concept of supervenience deserves wider currency because it allows us to think clearly about many things, not just about images and pixels. Supervenience explains, for example, why physics is the most fundamental science and why the things that physicists study are the most fundamental things. To many people, this sounds like a value judgment, but it's not, or need not be. Physics is fundamental because everything in the universe, from your pancreas to Ottawa, supervenes on physical stuff. (Or so "physicalists" like me claim.) If there were a universe physically identical to ours, then it would also include a pancreas just like yours and an Ottawa just like Canada's.

Supervenience is especially helpful when grappling with three contentious and closely related issues: (1) the relationship between science and the humanities, (2) the relationship between the mind and brain, and (3) the relationship between facts and values.

Humanists sometimes perceive science as imperialistic, as aspiring to take over the humanities, to "reduce" everything to electrons, genes, numbers, and neurons, and thus to "explain away" all of the things that make life worth living. Such thoughts are accompanied by disdain or fear, depending on how credible such ambitions are taken to be. Scientists, for their part, sometimes are imperious, dismissing humanists and their pursuits as childish and unworthy of respect. Supervenience can help us think about how science and the humanities fit together, why science is sometimes perceived as encroaching on the humanist's territory, and the extent to which such perceptions are and are not valid.

It would seem that humanists and scientists study different things. Humanists are concerned with things like love, revenge, beauty, cruelty, and our evolving conceptions of such things. Scientists study things like electrons and nucleotides. But sometimes it sounds like scientists are getting greedy. Physicists aspire to construct a complete physical theory, which is sometimes called a "Theory of Everything" (TOE). If humanists and scientists study different things, and if physics covers everything, then what is left for the humanists? (Or, for that matter, non-physicists?)

There is a sense in which a TOE really is a TOE, and there is a sense in which it's not. A TOE is a complete theory of everything upon which everything else supervenes. If two worlds are physically identical, then they are also humanistically identical, containing exactly the same love, revenge, beauty, cruelty, and conceptions thereof. But that does not mean that a TOE puts all other theorizing out of business, not by a long shot. A TOE won't tell you anything interesting about Macbeth or the Boxer Rebellion.

Perhaps the threat from physics was never all that serious. Today, the real threat, if there is one, is from the behavioral sciences, especially the sciences that connect the kind of "hard" science we all studied in high school to humanistic concerns. In my opinion, three sciences stand out in this regard: behavioral genetics, evolutionary psychology, and cognitive neuroscience. I study moral judgment, a classically humanistic topic. I do this in part by scanning people's brains while they make moral judgments. More recently I've started looking at genes, and my work is guided by evolutionary thinking. My work assumes that the mind supervenes on the brain, and I attempt to explain human values — for example the tension between individual rights and the greater good — in terms of competing neural systems.

I can tell you from personal experience that this kind of work makes some humanists uncomfortable. During the discussion following a talk I gave at Harvard's Humanities Center, a prominent professor declared that my talk — not any particular conclusion I'd drawn, but the whole approach — made him physically ill. (Of course, this could just be me!)

The subject matter of the humanities has always supervened on the subject matter of the physical sciences, but in the past a humanist could comfortably ignore the subvening physical details, much as an admirer of a picture can ignore the pixel-level details. Is that still true? Perhaps it is. Perhaps it depends on one's interests. In any case, it's nothing to be worried sick about.

NB: Andrea Heberlein points out that "supervenience" may also refer to exceptional levels of convenience, as in, "New Chinese take-out right around the corner — Supervenient!"

One of the most pernicious misconceptions in cognitive science is the belief in a dichotomy between nature and nurture. Many psychologists, linguists and social scientists, along with the popular press, continue to treat nature and nurture as combatting ideologies, rather than complementary perspectives. For such people, the idea that something isboth "innate" and "learned", or both "biological" and "cultural", is an absurdity. Yet most biologists today recognize that understanding behavior requires that we understand the interaction between inborn cognitive processes (e.g. learning and memory) and individual experience. This is particularly true in human behaviour, since the capacities for language and culture are some of the key adaptations of our species, and involve irreducible elements of both biology and environment, of both nature and nurture.

The antidote to "nature versus nurture" thinking is to recognize the existence, and importance, of "instincts to learn". This phrase was introduced by Peter Marler, one of the fathers of birdsong research. A young songbird, while still in the nest, eagerly listens to adults of its own species sing. Months later, having fledged, it begins singing itself, and shapes its own initial sonic gropings to the template provided by those stored memories.  During this period of "subsong" the bird gradually refines and perfects its own song, until by adulthood it is ready to defend a territory and attract mates with its own, perhaps unique, species-typical song. 

Songbird vocal learning is the classic example of an instinct to learn.  The songbird's drive to listen, and to sing, and to shape its song to that which it heard, is all instinctive.  The bird needs no tutelage, nor feedback from its parents, to go through these stages.  Nonetheless, the actual song that it sings is learned, passed culturally from generation to generation.  Birds have local dialects, varying randomly from region to region.  If the young bird hears no song, it will produce only an impoverished squawking, not a typical song.

Importantly, this capacity for vocal learning is only true of some birds, like songbirds and parrots. Other bird species, like seagulls, chickens or owls, do not learn their vocalizations: rather, their calls develop reliably in the absence of any acoustic input.  The calls of such birds are truly instinctive, rather than learned.  But for those birds capable of vocal learning, the song that an adult bird sings is the result of a complex interplay between instinct (to listen, to rehearse, and to perfect) and learning (matching the songs of adults of its species).
 
It is interesting, and perhaps surprising, to realize that most mammals do not have a capacity for complex vocal learning of this sort.  Current research suggests that, aside from humans, only marine mammals (whales, dolphins, seals…), bats, and elephants have this ability.  Among primates, humans appear to be the only species that can hear new sounds in the environment, and then reproduce them.  Our ability to do this seems to depend on a babbling stage during infancy, a period of vocal playfulness that is as instinctual as the young bird's subsong.  During this stage, we appear to fine tune our vocal control so that, as children, we can hear and reproduce the words and phrases of our adult caregivers.
 
So is human language an instinct, or learned? The question, presupposing a dichotomy, is intrinsically misleading.  Every word that any human speaks, in any of our species' 6000 languages, has been learned.   And yet the capacity to learn that language is a human instinct, something that every normal human child is born with, and that no chimpanzee or gorilla possesses. 

The instinct to learn language is, indeed, innate (meaning simply that it reliably develops in our species), even though every language is learned.  As Darwin put it in Descent of Man, "language is an art, like brewing or baking; but … certainly is not a true instinct, for every language has to be learnt.  It differs, however, widely from all ordinary arts, for man has an instinctive tendency to speak, as we see in the babble of our young children; whilst no child has an instinctive tendency to brew, bake, or write."
 
And what of culture? For many, human culture seems the very antithesis of "instinct". And yet it must be true that language plays a key role in every human culture. Language is the primary medium for the passing on of historically-accumulated knowledge, tastes, biases and styles that makes each of our human tribes and nations its own unique and precious entity.  And if human language is best conceived of as an instinct to learn, why not culture itself? 
 
The past decade has seen a remarkable unveiling of our human genetic and neural makeup, and the coming decade promises even more remarkable breakthroughs.  Each of us six billion humans is genetically unique (with the fascinating exception of identical twins).  For each of us, our unique genetic makeup influences, but does not determine, what we are. 
 
If we are to grapple earnestly and effectively with the reality of human biology and genetics, we will need to jettison outmoded dichotomies like the traditional distinction between nature and nurture.  In their place, we will need to embrace the reality of the many instincts to learn (language, music, dance, culture…) that make us human.  
 
I conclude that the dichotomy-denying phrase "instinct to learn" deserves a place in the cognitive toolkit of everyone who hopes, in the coming age of individual genomes, to understand human culture and human nature in the context of human biology.  Human language, and human culture, are not instincts — but they are instincts to learn.

I think the scientific concept that would improve everybody's cognitive toolkit the most is "scientific concept".

Despite spectacular success in research, I feel that our global scientific community has been nothing short of a spectacular failure when it comes to educating the public. Haitians burned 12 "witches" in 2010. In the US, recent polls show that 39% consider astrology scientific, and 40% believe that our human species is less than 10,000 years old. If everyone understood the concept of "scientific concept", these percentages would be zero.

Moreover, the world would be a better place, since people with a scientific lifestyle, basing their decisions on correct information, maximize their chances of success. By making rational buying and voting decisions, they also strengthen the scientific approach to decision-making in companies, organizations and governments.

Why have we scientists failed so miserably? I think the answers lie mainly in psychology, sociology and economics.

A scientific lifestyle requires a scientific approach to both gatherininformation and using information, and both have their pitfalls.

You're clearly more likely to make the right choice if you're aware of the full spectrum of arguments before making your mind up, yet there are many reasons why people don't get such complete information. Many lack access to it (3% of Afghans have internet, and in a 2010 poll, 92% didn't know about the 9/11 attacks). 

Many are too swamped with obligations and distractions to seek it. Many seek information only from sources that confirm their preconceptions. The most valuable information can be hard to find even for those who are online and uncensored, buried in an unscientific media avalanche.

Then there's what we do with the information we have. The core of a scientific lifestyle is to change your mind when faced with information that disagrees with your views, avoiding intellectual inertia, yet many laud leaders stubbornly sticking to their views as "strong". The great physicist Richard Feynman hailed "distrust of experts" as a cornerstone of science, yet herd mentality and blind faith in authority figures is widespread. Logic forms the basis of scientific reasoning, yet wishful thinking, irrational fears and other cognitive biases often dominate decisions.

So what can we do to promote a scientific lifestyle?

The obvious answer is improving education. In some countries, having even the most rudimentary education would be a major improvement (less than half of all Pakistanis can read). By undercutting fundamentalism and intolerance, it would curtail violence and war.

By empowering women, it would curb poverty and the population explosion. However, even countries that offer everybody education can make major improvements.

 All too often, schools resemble museums, reflecting the past rather than shaping the future. The curriculum should shift from one watered down by consensus and lobbying to skills our century needs, for relationships, health, contraception, time management, critical thinking and recognizing propaganda. For youngsters, learning a global language and typing should trump long division and writing cursive. In the internet age, my own role as a classroom teacher has changed. I'm no longer needed as a conduit of information, which my students can simply download on their own. Rather, my key role is inspiring a scientific lifestyle, curiosity and desire to learn more.

Now let's get to the most interesting question: how can we really make a scientific lifestyle take root and flourish? 

Reasonable people have been making similar arguments for better education since long before I was in diapers, yet rather than improving, education and adherence to a scientific lifestyle is arguably deteriorating further in many countries, including the US. Why? Clearly because there are powerful forces pushing back in the opposite direction, and they are pushing more effectively. Corporations concerned that a better understanding of certain scientific issues would harm their profits have an incentive to muddy the waters, as do fringe religious groups concerned that questioning their pseudoscientific claims would erode their power.

So what can we do? The first thing we scientists need to do is get off our high horses, admit that our persuasive strategies have failed, and develop a better strategy. We have the advantage of having the better arguments, but the anti-scientific coalition has the advantage of better funding.

However, and this is painfully ironic, it is also more scientifically organized! If a company wants to change public opinion to increase their profits, it deploys scientific and highly effective marketing tools. What do people believe today? What do we want them to believe tomorrow? Which of their fears,  insecurities, hopes and other emotions can we take advantage of? What's the most cost-effective way of changing their mind? Plan a campaign. Launch. Done.

Is the message oversimplified or misleading? Does it unfairly discredit the competition? That's par for the course when marketing the latest smartphone or cigarette, so it would be naive to think that the code of conduct should be any different when this coalition fights science.

Yet we scientists are often painfully naive, deluding ourselves that just because we think we have the moral high ground, we can somehow defeat this corporate-fundamentalist coalition by using obsolete unscientific strategies. Based of what scientific argument wil it make a hoot of a difference if we grumble "we won't stoop that low" and "people need to change" in faculty lunch rooms and recite statistics to journalists? 

We scientists have basically been saying "tanks are unethical, so let's fight tanks with swords".

To teach people what a scientific concept is and how a scientific lifestyle will improve their lives, we need to go about it scientifically:

We need new science advocacy organizations which use all the same scientific marketing and fundraising tools as the anti-scientific coalition.
We'll need to use many of the tools that make scientists cringe, from ads and lobbying to focus groups that identify the most effective sound bites.
We won't need to stoop all the way down to intellectual dishonesty, however. Because in this battle, we have the most powerful weapon of all on our side: the facts.

THE WAKING DREAM I HAVE FOR MY TOOLKIT IS ONE FILLED WITH OBJECTS OF UNDERSTANDING AND COMMUNICATION.

THE TOOLS IN MY TOOLBOX RESPOND TO ME. THEY NOD WHEN I TALK, GIVE ME EVIDENCE OF ME, AND SUGGEST SECONDARY AND TERTIARY JOURNEYS THAT EXTEND MY CURIOSITIES.

THIS TOOLKIT IS WOVEN OF THREADS OF IGNORANCE AND STITCHES OF QUESTIONS THAT INVITE KNOWLEDGE IN.

IN THIS WEAVE ARE MAPS AND PATTERNS WITH ENOUGH STITCHES TO ALLOW ME TO MAKE THE CHOICE, AS I WISH, TO ADD A TINY DROP OF SUPERGLUE.

I WANT AN iPHONE / iPAD / iMAC THAT NODS.

THE FIRST MOVIES ARCHIVED STAGE SHOWS.

THE iPAD AND KINDLE ARCHIVE MAGAZINES, NEWSPAPERS AND BOOKS.

I WANT A NEW MODALITY WITH WHICH I CAN CONVERSE AT DIFFERING LEVELS OF COMPLEXITY, IN DIFFERENT LANGUAGES, AND WHICH UNDERSTANDS THE NUANCE OF MY QUESTIONS.

I WANT HELP FLYING THROUGH MY WAKING DREAMS CONNECTING THE THREADS OF THESE EPIPHANIES.

I BELIEVE WE ARE AT THIS CUSP.

A FIRST TOE IN THE WARM BATH OF THIS NEW MODALITY.

Constant awareness of the Eintstellung Effect would make a useful addition to our cognitive toolkit. 

The Einstellung Effect is more ubiquitous than its name suggests. We constantly experience it when trying to solve a problem by pursuing solutions that have worked for us in the past - instead of evaluating and addressing it on its own terms. Thus, while we may eventually solve the problem, we may also be wasting an opportunity to so in a more rapid, effective, and resourceful manner. 

Think of a chess match. If you are a chess master with a deep familarity with chess history, you are likely to spot game developments that look similar to other matches that you know by heart. Knowing how those previous matches unfolded, you may automatically pursue similar solutions. 

This may be the right thing to do in matches that are exactly alike - but in all other situations, you've got to watch out! Familar solutions may not be optima. Some recent research into the occurences of the Einstellung Effect in chess players suggests that it tends to be less prominent once players reach a certain level of mastery, getting a better grasp of the risks associated with pursuing solutions that look familiar and trying to avoid acting on "autopilot".  

The irony here is that the more expansive our cognitive toolkit, the more likely we are to fall back on solutions and approaches that have worked in the past instead of asking whether the problem in front of us is fundamentally different from anything else we have dealt with in the past. A cognitive toolkit that has no built-in awareness of the Einstellung Effect seems somewhat defective to me.

In 1909, the biologist Jakob von Uexküll introduced the concept of theumwelt. He wanted a word to express a simple (but often overlooked) observation: different animals in the same ecosystem pick up on different environmental signals. In the blind and deaf world of the tick, the important signals are temperature and the odor of butyric acid. For the black ghost knifefish, it's electrical fields. For the echolocating bat, it's air-compression waves. The small subset of the world that an animal is able to detect is its umwelt. The bigger reality, whatever that might mean, is called the umgebung.

The interesting part is that each organism presumably assumes its umwelt to be the entire objective reality "out there." Why would any of us stop to think that there is more beyond what we can sense? In the movie The Truman Show, the eponymous Truman lives in a world completely constructed around him by an intrepid television producer. At one point an interviewer asks the producer, "Why do you think Truman has never come close to discovering the true nature of his world?" The producer replies, "We accept the reality of the world with which we're presented." We accept our umwelt and stop there.

To appreciate the amount that goes undetected in our lives, imagine you're a bloodhound dog. Your long nose houses two hundred million scent receptors. On the outside, your wet nostrils attract and trap scent molecules. The slits at the corners of each nostril flare out to allow more air flow as you sniff. Even your floppy ears drag along the ground and kick up scent molecules. Your world is all about olfaction. One afternoon, as you're following your master, you stop in your tracks with a revelation. What is it like to have the pitiful, impoverished nose of a human being? What can humans possibly detect when they take in a feeble little noseful of air? Do they suffer a hole where smell is supposed to be?

Obviously, we suffer no absence of smell because we accept reality as it's presented to us. Without the olfactory capabilities of a bloodhound, it rarely strikes us that things could be different. Similarly, until a child learns in school that honeybees enjoy ultraviolet signals and rattlesnakes employ infrared, it does not strike her that plenty of information is riding on channels to which we have no natural access. From my informal surveys, it is very uncommon knowledge that the part of the electromagnetic spectrum that is visible to us is less than a ten-trillionth of it.

Our unawareness of the limits of our umwelt can be seen with color blind people: until they learn that others can see hues they cannot, the thought of extra colors does not hit their radar screen. And the same goes for the congenitally blind: being sightless is not like experiencing "blackness" or "a dark hole" where vision should be. As a human is to a bloodhound dog, a blind person does not miss vision. They do not conceive of it. Electromagnetic radiation is simply not part of their umwelt.

The more science taps into these hidden channels, the more it becomes clear that our brains are tuned to detect a shockingly small fraction of the surrounding reality. Our sensorium is enough to get by in our ecosystem, but is does not approximate the larger picture.

I think it would be useful if the concept of the umwelt were embedded in the public lexicon. It neatly captures the idea of limited knowledge, of unobtainable information, and of unimagined possibilities. Consider the criticisms of policy, the assertions of dogma, the declarations of fact that you hear every day — and just imagine if all of these could be infused with the proper intellectual humility that comes from appreciating the amount unseen.

Root cause analysis is an attractive concept for certain matters in industry, engineering and quality control. A classic application is to determine why a plane crashed by finding the proverbial "black box" — the tamper-proof event data recorder. Even though this box is usually bright orange, the term symbolizes the sense of dark matter, a container with critical information to help illuminate what happened. Getting the black box audio recording is just one component of a root cause analysis for why a plane goes down.

Man is gradually being morphed into an event data recorder by virtue of each person's digital identity and presence on the web. Not only do we post our own data, sometimes unwittingly, but also others post information about us, and all of this is permanently archived. In that way it is close to tamper-proof. With increasing use of biosensors, high-resolution imaging (just think of our current cameras and video recording, no less digital medical imaging), and DNA sequencing, the human data event recorder will be progressively enriched with data and information.  

In our busy, networked lives with constant communication, streaming and distraction, the general trend has moved away from acquiring deep knowledge for why something happened.  This is best exemplified in health and medicine. Physicians rarely seek root cause. If a patient has a common condition such as high blood pressure, diabetes, or asthma, he or she is put on some prescription drugs without any attempt at ascertaining why the individual crashed — certainly a new, chronic medical condition can be likened to such an event. There are usually specific reasons for these disorders but they are not hunted down. Taken to an extreme, when an individual dies and the cause is not known it is now exceedingly rare that an autopsy is ever performed. Doctors have generally caved in their quest to define root cause, and they are fairly representative of most of us. Ironically, this is happening at a time when there is unprecedented capability for finding the explanation. But we're just too busy.

So to tweak our cognitive performance in the digital world where there is certainly no shortage of data, it's time to use it and understand, as fully as possible, why unexpected or unfavorable things happen. Or even why something great transpired. It's a prototypic scientific concept that has all too often been left untapped. Each person is emerging as an extraordinary event recorder and part of the Internet of all things. Let's go deep. Nothing unexplained these days should go without a hunt.

When John Cabot came to the Grand Banks off Newfoundland in 1497 he was astonished at what he saw. Fish, so many fish — fish in numbers he could hardly comprehend. According to Farley Mowat, Cabot wrote that the waters were so "swarming with fish [that they] could be taken not only with a net but in baskets let down and [weighted] with a stone."

The fisheries boomed for five hundred years, but by 1992 it was all over. The Grand Banks cod fishery was destroyed, and the Canadian government was forced to close it entirely, putting 30,000 fishers out of work. It has never recovered.

What went wrong? Many things, from factory fishing to inadequate oversight, but much of it was aided and abetted by treating each step toward disaster as normal. The entire path, from plenitude to collapse, was taken as the status quo, right up until the fishery was essentially wiped out.

In 1995 fisheries scientist Daniel Pauly coined a phrase for this troubling ecological obliviousness — he called it "shifting baseline syndrome". Here is how Pauly first described the syndrome: "Each generation of fisheries scientist accepts as baseline the stock situation that occurred at the beginning of their careers, and uses this to evaluate changes. When the next generation starts its career, the stocks have further declined, but it is the stocks at that time that serve as a new baseline. The result obviously is a gradual shift of the baseline, a gradual accommodation of the creeping disappearance of resource species…"

It is blindness, stupidity, intergeneration data obliviousness. Most scientific disciplines have long timelines of data, but many ecological disciplines don't. We are forced to rely on second-hand and anecdotal information — we don't have enough data to know what is normal, so we convince ourselves that this is normal.

But it often isn't normal. Instead, it is a steadily and insidiously shifting baseline, no different than convincing ourselves that winters have always been this warm, or this snowy. Or convincing ourselves that there have always been this many deer in the forests of eastern North America. Or that current levels of energy consumption per capita in the developed world are normal. All of these are shifting baselines, where our data inadequacy, whether personal or scientific, provides dangerous cover for missing important longer-term changes in the world around us.

When you understand shifting baseline syndrome it forces you to continually ask what is normal. Is this? Was that? And, at least as importantly, it asks how we "know" that it's normal. Because, if it isn't, we need to stop shifting the baselines and do something about it before it's too late.

In the late 1960s, the psychologist Walter Mischel began a simple experiment with four-year old children. He invited the kids into a tiny room, containing a desk and a chair, and asked them to pick a treat from a tray of marshmallows, cookies, and pretzel sticks. Mischel then made the four-year olds an offer: they could either eat one treat right away or, if they were willing to wait while he stepped out for a few minutes, they could have two treats when he returned. Not surprisingly, nearly every kid chose to wait.

At the time, psychologists assumed that the ability to delay gratification — to get that second marshmallow or cookie — depended on willpower. Some people simply had more willpower than others, which allowed them to resist tempting sweets and save money for retirement.

However, after watching hundreds of kids participate in the marshmallow experiment, Mischel concluded that this standard model was wrong. He came to realize that willpower was inherently weak, and that children that tried to outlast the treat — gritting their teeth in the face of temptation — soon lost the battle, often within thirty seconds.

Instead, Mischel discovered something interesting when he studied the tiny percentage of kids who could successfully wait for the second treat. Without exception, these "high delayers" all relied on the same mental strategy: they found a way to keep themselves from thinking about the treat, directing their gaze away from the yummy marshmallow. Some covered their eyes or played hide-and-seek underneath the desk. Others sang songs from "Sesame Street," or repeatedly tied their shoelaces, or pretended to take a nap. Their desire wasn't defeated — it was merely forgotten.

Mischel refers to this skill as the "strategic allocation of attention," and he argues that it's the skill underlying self-control. Too often, we assume that willpower is about having strong moral fiber. But that's wrong — willpower is really about properly directing the spotlight of attention, learning how to control that short list of thoughts in working memory. It's about realizing that if we're thinking about the marshmallow we're going to eat it, which is why we need to look away.

What's interesting is that this cognitive skill isn't just a useful skill for dieters. Instead, it seems to be a core part of success in the real world. For instance, when Mischel followed up with the initial subjects 13 years later — they were now high school seniors — he realized that performance on the marshmallow task was highly predictive on a vast range of metrics. Those kids who struggled to wait at the age of four were also more likely to have behavioral problems, both in school and at home. They struggled in stressful situations, often had trouble paying attention, and found it difficult to maintain friendships. Most impressive, perhaps, were the academic numbers: The little kid who could wait fifteen minutes for their marshmallow had an S.A.T. score that was, on average, two hundred and ten points higher than that of the kid who could wait only thirty seconds.

These correlations demonstrate the importance of learning to strategically allocate our attention. When we properly control the spotlight, we can resist negative thoughts and dangerous temptations. We can walk away from fights and improve our odds against addiction. Our decisions are driven by the facts and feelings bouncing around the brain — the allocation of attention allows us to direct this haphazard process, as we consciously select the thoughts we want to think about.

Furthermore, this mental skill is only getting more valuable. We live, after all, in the age of information, which makes the ability to focus on the important information incredibly important. (Herbert Simon said it best: "A wealth of information creates a poverty of attention.") The brain is a bounded machine and the world is a confusing place, full of data and distractions — intelligence is the ability to parse the data so that it makes just a little bit more sense. Like willpower, this ability requires the strategic allocation of attention.

One final thought: In recent decades, psychology and neuroscience have severely eroded classical notions of free will. The unconscious mind, it turns out, is most of the mind. And yet, we can still control the spotlight of attention, focusing on those ideas that will help us succeed. In the end, this may be the only thing we can control. We don't have to look at the marshmallow.

Confluence of factors is highly influential in setting off changes in complex systems. A common example is in risk — the "Swiss cheese theory". Losses only occur if all controls fail — the holes in the Swiss cheese align.

Confluence, coincidence of events in a single setting, is well understood. Parallel developments, often in different settings or disciplines, can be influential in shaping events. A coincidence of similar logic and processes in seemingly unrelated activities provide indications of likely future developments and risks. The ability to better recognize"parallelism" would improve cognitive processes.

Economic forecasting is dismal, prompting John Kenneth Galbraith to remark that economists were only put on earth to make astrologers look good. Few economists anticipated the current financial problems, at least before they happened.

However, the art market proved remarkably accurate in anticipating developments, especially the market in the work of Damien Hirst — the best known of a group of artists dubbed yBAs (young British Artists).

Hirst's most iconic work — The Physical Impossibility of Death in the Mind of Someone Living — is a 14-foot (4.3 meter) tiger shark immersed in formaldehyde in a vitrine weighing over two tons. Charles Saatchi (the advertising guru) bought it for £50,000. In December 2004, Saatchi sold the work to Steve Cohen, founder and principal of the uber hedge fund — SAC Capital Advisers, which manages $20 billion. Cohen paid $12 million for The Physical Impossibility of Death in the Mind of Someone Living, although there are allegation that it was only $8 million.

In June 2007, Damien Hirst tried to sell a life size platinum cast of a human skull, encrusted with £15 million worth of 8,601 pave-set industrial diamonds, weighing 1,100 carats including a 52.4 carat pink diamond in the center of the forehead valued at £4 million. For the Love of God was a memento mori, in Latin remember you must die. The work was offered for sale at £50 million as part of Hirst's Beyond Belief show. In September 2007, For the Love of God was sold to Hirst and some investors for full price, for later resale.

The sale of The Physical Impossibility of Death in the Mind of Someone Living marked the final phase of the irresistible rise of markets. The failure of For the Love of God to sell marked its zenith as clearly as any economic marker.

Parallelism exposes common thought processes and similar valuation approaches to unrelated objects.

Hirst was the artist of choice for conspicuously consuming hedge fund managers, who were getting very rich managing money. Inflated prices suggested the presence of "irrational excess". The nature of sought after Hirst pieces and even their titles provided an insight into the hubristic self-image of financiers. With its jaws gaping, poised to swallow its prey,The Physical Impossibility of Death in the Mind of Someone Living mirrored the killer instincts of hedge funds, feared predators in financial markets. Cohen "… liked the whole fear factor."

The work of Japanese artist Takeshi Murakami provides confirmation. Inspired by Hokusai's famous 19th century woodblock print The Great Wave of Kanagawa, Murakami's 727 paintings showed Mr. DOB, a post nuclear Mickey Mouse character, as a god riding on a cloud or a shark surfing on a wave. The first 727 is owned by New York's Museum of Modern Art, the second by Steve Cohen.

Parallelism is also evident in the causes underlying several crises facing humanity. It is generally acknowledged that high levels of debt were a major factor in the ongoing global financial crisis. What is missed is that the logic of debt is similar to one underlying other problematic issues.

There is a striking similarity between the problems of the financial system, irreversible climate change and shortages of vital resources like oil, food and water. Economic growth and wealth was based on borrowed money. Debt allowed society to borrow from the future. It accelerated consumption, as debt is used to purchase something today against the uncertain promise of paying back the borrowing in the future. Society polluted the planet, creating changes in the environment which are difficult to reserve. Under-priced, natural finite resources were wantonly utilized, without proper concern about conservation.

In each area, society borrowed from and pushed problems into the future. Current growth and short-term profits were pursued at the expense of risks which were not evident immediately and that would emerge later.

To dismiss this as short-term thinking and greed is disingenuous. A crucial cognitive factor underlying the approach was a similar process of problem solving — borrowing from or pushing problems further into the future. This was consistently applied across different problems, without consideration of either it relevance, applicability or desirability. Where such parallelism exists, it feeds on itself, potentially leading to total systemic collapse.

Recognition and understanding of parallelism is one way to improve cognitive thinking. It may provide a better mechanism for predicting specific trends. It may also enable people to increase the dialectic richness, drawing on different disciplines. It requires overcoming highly segmented and narrow educational disciplines, rigid institutional structures and restricted approaches to analysis or problem solving.

The "Cognitive Iceberg:" Humans Are Blind To Many Of The Processes That Shape Their Mental Lives

The human brain is an inconceivably complex tool, and while we're focusing on the business of daily life, our brains are processing multitudes of information below the surface of conscious awareness. Meanwhile, this peripheral information subtly shapes our thoughts, feelings and actions, and crafts some of our most critical life outcomes. This information takes many forms, but I'll illustrate the principle with three brief examples:

1. Color

Color is a ubiquitous feature of the environment, though we rarely notice colors unless they're particularly bright or deviate dramatically from our expectations. Nonetheless, colors have the capacity to shape a range of outcomes: men are ever so slightly more attractive to women when they wear red colored shirts (rather than shirts of another color); the same effect applies to women, who seem more attractive to men when their pictures are bounded by a red colored border. Red signals both romantic intent and dominance amongst lower-order species, and this same signal applies to men and women. This same relationship between red and dominance explains why sporting teams that wear red are more aggressive and tend to dominate sporting teams that wear other colors; meanwhile, sports referees and umpires assign more points to teams wearing red uniforms, which may explain in part why those teams tend to outperform teams wearing other colors. But, red isn't always beneficial: we've come to associate red with errors and caution, which makes people avoidant and in turn limits their creativity (though it also improves their attention to detail). These effects have sound bases in biology and human psychology, but that doesn't make them any less remarkable or surprising to the lay population.

2. Weather and Ambient Temperature

No one's surprised that the sunny warmth of summer makes people happy, but weather conditions and ambient temperature have other more surprising effects on our mental lives. Rainy weather makes us more introspective and thoughtful, which in turn improves our memory — in one study, people remembered the features of a store with greater accuracy on rainy days than on sunny days. On a grander scale, the stock market tends to rise on fine, sunny days, while cooler, rainy days prompt sluggishness and brief downturns. More surprising, still, is the relationship between changes in weather and various accidents, suicide, depression and irritability, all of which are claimed to respond to changes in the electrical state of the atmosphere. The metaphor between warmth and human kindness is also more than a metaphor, as recent studies have shown that people find strangers more likable when they form their first impressions while holding a cup of warm coffee. The warmth-kindness metaphor extends to social exclusion, as people literally feel colder when they've been socially excluded. The simple relationship between fine weather and happiness is joined by a series of more surprising and complicated relationships between weather and warmth on the one hand, and a range of important life outcomes on the other.

3. Symbols and Images

Urban landscapes are populated by thousands of symbols and images that unwittingly influence how we think and behave. Self-identified Christians tend to behave more honestly when they're exposed to an image of the crucifix, even when they have no conscious memory of seeing the crucifix in the first place. Honesty is a virtue, but another experiment showed that Christians held lower opinions of themselves after they were subliminally exposed to an image of then Pope John Paul II. On a brighter note, people think more creatively when they're exposed to the Apple Computers logo, or when they witness the illumination of an incandescent light bulb; both the Apple logo and the illuminated light bulb are popularly associated with creativity, and deeply ingrained metaphors once activated have the capacity to shape actual behavior. Similar associative logic suggests that national flags should prompt unity, and indeed a sample of left-wing and right-wing Israelis were more accommodating of opposing political views when they were subliminally exposed to an image of the Israeli flag. Likewise, a sample of Americans responded more favorably to Muslims when seated in front of a large U.S. flag.

These three cues — colors, weather conditions, and symbols and images — are joined by dozens of others that have a surprising capacity to influence how we think, feel, behave, and decide. Once we understand what those cues are and how they shape our mental lives, we're better equipped to harness them to our advantage.

There is a problem that anyone who has lived in New York City must wonder about: you can't get a cab from 4 to 5 pm. The reason for this is not a mystery: at a moment of peak demand, taxi drivers tend to change shifts. Too many cabs are headed to garages in Queens because when a taxi is operated by two drivers 24 hours a day, a fair division of shifts is to switch over at 5 pm. Now this is a problem for the city's Taxi and Limousine Commission, it may even be a hard one to solve, but it is not a wicked problem. For one thing, it's easy to describe, as I just showed you. That right there boots it from the category.

Among some social scientists, there is this term of art: wicked problems. We would be vastly better off if we understood what wicked problems are, and learned to distinguish between them and regular (or "tame") problems.

Wicked problems have these features: It is hard to say what the problem is, to define it clearly or to tell where it stops and starts. There is no "right" way to view the problem, no definitive formulation. The way it's framed will change what the solution appears to be. Someone can always say that the problem is just a symptom of another problem and that someone will not be wrong. There are many stakeholders, all with their own frames, which they tend to see as exclusively correct. Ask what the problem is and you will get a different answer from each. The problem is inter-connected to a lot of other problems; pulling them apart is almost impossible.

It gets worse. Every wicked problem is unique, so in a sense there is no prior art and solving one won't help you with the others. No one has "the right to be wrong," meaning enough legitimacy and stakeholder support to try stuff that will almost certainly fail, at first. Instead failure is savaged, and the trier is deemed unsuitable for another try. The problem keeps changing on us. It is never definitely resolved. Instead, we just run out of patience, or time, or money. It's not possible to understand the problem first, then solve it. Rather, attempts to solve it reveal further dimensions of the problem. (Which is the secret of success for people who are "good" at wicked problems.)

Know any problems like that? Sure you do. Probably the best example in our time is climate change. What could be more inter-connected than it? Someone can always say that climate change is just a symptom of another problem--our entire way of life, perhaps — and he or she would not be wrong. We've certainly never solved anything like it before. Stakeholders: everyone on the planet, every nation, every company.

When General Motors was about go bankrupt and throw tends of thousands of people out of work that was a big, honking problem, which rightly landed on the president's desk, but it was not a wicked one. Barack Obama's advisors could present him with a limited range of options; if he decided to take the political risk and save General Motors from collapse he could be reasonably certain that the recommended actions would work. If they didn't, he could try more drastic measures.

But health care reform wasn't like that at all. In the United States, rising health care costs are a classic case of a wicked problem. No "right" way to view it. Every solution comes with its own contestable frame. Multiple stakeholders who don't define the problem the same way. If the uninsured go down but costs go up, is that progress? We don't even know.

Wicked!

Still, we would be better off if we knew when we were dealing with a wicked problem, as opposed to the regular kind. If we could designate some problems as wicked we might realize that "normal" approaches to problem-solving don't work. We can't define the problem, evaluate possible solutions, pick the best one, hire the experts and implement. No matter how much we may want to follow a routine like that, it won't succeed. Institutions may require it, habit may favor it, the boss may order it, but wicked problems don't care.

Presidential debates that divided wicked from tame problems would be very different debates. Better, I think. Journalists who covered wicked problems differently than they covered normal problems would be smarter journalists. Institutions that knew when how to distinguish wicked problems from the other kind would eventually learn the limits of command and control.

Wicked problems demand people who are creative, pragmatic, flexible and collaborative. They never invest too much in their ideas because they know they are going to have to alter them. They know there's no right place to start so they simply start somewhere and see what happens. They accept the fact that they're more likely to understand the problem after its "solved" than before. They don't expect to get a good solution; they keep working until they've found something that's good enough. They're never convinced that they know enough to solve the problem, so they are constantly testing their ideas on different stakeholders.

Know any people like that? Maybe we can get them interested in health care...

In 1967, Richard Dawkins introduced the idea of a meme: a unit of cultural transmission capable of replicating itself and of undergoing Darwinian selection. "Meme" has become a remarkably successful addition to everybody's cognitive toolkit. I want to suggest that the concept of a meme should be, if not replaced, at least supplemented with that of a cultural attractor.

The very success of the word "meme" is, or so it seems, an illustration of the idea of a meme: the word has now been used billions of time. But is the idea of a meme being replicated whenever the word is being used? Well, no. Not only do "memeticists" have many quite different definition of a meme, but also and more importantly most users of the term have no clear idea of what a meme might be. Each time, the term is being used with a vague meaning relevant in the circumstances. All these meanings overlap but they are not replications of one another. The idea of a meme, as opposed to the word "meme", may not be such a good example of a meme after all!

The case of the meme idea illustrates a general puzzle. Cultures do contain items — ideas, norms, tales, recipes, dances, rituals, tools, practices, and so on — that are produced again and again. These items remain self-similar over social space and time: in spite of variations, an Irish stew is an Irish stew, Little Red Riding Hood is Little Red Riding Hood and a samba is a samba. The obvious way to explain this stability at the macro level of the culture is, or so it seems, to assume fidelity at the micro level of interindividual transmission. Little Red Riding Hood must have been replicated faithfully enough most of the time for the tale to have remained self-similar over centuries of oral transmission or else the story would have drifted in all kinds of ways and the tale itself would have vanished like water in the sand. Macro stability implies micro fidelity. Right? Well, no. When we study micro processes of transmission — leaving aside those that use techniques of strict replication such as printing or internet forwarding — what we observe is a mix of preservation of the model and of construction of a version that suits the capacities and interests of the transmitter. From one version to the next, the changes may be small, but when they occur at the population scale, their cumulative effect should compromise the stability of cultural items. But — and here lies the puzzle — they don't. What, if not fidelity, explains stability?

Well, bits of culture — memes if you want to dilute the notion and call them that — remain self-similar not because they are replicated again and again but because variations that occur at almost every turn in their repeated transmission, rather than resulting in "random walks" drifting away in all directions from an initial model, tend to gravitate around cultural attractors. Ending Little Red Riding Hood when the wolf eats the child would make for a simpler story to remember, but a Happy Ending is too powerful a cultural attractor. If a person had only heard the story ending with the wolf's meal, my guess is that either she would not have retold it at all — and that is selection — , or she would have modified by reconstructing a happy ending — and this is attraction. Little Red Riding Hood has remained culturally stable not because it has been faithfully replicated all along, but because the variations present in all its versions have tended to cancel one another out.

Why should there be cultural attractors at all? Because there are in our minds, our bodies, and our environment biasing factors that affect the way we interpret and re-produce ideas and behaviors. (I write "re-produce" with a hyphen because, more often than not, we produce a new token of the same type without reproducing in the usual sense of copying some previous tokens.) When these biasing factors are shared in a population, cultural attractors emerge.

Here are a few rudimentary examples.

Rounded numbers are cultural attractors: they are easier to remember and provide better symbols for magnitudes. So, we celebrate twentieth wedding anniversaries, hundredth issue of journals, millionth copy sold of a record, and so on. This, in turn, creates a special cultural attractor for prices, just below rounded numbers — $9.99 or $9,990 are likely price tags — , so as to avoid the evocation of a higher magnitude.

In the diffusion of techniques and artifacts, efficiency is a powerful cultural attractor. Paleolithic hunters learning from their elders how to manufacture and use bows and arrows were aiming not so much at copying the elders than at becoming themselves as good as possible at shooting arrows. Much more than faithful replication, this attraction of efficiency when there aren't that many ways of being efficient, explains the cultural stability (and also the historical transformations) of various technical traditions.

In principle there should be no limit to the diversity of supernatural beings humans can imagine. However, as Pascal Boyer has argued, only a limited repertoire of such beings is exploited in human religions. Its members — ghosts, gods, ancestor spirits, dragons, and so on — have all in common two features. On the one hand, they each violate some major intuitive expectations about living beings: expectation of mortality, of belonging to one and only one species, of being limited in one's access to information, and so on. On the other hand, they satisfy all other intuitive expectations and are therefore, in spite of their supernaturalness, rather predictable. Why should this be so? Because being "minimally counterintuitive" (Boyer's phrase) makes for "relevant mysteries" (my phrase) and is a cultural attractor. Imaginary beings that are either less or more counterintuitive than that are forgotten or are transformed in the direction of this attractor.

And what is the attractor around which the "meme" meme gravitate? The meme idea — or rather a constellation of trivialized versions of it — has become an extraordinarily successful bit of contemporary culture not because it has been faithfully replicated again and again, but because our conversation often does revolve — and here is the cultural attractor — around remarkably successful bits of culture that, in the time of mass media and the internet, pop up more and more frequently and are indeed quite relevant to our understanding of the world we live in. They attract our attention even when — or, possibly, especially when — we don't understand that well what they are and how they come about. The meaning of "meme" has drifted from Dawkins precise scientific idea to a means to refer to these striking and puzzling objects.

This was my answer. Let me end by sharing a question (which time will answer): is the idea of a cultural attractor itself close enough to a cultural attractor for a version of it to become in turn a "meme"?

Various concepts come to mind for inclusion in that cognitive toolkit. "Emergence," or related to that, "the failure of reductionism" — mistrust the idea that if you want to understand a complex phenomenon, the only tool of science to use is to break it into its component parts, study them individually in isolation, and then glue the itty-bitty little pieces back together. This often doesn't work and, increasingly, it seems like it doesn't work for the most interesting and important phenomena out there. To wit — you have a watch that doesn't run correctly and often, indeed, you can fix it by breaking it down to its component parts and finding the gear that has had a tooth break (actually, I haven't a clue if there is any clock on earth that still works this way). But if you have a cloud that doesn't rain, you don't break it down to its component parts. Ditto for a person whose mind doesn't work right. Or for going about understanding the problems of a society or ecosystem. So that was a scientific concept that was tempting to cite.

Related to that are terms like "synergy" and "interdisciplinary," but heaven save us from having to hear more about those words. There are now whole areas of science where you can't get a faculty position unless you work one of those words into the title of your job talk and have it tattooed on the small of your back.

Another useful scientific concept is "genetic vulnerability." This would be great if it found its way into everyone's cognitive toolkit because its evil cousins of genetic inevitability and genetic determinism are already deeply entrenched there, and with long long legacies of bad consequences. Everyone should be taught about work like that of Avshalom Caspi and colleagues, who looked at genetic polymorphisms related to various neurotransmitter systems that are associated with psychiatric disorders and anti-social behaviors. Ah ha, far too many people will say, drawing on that nearly useless, misshapen tool of genetic determinism, have one of those polymorphisms and you're hosed by inevitability. And instead, what those studies beautifully demonstrate is how these polymorphisms carry essentially zero increased risk of those disorders…..unless you grow up in particularly malign environments. Genetic determinism, my tuches.

But the scientific concept that I've chosen is one that is useful simply because it isn't a scientific concept, can be the antithesis of — "anecdotalism." Every good journalist knows its power — start an article with statistics about foreclosure rates or feature a family victimized by some bank? No brainer. Display maps showing the magnitudes of refugees flowing out of Darfur or the face of one starving orphan in a camp? Obvious choice. Galvanize the readership.

But anecdotalism is potentially a domain of distortion as well. Absorb the lessons of science and cut saturated fats from your diet, or cite the uncle of the spouse of a friend who eats nothing but pork rinds and is still pumping iron at age 110? Depend on one of the foundations of the 20th century's extension of life span and vaccinate your child, or obsess over a National Enquirer-esque horror story of one vaccination disaster and don't immunize? I shudder at the current potential for another case of anecdotalism — I write four days after the Arizona shooting of Gabby Giffords and 19 other people by Jared Loughner. As of this writing, experts such as the esteemed psychiatrist Fuller Torrey are guessing that Loughner is a paranoid schizophrenic. And if this is true, this anecdotalism will give new legs to the tragic misconception that the mentally ill are more dangerous than the rest of us.

So maybe when I say argue for "anecdotalism" going into everyone's cognitive toolkit, I am really arguing for two things to be incorporated — a) appreciation of how distortive it can be, and b) recognition, in a salute to the work of people like Tversky and Kahnemann, of its magnetic pull, its cognitive satisfaction. As a social primate complete with a region of the cortex specialized for face recognition, the individual face — whether literal or metaphorical — has a special power. But unappealing, unintuitive patterns of statistics and variation generally teach us much more.

On its face, defeasibility is a modest concept with roots in logic and epistemology. An inference is defeasible if it can potentially be "defeated" in light of additional information. Unlike deductively sound conclusions, the products of defeasible reasoning remain subject to revision, held tentatively no matter how firmly.

All scientific claims — whether textbook pronouncements or haphazard speculations — are held defeasibly. It is a hallmark of the scientific process that claims are forever vulnerable to refinement and rejection, hostage to what the future could bring. Far from being a weakness, this is a source of science's greatness. Because scientific inferences are defeasible, they remain responsive to a world that can reveal itself gradually, change over time, and deviate from our dearest assumptions.

The concept of defeasibilility has proven valuable in characterizing artificial and natural intelligence. Everyday inferences, no less than scientific inferences, are vetted by the harsh judge of novel data: additional information that can potentially defeat current beliefs. On further inspection, the antique may turn out to be a fake and the alleged culprit an innocent victim. Dealing with an uncertain world forces cognitive systems to abandon the comforts of deduction and engage in defeasible reasoning.

Defeasibility is a powerful concept when we recognize it not as a modest term of art, but as the proper attitude towards all belief. Between blind faith and radical skepticism is a vast but sparsely populated space where defeasibility finds its home. Irreversible commitments would be foolish; boundless doubt paralyzing. Defeasible beliefs provide the provisional certainty necessary to navigate an uncertain world.

Recognizing the potential revisability of our beliefs is a prerequisite to rational discourse and progress, be it in science, politics, religion, or the mundane negotiations of daily life. Consider the world we could live in if all of our local and global leaders, if all of our personal and professional friends and foes, recognized the defeasibility of their beliefs and acted accordingly. That sure sounds like progress to me. But of course, I could be wrong.

A wider understanding of the fact that you can't prove a negative would, in my view, do a great deal to upgrade the public debate around science and technology.

As a journalist I have lost count of the number of times that people have demanded that a particular technology be "proven to do no harm". This is, of course, impossible, in just the same way that proving that there are no black swans is impossible. You can look for a black swan (harm) in various ways, but if you fail to find one that does not mean that none exists: absence of evidence is not evidence of absence.

All you can do is look again for harm, in a different way. If you still fail to find it after looking in all the ways you can possibly think of, the question is still open: "lack of evidence of harm" means both "safe as far as we can tell" and "we still can't be sure if it's safe or not".

Scientists are often accused of logic-chopping when they point this out. But it would be immensely helpful to public discourse if there was a wider understanding that you can show something is definitely dangerous, but you cannot show it is definitely safe.

If you used a personal computer 25 years ago, everything you needed to worry about was taking place in the box in front of you. Today, the applications you use over the course of an hour are scattered across computers all over the world; for the most part, we've lost the ability to tell where our data sits at all. We invent terms to express this lost sense of direction: our messages, photos, and on-line profiles are all somewhere in "The Cloud".

The Cloud is not a single thing; what you think of as your Gmail account or Facebook profile is in fact made possible by the teamwork of a huge number of physically dispersed components — a distributed system, in the language of computer science. But we can think of it as a single thing, and this is the broader point: The ideas of distributed systems apply whenever we see many small things working independently but cooperatively to produce the illusion of a single unified experience. This effect takes place not just on the Internet, but in many other domains as well. Consider for example a large corporation that is able to release new products and make public announcements as though it were a single actor, when we know that at a more detailed level it consists of tens of thousands of employees. Or a massive ant colony engaged in coordinated exploration, or the neurons of your brain creating your experience of the present moment.

The challenge for a distributed system is to achieve this illusion of a single unified behavior in the face of so much underlying complexity. And this broad challenge, appropriately, is in fact composed of many smaller challenges in tension with each other.

One basic piece of the puzzle is the problem of consistency. Each component of a distributed system sees different things and has a limited ability to communicate with everyone else, so different parts of the system can develop views of the world that are mutually inconsistent. There are numerous examples of how this can lead to trouble, both in technological domains and beyond. Your handheld device doesn't sync with your e-mail, so you act without realizing that there's already been a reply to your message. Two people across the country both reserve seat 5F on the same flight at the same time. An executive in an organization "didn't get the memo" and so strays off-message. A platoon attacks too soon and alerts the enemy.

It is natural to try "fixing" these kinds of problems by enforcing a single global view of the world, and requiring all parts of the system to constantly refer to this global view before acting. But this undercuts many of the reasons why one uses a distributed system in the first place. It makes the component that provides the global view a massive bottleneck, and a highly dangerous single point of potential failure. The corporation doesn't function if the CEO has to sign off on every decision.

To get a more concrete sense for some of the underlying design issues, it helps to walk through an example in a little detail, a basic kind of situation in which we try to achieve a desired outcome with information and actions that are divided over multiple participants. The example is the problem of sharing information securely: imagine trying to back up a sensitive database on multiple computers, while protecting the data so that it can only be reconstructed if a majority of the backup computers cooperate. But since the question of secure information sharing ultimately has nothing specifically to do with computers or the Internet, let's formulate it instead using a story about a band of pirates and a buried treasure.

Suppose that an aging Pirate King knows the location of a secret treasure, and before retiring he intends to share the secret among his five shiftless sons. He wants them to be able to recover the treasure if three or more of them work together, but he also wants to prevent a "splinter group" of one or two from being able to get the treasure on their own. To do this, he plans to split the secret of the location into five "shares," giving one to each son, in such a way that he ensures the following condition. If at any point in the future, at least three of the sons pool their shares of the secret, then they will know enough to recover the treasure. But if only one or two pool their shares, they will not have enough information.

How to do this? It's not hard to invent ways of creating five clues so that all of them are necessary for finding the treasure. But this would require unanimity among the five sons before the treasure could be found. How can we do it so that cooperation among any three is enough, and cooperation among any two is insufficient?

Like many deep insights, the answer is easy to understand in retrospect. The Pirate King draws a secret circle on the globe (known only to himself) and tells his sons that he's buried the treasure at the exact southernmost point on this circle. He then tells each son a different point on this circle. Three points are enough to uniquely reconstruct a circle, so any three pirates can pool their information, identify the circle, and find the treasure. But for any two pirates, an infinity of circles pass through their two points, and they cannot know which is the one they need for recovering the secret. It's a powerful trick, and broadly applicable; in fact, versions of this secret-sharing scheme form a basic principle of modern data security, discovered by the cryptographer Adi Shamir, where arbitrary data can be encoded using points on a curve, and reconstructed from knowledge of other points on the same curve.

The literature on distributed systems is rich with ideas in this spirit. More generally, the principles of distributed systems give us a way to reason about the difficulties inherent in complex systems built from many interacting parts. And so to the extent that we sometimes are fortunate enough to get the impression of a unified Web, a unified global banking system, or a unified sensory experience, we should think about the myriad challenges involved in keeping these experiences whole.

Okay, okay: it's a terrible phrase. (In my defense, I didn't coin it. Philosophers of science have been kicking it around for a while.) But if "The Pessimistic Meta-Induction from the History of Science" is cumbersome to say and difficult to remember, it is also a great idea. In fact, as the "meta" part suggests, it's the kind of idea that puts all other ideas into perspective.

Here's the gist: because so many scientific theories from bygone eras have turned out to be wrong, we must assume that most of today's theories will eventually prove incorrect as well. And what goes for science goes in general. Politics, economics, technology, law, religion, medicine, child-rearing, education: no matter the domain of life, one generation's verities so often become the next generation's falsehoods that we might as well have a Pessimistic Meta-Induction from the History of Everything.

Good scientists understand this. They recognize that they are part of a long process of approximation. They know that they are constructing models rather than revealing reality. They are comfortable working under conditions of uncertainty — not just the local uncertainty of "Will this data bear out my hypothesis?", but the sweeping uncertainty of simultaneously pursuing and being cut off from absolute truth.

The rest of us, by contrast, often engage in a kind of tacit chronological exceptionalism. Unlike all those suckers who fell for the flat earth or the geocentric universe or cold fusion or the cosmological constant, we ourselves have the great good luck to be alive during the very apex of accurate human thought. The literary critic Harry Levin put this nicely: "The habit of equating one's age with the apogee of civilization, one's town with the hub of the universe, one's horizons with the limits of human awareness, is paradoxically widespread." At best, we nurture the fantasy that knowledge is always cumulative, and therefore concede that future eras will know more than we do. But we ignore or resist the fact that knowledge collapses as often as it accretes, that our own most cherished beliefs might appear patently false to posterity.

That fact is the essence of the meta-induction — and yet, despite its name, this idea is not pessimistic. Or rather, it is only pessimistic if you hate being wrong. If, by contrast, you think that uncovering your mistakes is one of the best ways to revise and improve your understanding of the world, then this is actually a highly optimisticinsight.

The idea behind the meta-induction is that all of our theories are fundamentally provisional and quite possibly wrong. If we can add that idea to our cognitive toolkit, we will be better able to listen with curiosity and empathy to those whose theories contradict our own. We will be better able to pay attention to counterevidencethose anomalous bits of data that make our picture of the world a little weirder, more mysterious, less clean, less done. And we will be able to hold our own beliefs a bit more humbly, in the happy knowledge that better ideas are almost certainly on the way.

In 1971, when I was a teenager, my father died in a big airplane crash. Somehow I began to turn 'serious', trying to understand the world around me and my place in it, looking for meaning and sense, beginning to realize: everything was very different than I had previously assumed in the innocence of childhood.

It was the beginning of my own "building a cognitive toolset" and I remember the pure joy of discovery, reading voraciously and — quite out of sync with friends and school — I devoured encyclopedias, philosophy, biographies and... science fiction.

One such story stayed with me and one paragraph within it especially:
"We need to make use of Thargola's Sword! The principle of Parsimony. 
First put forth by the medieval philosopher Thargola14, who said, 
'We must drive a sword through any hypothesis that is not strictly necessary"

That really made me think — and rethink again...
Finding out who this man might have been took quite a while, but it was also another beginning: a love affair with libraries, large tomes, dusty bindings... surfing knowledge, as it were.

And I did discover: there had been a monk named Guillelmi, from a hamlet surrounded by oaks, apocryphally called 'William of Ockham'. He crossed my path again years later when lecturing in Munich near Occam Street, realizing he had spent the last 20 years of his life there, under King Ludwig IV in the mid 1300s.

Isaac Asimov had pilfered, or let's say homaged, good old Guillelmi for what is now known in many variants as "Ockham's razor", such as 
"Plurality should not be posited without necessity."
"Entities are not to be multiplied beyond necessity"
or more general and colloquial and a bit less transliterated from Latin:
A simpler explanation invoking fewer hypothetical constructs is preferrable.

And there it was, the dancing interplay between Simplex and Complex, which has fascinated me in so many forms ever since. For me, it is very near the center of "understanding the world", as our question posited.

Could it really be true, that the innocent sounding 'keep it simple' is really such an optimal strategy for dealing with questions large and small, scientific as well as personal? Surely, trying to eliminate superflous assumptions can be a useful tenet, and can be found from Sagan to Hawking as part of their approach to thinking in science. But something never quite felt right to me — intuitively it was clear that sometimesthings are just not simple — and that merely "the simplest" of all explanations cannot be taken as truth or proof.

Any detective story would pride itself in not using the most obvious explanation who did it or how it happened. 
Designing a car to 'have the optimal feel going into a curve at high speed' will require hugely complex systems to finally arrive at "simply good". 
Water running downhill will take a meandering path instead of the straight line.

Both are examples for a domain shift: the non-simple solution is still "the easiest" seen from another viewpoint: for the water the least energy used going down the shallowest slope is more important than taking the straightest line from A to B.

And that is one of the issues with Ockham: 
The definition of what "simple" is — can already be anything but simple.
And what "simpler" is — well, it just doesn't get any simpler there.

There is that big difference between simple and simplistic.
And seen more abstractly, the principle of simple things leading to complexity dances in parallel and involved me deeply throughout my life.

In the early seventies I also began tinkering with the first large scale modular synthesizers, finding quickly how hard it is to recreate seemingly 'simple sounds'.
There was unexpected complexity in a single note struck on a piano that eluded even dozens of oscillators and filters, by magnitudes.

Lately one of many projects has been to revisit the aesthetic space of scientific visualizations, and another, which is the epitomy of mathematics made tangible: Fractals — which I had done almost 20 years ago with virtuoso coder Ben Weiss, now enjoying them via realtime flythroughs on a handheld little smartphone. 
Here was the most extreme example: a tiny formula, barely one line on paper, used recursively iterated it yields worlds of complex images of amazing beauty.
(Ben had the distinct pleasure of showing Benoit Mandelbrot an alpha version at the last TED just months before his death)

My hesitation towards overuse of parsimony was expressed perfectly in the quote by Albert Einstein, arguably the counterpart "blade" to Ockham's razor:
"Things should be made as simple as possible — but not simpler"

And there we have the perfect application of its truth, used recursively on itself: Neither Einstein nor Ockham actually used the exact words as quoted!

After I sifted through dozens of books, his collected works and letters in German, the Einstein archives: nowhere there, nor in Britannica, Wikipedia or Wikiquote was anyone able to substantiate exact sources, and the same applies to Ockham. If anything can be found, it is earlier precedences...;)

Surely one can amass retweeted, reblogged and regurgitated instances for both very quickly — they have become memes, of course. One could also take the standpoint that in each case they certainly 'might' have said it 'just like that', since each used several expressions quite similar in form and spirit.
But just to attribute the exact words because they are kind of close would be, well..another case of: it is not that simple!

And there is a huge difference between additional and redundantinformation.
(Or else one could lose the second redundant "ein" in "Einstein" ?)

Linguistic jesting aside: Nonetheless, the Razor and the Blade constitute a very useful combination of approaching analytical thinking. 
Shaving away non-essential conjectures is a good thing, a worthy inclusion in "everybody's toolkit" — and so is the corollary: not to overdo it!

And my own bottom line: There is nothing more complex than simplicity.

The names Lysenko and Lamarck are nearly synonymous with bad science — worse than merely mediocre science because of the huge political and economical consequences.

From 1927 to 1964 Lysenkov managed to keep the "theory of the inheritance of acquired characteristics" dogmatically directing Soviet agriculture and science. Andrei Sakharov and other Soviet physicists finally provoked the fall of this cabal in the 1960s, blaming it for the "shameful backwardness of Soviet biology and of genetics in particular … defamation, firing, arrest, even death, of many genuine scientists".

At the opposite (yet equally discredited) end of the genetic theory spectrum was the Galtonian eugenic movement, which from 1883 onward grew in popularity in many countries until the 1948 Universal Declaration of Human Rights, ("the most translated document in the world") stated that "Men and women of full age, without any limitation due to race, nationality or religion, have the right to marry and to found a family." Nevertheless, forced sterilizations persisted into the 1970s. The "shorthand abstraction" is that Lysenkoism overestimated the impact of environment and eugenics overestimated the role of genetics.

One form of scientific blindness occurs, as above, when a theory displays exceptional political or religious appeal. But another source of blindness arises when we rebound from catastrophic failures of pseudoscience (or science).

We might conclude from the two aforementioned genetic disasters that we only need to police abuses of our human germ-line inheritance. Combining the above with the ever-simmering debate on Darwin, we might develop a bias that human evolution has stopped or that "design" has no role.

But we are well into an unprecedented new phase of evolution in which we must generalize beyond our DNA-centric world-view. We now inherit acquired characteristics. We always have, but now this feature is dominant and exponential. We apply eugenics at the individual family level (where it is a right) not the governmental level (where it is a wrong). Moreover, we might aim for the same misguided targets that eugenics chose (i.e. uniformity around "ideal" traits), via training and medications.

Evolution has accelerated from geologic speed to internet speed — still employing random mutation and selection, but also using non-random intelligent design — which makes it even faster. We are losing species — not just by extinction, but by merger. There are no longer species barriers between humans, bacteria and plants — or even between humans and machines.

Short-hand abstractions are only one device that we employ to construct the "Flynn Effect". How many of us noticed the minor milestone when the SAT tests first permitted calculators? How many of us have participated in conversations semi-discreetly augmented by Google or text messaging? Even without invoking artificial intelligence, how far are we from commonplace augmentation of our decision-making the way we have augmented our math, memory, and muscles?

New York City, my new home, teaches you, that while we are social creatures, it's often best not to admit it.

As you weave among the obstacles on the sidewalks of Manhattan, it's easy to get distracted from your thoughts and pay attention to the people you're encountering. It's okay to do that if you're at a stop, but if you're in motion, if your eyes engage with another, that signals that you would like to negotiate.

Not good. A sign of weakness. Whether the oncoming traffic is aware or not, he or she will take advantage of this weakness and charge right into your path, all the while not making eye contact. There is no appeal. All you can do is shift out of their path, but even this won't avoid a collision because your adversary will unconsciously shift closer to you. Your weakness is attractive. Your space is up for grabs. At this point you have no choice but to collide, and in the etiquette of New York street walking you're responsible.

That's why the people who check their smartphones for text messages or emails while walking so totally command the sidewalks. They are heat-seeking missiles, and it's your heat they seek.

I don't think this is just New York, it's a feature of the human species. We seek companionship.

For a while in 2005 I lived on the beach in northeast Florida outside St Augustine. The beach is so long and relatively empty, they let you drive on the beach to find the perfect spot to bathe, and if you're willing to drive a bit you can be alone. So I would drive to a secluded spot, park my car and go out into the surf. When I came back, more often than not, there was a car parked right next to mine. They could have parked anywhere in a mile in either direction and had it all to themselves.

Add that to your cognitive toolkit!

An important part of my scientific toolkit is how to think about things in the world over a wide range of magnitudes and time scales. This involves first understanding powers of ten; second, visualizing data over a wide range of magnitudes on graphs using logarithmic scales; and third, appreciating the meaning of magnitude scales such as the decibel (dB) scale for the loudness of sounds and the Richter scale for the strengths of earthquakes.

This toolkit ought to be a part of everyone thinking, but sadly I have found that even well educated nonscientists are flummoxed by log scales and can only vaguely grasp the difference between an earthquake on a Richter scale of 6 and 8 (a thousand times more energy released). Thinking in powers of 10 is such a basic skill that it ought to be taught along with integers in elementary school.

Scaling laws are found throughout nature. Galileo in 1638 pointed out that large animals have disproportionately thicker leg bones than small animals to support the weight of the animal. The heavier the animal, the more stout their legs need to be. This leads to a prediction that the thickness of the leg bone should scale with the 3/2 power of the length of the bone.

Another interesting scaling law is that between the volume of the cortical white matter, corresponding to the long-distance wires between cortical areas, and the gray matter, where the computing takes place. For mammals ranging over 5 orders of magnitude in weight from a pygmy shrew to an elephant, the white matter scales as the 5/4 power of the gray matter. This means that the bigger the brain, the disproportionately larger the fraction of the volume taken up by cortical wiring used for communication compared to the computing machinery.

I am concerned that students I teach have lost the art of estimating with powers of 10. When I was a student I used a slide rule to compute, but students now use calculators. A slide rule lets you carry out a long series of multiplications and divisions by adding and subtracting the logs of numbers; but at the end you need to figure out the powers of 10 by making a rough estimate. A calculator keeps track of this for you, but if you make a mistake in keying in a number you can be off by 10 orders of magnitude, which happens to students who don't have a feeling for orders of magnitude.

A final reason why familiarity with powers of 10 would improve everyone's cognitive toolkit is that it helps us comprehend our life and the world in which we live:

How many seconds are there in a lifetime? 109 sec

A second is an arbitrary time unit, but one that is based on our experience. Our visual system is bombarded by snapshots at a rate of around 3 per second caused by rapid eye movements called saccades. Athletes often win or lose a race by a fraction of a second. If you earned a dollar for every second in your life you would be a billionaire. However, a second can feel like a minute in front of an audience and a quiet weekend can disappear in a flash. As a child, a summer seemed to last forever, but as an adult, summer is over almost before it begins. William James speculated that subjective time was measured in novel experiences, which become rarer as you get older. Perhaps life is lived on a logarithmic time scale, compressed toward the end.

What is the GDP of the world? $1014

A billion dollars was once worth a lot, but there is now a long list of multibillionaires. The US government recently stimulated the world economy by loaning several trillion dollars to banks. It is difficult to grasp how much a trillion dollars ($10[12] ) represents, but several clever videos on YouTube (key words: trillion dollars) illustrate this with physical comparisons (a giant pile of $100 bills) and what you can buy with it (10 years of US response to 9/11). When you start thinking about the world economy, the trillions of dollars add up. A trillion here, a trillion there, pretty soon your talking about real money. But so far there aren't any trillionaires.

How many synapses are there in the brain? 1015

Two neurons can communicate with each other at a synapse, which is the computational unit in the brain. The typical cortical synapse is less than a micron in diameter (10[-6] meter), near the resolution limit of the light microscope. If the economy of the world is a stretch for us to contemplate, thinking about all the synapses in your head is mind boggling. If I had a dollar for every synapse in your brain I could support the current economy of the world for 10 years. Cortical neurons on average fire once a second, which implies a bandwidth of around 10[15] bits per second, greater than the total bandwidth of the internet backbone.

How many seconds will the sun shine? 1017 sec

Our sun has shined for billions of years and will continue to shine for billions more. The universe seems to be standing still during our lifetime, but on longer time scales the universe is filled with events of enormous violence. The spatial scales are also immense. Our space-time trajectory is a very tiny part of the universe, but we can at least attach powers of 10 to it and put it into perspective.

Humans are the giraffes of altruism. We're freaks of nature, able (at our best) to achieve ant-like levels of service to the group. We readily join together to create superorganisms, but unlike the eusocial insects, we do it with blatant disregard for kinship, and we do it temporarily, and contingent upon special circumstances (particularly intergroup conflict, as is found in war, sports, and business).

Ever since the publication of G. C. Williams' 1966 classic Adaptation and Natural Selection, biologists have joined with social scientists to form an altruism debunkery society. Any human or animal act that appears altruistic has been explained away as selfishness in disguise, linked ultimately to kin selection (genes help copies of themselves), or reciprocal altruism (agents help only to the extent that they can expect a positive return, including to their reputations).

But in the last few years there's been a growing acceptance of the fact that "Life is a self-replicating hierarchy of levels," and natural selection operates on multiple levels simultaneously, as Bert Hölldobler and E. O. Wilson put it in their recent book, The Superorganism. Whenever the free-rider problem is solved at one level of the hierarchy, such that individual agents can link their fortunes and live or die as a group, a superorganism is formed. Such "major transitions" are rare in the history of life, but when they have happened, the resulting superorganisms have been wildly successful. (Eukaryotic cells, multicelled organisms, and ant colonies are all examples of such transitions).

Building on Hölldobler and Wilson's work on insect societies, we can define a "contingent superorganism" as a group of people that form a functional unit in which each is willing to sacrifice for the good of the group in order to surmount a challenge or threat, usually from another contingent superorganism. It is the most noble and the most terrifying human ability. It is the secret of successful hive-like organizations, from the hierarchical corporations of the 1950s to the more fluid dot-coms of today. It is the purpose of basic training in the military. It is the reward that makes people want to join fraternities, fire departments, and rock bands. It is the dream of fascism.

Having the term "contingent superorganism" in our cognitive toolkit may help people to overcome 40 years of biological reductionism and gain a more accurate view of human nature, human altruism, and human potential. It can explain our otherwise freakish love of melding ourselves (temporarily, contingently) into something larger than ourselves.

Everyone is familiar with Digital Code, or the shorthand IT. Soon all may be discoursing about Life Code…

It took a while to learn how to read life code; Mendel's initial cookbook was largely ignored. Darwin knew but refused, for decades, to publish such controversial material. Even a term that now lies within every cheesy PR description of a firm, on jeans, and pop psych books…DNA… was largely ignored after its 1953 discovery. For close to a decade very few cited Watson and Crick. They were not even nominated, by anyone, for a Nobel till after 1960, despite the discovery of how life code is written.

First ignorance then controversy continued dogging life code as humanity moved from reading it to copying it. Tadpoles were cloned in 1952, but few focused until Dolly the sheep begat wonder, consternation, and fear. Much the same occurred with in vitro fertilization and Louise Brown, a breakthrough that got the Nobel in 2010, a mere 37 years after the first birth. Copying genes and dozens of species, leading to hundreds of thousands of almost identical animals is now commonplace. The latest controversy is no longer how do we deal with rare clones but should we eat them.

Much has occurred as we learned to read and copy life code, but there is still little understanding for what has occurred recently. But it is this third stage of life code, writing and re-writing, is by far the most important and profound change. 

Few realize, so far, that life code is spreading across industries, economies, countries, and cultures. As we begin to rewrite existing life, strange things evolve. Bacteria can be programmed to solve Sudoku puzzles. Viruses begin to create electronic circuits. As we write life from scratch, Venter, Smith et al. partner with Exxon to try to change the world's energy markets. Designer genes introduced by retroviruses, organs built from scratch, the first synthetic cells further examples of massive change.

We see more and more products, derived from life code, changing fields as diverse as energy, textiles, chemicals, IT, vaccines, medicines, space exploration, agriculture, fashion, finance, and real estate. And gradually, "life code" a concept that only got 559 Goggle hits in 2000, and fewer than 50,000 in 2009, becomes a part of the everyday public discourse.

Many of the Fortune 500 within the next decade will be companies based on the understanding and application of life code, much as has occurred over the past decades with digital code leading to the likes of Digital, Lotus, HP, IBM, Microsoft, Amazon, Google, and Facebook.

But this is just the beginning. The real change will become apparent as we re-write life code to morph the human species. We are already transitioning from a humanoid that is shaped by and shapes its own environment into a Homo evolutis, a species that directly and deliberately designs and shapes its own evolution and that of other species…

The most common misunderstanding about science is that scientists seek and find truth. They don't — they make and test models.

Kepler packing Platonic solids to explain the observed motion of planets made pretty good predictions, which were improved by his laws of planetary motion, which were improved by Newton's laws of motion, which were improved by Einstein's general relativity. Kepler didn't become wrong because of Newton being right, just as Newton didn't then become wrong because of Einstein being right; this succession of models differed in their assumptions, accuracy, and applicability, not their truth.

This is entirely unlike the polarizing battles that define so many areas of life: either my political party, or religion, or lifestyle is right, or yours is, and I believe in mine. The only thing that's shared is the certainty of infallibility.

Building models is very different from proclaiming truths. It's a never-ending process of discovery and refinement, not a war to win or destination to reach. Uncertainty is intrinsic to the process of finding out what you don't know, not a weakness to avoid. Bugs are features — violations of expectations are opportunities to refine them. And decisions are made by evaluating what works better, not by invoking received wisdom.

These are familiar aspects of the work of any scientist, or baby: it's not possible to learn to talk or walk without babbling or toddling to experiment with language and balance. Babies who keep babbling turn into scientists who formulate and test theories for a living. But it doesn't require professional training to make mental models — we're born with those skills. What's needed is not displacing them with the certainty of absolute truths that inhibit the exploration of ideas. Making sense of anything means making models that can predict outcomes and accommodate observations. Truth is a model.

There is a widely used notion that does plenty of damage: the notion of "scientifically proven". Nearly an oxymoron. The very foundation of science is to keep the door open to doubt. Precisely because we keep questioning everything, especially our own premises, we are always ready to improve our knowledge. Therefore a good scientist is never 'certain'. Lack of certainty is precisely what makes conclusions more reliable than the conclusions of those who are certain: because the good scientist will be ready to shift to a different point of view if better elements of evidence, or novel arguments emerge. Therefore certainty is not only something of no use, but is in fact damaging, if we value reliability.

Failure to appreciate the value of the lack of certainty is at the origin of much silliness in our society. Are we sure that the Earth is going to keep heating up, if we do not do anything? Are we sure of the details of the current theory of evolution? Are we sure that modern medicine is always a better strategy than traditional ones? No we are not, in none of these cases. But if from this lack of certainty we jump to the conviction that we better not care about global heating, that there is no evolution and the world was created six thousand years ago, or that traditional medicine must be more effective that the modern medicine, well, we are simply stupid. Still, many people do these silly inferences. Because the lack of certainty is perceived as a sign of weakness, instead of being what it is: the first source of our knowledge.

Every knowledge, even the most solid, carries a margin of uncertainty. (I am very sure about my own name ... but what if I just hit my head and got momentarily confused?) Knowledge itself is probabilistic in nature, a notion emphasized by some currents of philosophical pragmatism. Better understanding of the meaning of probability, and especially realizing that we never have, nor need, 'scientifically proven' facts, but only a sufficiently high degree of probability, in order to take decisions and act, would improve everybody' conceptual toolkit.

The idea that the brain is basically an engine of prediction is one that will, I believe, turn out to be very valuable not just within its current home (computational cognitive neuroscience) but across the board: for the arts, for the humanities, and for our own personal understanding of what it is to be a human being in contact with the world.

The term 'predictive coding' is currently used in many ways, across a variety of disciplines. The usage I recommend for the Everyday Cognitive Toolkit is, however, more restricted in scope. It concerns the way the brain exploits prediction and anticipation in making sense of incoming signals and using them to guide perception, thought, and action. Used in this way 'predictive coding' names a technically rich body of computational and neuroscientific research (key theorists include Dana Ballard, Tobias Egner, Paul Fletcher, Karl Friston, David Mumford, and Rajesh Rao) . This corpus of research uses mathematical principles and models that explore in detail the ways that this form of coding might underlie perception, and inform belief, choice, and reasoning.

The basic idea is simple. It is that to perceive the world is to successfully predict our own sensory states. The brain uses stored knowledge about the structure of the world and the probabilities of one state or event following another to generate a prediction of what the current state is likely to be, given the previous one and this body of knowledge. Mismatches between the prediction and the received signal generate error signals that nuance the prediction or (in more extreme cases) drive learning and plasticity.

We may contrast this with older models in which perception is a 'bottom-up' process, in which incoming information is progressively built (via some kind of evidence accumulation process, starting with simple features and working up) into a high-level model of the world. According to the predictive coding alternative, the reverse is the case. For the most part, we determine the low-level features by applying a cascade of predictions that begin at the very top; with our most general expectations about the nature and state of the world providing constraints on our successively more detailed (fine grain) predictions.

This inversion has some quite profound implications.

First, the notion of good ('veridical') sensory contact with the world becomes a matter of applying the right expectations to the incoming signal. Subtract such expectations and the best we can hope for are prediction errors that elicit plasticity and learning. This means, in effect, that all perception is some form of 'expert perception', and that the idea of accessing some kind of unvarnished sensory truth is untenable (unless that merely names another kind of trained, expert perception!).

Second, the time course of perception becomes critical. Predictive coding models suggest that what emerges first is the general gist (including the general affective feel) of the scene, with the details becoming progressively filled in as the brain uses that larger context — time and task allowing — to generate finer and finer predictions of detail. There is a very real sense in which we properly perceive the forest before the trees.

Third, the line between perception and cognition becomes blurred. What we perceive (or think we perceive) is heavily determined by what we know, and what we know (or think we know) is constantly conditioned on what we perceive (or think we perceive). This turns out to offer a powerful window on various pathologies of thought and action, explaining the way hallucinations and false beliefs go hand-in-hand in schizophrenia, as well as other more familiar states such as  'confirmation bias' (our tendency to 'spot' confirming evidence more readily than disconfirming evidence).

Fourth, if we now consider that prediction errors can be suppressed not just by changing predictions but by changing the things predicted, we have a simple and powerful explanation for behavior and the way we manipulate and sample our environment. In this view, action is there to make predictions come true and provides a nice account of phenomena that range from homeostasis to the maintenance of our emotional and interpersonal status quo.

Understanding perception as prediction thus offers, it seems to me, a powerful tool for appreciating both the power and the potential pitfalls of our primary way of being in contact with the world. Our primary contact with the world, all this suggests, is via our expectations about what we are about to see or experience. The notion of predictive coding, by offering a concise and technically rich way of gesturing at this fact, provides a cognitive tool that will more than earn its keep in science, law, ethics, and the understanding of our own daily experience.

The concept of constraint satisfaction is crucial for understanding and improving human reasoning and decision making. A "constraint" is a condition that must be taken into account when solving a problem or making a decision, and "constraint satisfaction" is the process of meeting the relevant constraints. The key idea is that often there are only a few ways to satisfy a full set of constraints simultaneously.

For example, when moving into a new house, my wife and I had to decide how to arrange the furniture in the bedroom. We had an old headboard, which was so rickety that it had to be leaned against a wall. This requirement was a constraint on the positioning of the headboard. The other pieces of furniture also had requirements (constraints) on where they could be placed. Specifically, we had two small end tables that had to be next to either side of the headboard; a chair that needed to be somewhere in the room; a reading lamp that needed to be next to the chair; and an old sofa that was missing one of its rear legs, and hence rested on a couple of books — and we wanted to position it so that people couldn't see the books. Here was the remarkable fact about our exercises in interior design: Virtually always, as soon as we selected the wall for the headboard, bang! The entire configuration of the room was determined. There was only one other wall large enough for the sofa, which in turn left only one space for the chair and lamp.

In general, the more constraints, the fewer the possible ways of satisfying them simultaneously. And this is especially the case when there are many "strong" constraints. A strong constraint is like the locations of the end tables: there are very few ways to satisfy them. In contrast, a "weak" constraint, such as the location of the headboard, can be satisfied in many ways (many positions along different walls would work).

What happens when some constraints are incompatible with others? For instance, say that you live far from a gas station and so you want to buy an electric automobile — but you don't have enough money to buy one. Not all constraints are equal in importance, and as long as the most important ones are satisfied "well enough," you may have reached a satisfactory solution. For example, although an optimal solution to your transportation needs might have been an electric car, a hybrid that gets excellent gas mileage might be good enough.

In addition, once you begin the constraint satisfaction process, you can make it more effective by seeking out additional constraints. For example, when you are deciding what car to buy, you might start with the constraints of (a) your budget and (b) your desire to avoid going to a filling station. You then might consider the size of car needed for your purposes, length of the warrantee, and styling. You may be willing to make tradeoffs, for example, by satisfying some constraints very well (such as mileage) but just barely satisfying others (e.g., styling). Even so, the mere fact of including additional constraints at all could be the deciding factor.

Constraint satisfaction is pervasive. For example:

• This is how detectives — from Sherlock Holmes to the Mentalist — crack their cases, treating each clue as a constraint and looking for a solution that satisfies them all. 

• This is what dating services strive to do — find the clients' constraints, identify which constraints are most important to him or her, and then see which of the available candidates best satisfies the constraints.

• This is what you go through when finding a new place to live, weighing the relative importance of constraints such as the size, price, location, and type of neighborhood.

• And this is what you are do when you get dressed in the morning: you choose clothes that "go with each other" (both in color and style).

Constraint satisfaction is pervasive in part because it does not require "perfect" solutions. It's up to you to decide what the most important constraints are, and just how many of the constraints in general must be satisfied (and how well they must be satisfied). Moreover, constraint satisfaction need not be linear: You can appreciate the entire set of constraints at the same time, throwing them into your "mental stewpot" and letting them simmer. And this process need not be conscious. "Mulling it over" seems to consist of engaging in unconscious constraint satisfaction.

Finally, much creativity emerges from constraint satisfaction. Many new recipes were created when chefs discovered that only specific ingredients were available — and they thus were either forced to substitute different ingredients or to come up with a new "solution" (dish) to be satisfied. Creativity can also emerge when you decide to change, exclude, or add a constraint. For example, Einstein had one of his major breakthroughs when he realized that time need not pass at a constant rate. Perhaps paradoxically, adding constraints can actually enhance creativity — if a task is too open or unstructured, it may be so unconstrained that it is difficult to devise any solution.

You see the pattern everywhere: the top 1% of the population control 35% of the wealth. On Twitter, the top 2% of users send 60% of the messages. In the health care system, the treatment for the most expensive fifth of patients create four-fifths of the overall cost. These figures are always reported as shocking, as if the normal order of things has been disrupted, as if the appearance of anything other than a completely linear distribution of money, or messages, or effort, is a surprise of the highest order.

It's not. Or rather, it shouldn't be.

The Italian economist Vilfredo Pareto undertook a study of market economies a century ago, and discovered that no matter what the country, the richest quintile of the population controlled most of the wealth. The effects of this Pareto Distribution go by many names — the80/20 Rule, Zipfs Law, the Power Law distribution, Winner-Take-All — but the basic shape of the underlying distribution is always the same: the richest or busiest or most connected participants in a system will account for much much more wealth, or activity, or connectedness than average.

Furthermore, this pattern is recursive. Within the top 20% of a system that exhibits a Pareto distribution, the top 20% of that slice will also account for disproportionately more of whatever is being measured, and so on. The most highly ranked element of such a system will be much more highly weighted than even the #2 item in the same chart. (The word "the" is not only the commonest word in English, it appears twice as often the second most common, "of".)

This pattern was so common, Pareto called it a "predictable imbalance"; despite this bit of century-old optimism, however, we are still failing to predict it, even though it is everywhere.

Part of our failure to expect the expected is that we have been taught that the paradigmatic distribution of large systems is the Gaussian distribution, commonly known as the bell curve. In a bell curve distribution like height, say, the average and the median (the middle point in the system) are the same — the average height of a hundred American women selected at random will be about 5'4", and the height of the 50th woman, ranked in height order, will also be 5'4".

Pareto distributions are nothing like that — the recursive 80/20 weighting means that the average is far from the middle. This in turn means that in such systems most people (or whatever is being measured) are below average, a pattern encapsulated in the old economics joke: "Bill Gates walks into a bar and makes everybody a millionaire, on average."

The Pareto distribution shows up in a remarkably wide array of complex systems. Together, "the" and "of" account for 10% of all words used in English. The most volatile day in the history of a stock market will typically be twice that of the second-most volatile, and ten times the tenth-most. Tag frequency on Flickr photos obeys a Pareto distribution, as does the magnitude of earthquakes, the popularity of books, the size of asteroids, and the social connectedness of your friends. The Pareto Principle is so basic to the sciences that special graph paper that shows Pareto distributions as straight lines rather than as steep curves is manufactured by the ream.

And yet, despite a century of scientific familiarity, samples drawn from Pareto distributions are routinely presented to the public as anomalies, which prevents us from thinking clearly about the world. We should stop thinking that average family income and the income of the median family have anything to do with one another, or that enthusiastic and normal users of communications tools are doing similar things, or that extroverts should be only moderately more connected than normal people. We should stop thinking that the largest future earthquake or market panic will be as large as the largest historical one; the longer a system persists, the likelier it is that an event twice as large as all previous ones is coming.

This doesn't mean that such distributions are beyond our ability to affect them. A Pareto curve's decline from head to tail can be more or less dramatic, and in some cases, political or social intervention can affect that slope — tax policy can raise or lower the share of income of the top 1% of a population, just as there are ways to constrain the overall volatility of markets, or to reduce the band in which health care costs can fluctuate.

However, until we assume such systems are Pareto distributions, and will remain so even after any such intervention, we haven't even started thinking about them in the right way; in all likelihood, we're trying to put a Pareto peg in a Gaussian hole. A hundred years after the discovery of this predictable imbalance, we should finish the job and actually start expecting it.

Everybody knows about the familiar large-scale cycles of nature: day follows night follows day summer-fall-winter-spring-summer-fall-winter-spring, the water cycle of evaporation and precipitation that refills our lakes, scours our rivers and restores the water supply of every living thing on the planet. But not everybody appreciates how cycles — every spatial and temporal scale from the atomic to the astronomic — are quite literally the hidden spinning motors that power all the wonderful phenomena of nature.

Nikolaus Otto built and sold the first internal combustion gasoline engine in 1861, and Rudolf Diesel built his engine in 1897, two brilliant inventions that changed the world. Each exploits a cycle, the four-stroke Otto cycle or the two-stroke Diesel cycle, that accomplishes some work and then restores the system to the original position so that it is ready to accomplish some more work. The details of these cycles are ingenious, and they have been discovered and optimized by an R & D cycle of invention that is several centuries old. An even more elegant, micro-miniaturized engine is the Krebs cycle, discovered in 1937 by Hans Krebs, but invented over millions of years of evolution at the dawn of life. It is the eight-stroke chemical reaction that turns fuel — into energy in the process of metabolism that is essential to all life, from bacteria to redwood trees.

Biochemical cycles like the Krebs cycle are responsible for all the motion, growth, self-repair, and reproduction in the living world, wheels within wheels within wheels, a clockwork with trillions of moving parts, and each clock has to be rewound, restored to step one so that it can do its duty again. All of these have been optimized by the grand Darwinian cycle of reproduction, generation after generation, picking up fortuitous improvements over the eons.

At a completely different scale, our ancestors discovered the efficacy of cycles in one of the great advances of human prehistory: the role of repetition in manufacture. Take a stick and rub it with a stone and almost nothing happens — few scratches are the only visible sign of change. Rub it a hundred times and there is still nothing much to see. But rub it just so, for a few thousand times, and you can turn it into an uncannily straight arrow shaft. By the accumulation of imperceptible increments, the cyclical process creates something altogether new. The foresight and self-control required for such projects was itself a novelty, a vast improvement over the repetitive but largely instinctual and mindless building and shaping processes of other animals. And that novelty was, of course, itself a product of the Darwinian cycle, enhanced eventually by the swifter cycle of cultural evolution, in which the reproduction of the technique wasn't passed on to offspring through the genes but transmitted among non-kin conspecifics who picked up the trick of imitation.

The first ancestor who polished a stone into a handsomely symmetrical hand axe must have looked pretty stupid in the process. There he sat, rubbing away for hours on end, to no apparent effect. But hidden in the interstices of all the mindless repetition was a process of gradual refinement that was well nigh invisible to the naked eye designed by evolution to detect changes occurring at a much faster tempo. The same appearance of futility has occasionally misled sophisticated biologists. In his elegant book, Wetware, the molecular and cell biologist Dennis Bray describes cycles in the nervous system:

In a typical signaling pathway, proteins are continually being modified and demodified. Kinases and phosphates work ceaselessly like ants in a nest, adding phosphate groups to proteins and removing them again. It seems a pointless exercise, especially when you consider that each cycle of addition and removal costs the cell one molecule of — one unit of precious energy. Indeed, cyclic reactions of this kind were initially labeled "futile." But the adjective is misleading. The addition of phosphate groups to proteins is the single most common reaction in cells and underpins a large proportion of the computations they perform. Far from being futile, this cyclic reaction provides the cell with an essential resource: a flexible and rapidly tunable device.

The word "computations" is aptly chosen, for it turns out that all the "magic" of cognition depends, just as life itself does, on cycles within cycles of recurrent, re-entrant, reflexive information-transformation processes from the biochemical scale within the neuron to the whole brain sleep cycle, waves of cerebral activity and recovery revealed by EEGs. Computer programmers have been exploring the space of possible computations for less than a century, but their harvest of invention and discovery so far includes millions of loops within loops within loops. The secret ingredient of improvement is always the same: practice, practice, practice.

It is useful to remember that Darwinian evolution is just one kind of accumulative, refining cycle. There are plenty of others. The problem of the origin of life can be made to look insoluble ("irreducibly complex") if one argues, as Intelligent Design advocates have done, that "since evolution by natural selection depends on reproduction," there cannot be a Darwinian solution to the problem of how the first living, reproducing thing came to exist. It was surely breathtakingly complicated, beautifully designed — must have been a miracle.

If we lapse into thinking of the pre-biotic, pre-reproductive world as a sort of featureless chaos of chemicals (like the scattered parts of the notorious jetliner assembled by a windstorm), the problem does look daunting and worse, but if we remind ourselves that the key process in evolution is cyclical repetition (of which genetic replication is just one highly refined and optimized instance), we can begin to see our way to turning the mystery into a puzzle: How did all those seasonal cycles, water cycles, geological cycles, and chemical cycles, spinning for millions of years, gradually accumulate the preconditions for giving birth to the biological cycles? Probably the first thousand "tries" were futile, near misses. But as Cole Porter says in his most sensual song, see what happens if you "do it again, and again, and again."

A good rule of thumb, then, when confronting the apparent magic of the world of life and mind is: look for the cycles that are doing all the hard work.

We can learn nearly as much from an experiment that does not work as from one that does. Failure is not something to be avoided but rather something to be cultivated. That's a lesson from science that benefits not only laboratory research, but design, sport, engineering, art, entrepreneurship, and even daily life itself. All creative avenues yield the maximum when failures are embraced. A great graphic designer will generate lots of ideas knowing that most will be aborted. A great dancer realizes most new moves will not succeed. Ditto for any architect, electrical engineer, sculptor, marathoner, startup maven, or microbiologist. What is science, after all, but a way to learn from things that don't work rather than just those that do? What this tool suggests is that you should aim for success while being prepared to learn from a series of failures. More so, you should carefully but deliberately press your successful investigations or accomplishments to the point that they break, flop, stall, crash, or fail.

Failure was not always so noble. In fact much of the world today failure is still not embraced as a virtue. It is a sign of weakness, and often a stigmata that prohibits second chances. Children in many parts of the world are taught that failure brings disgrace, and that one should do everything in one's power to succeed without failure. The rise of the West is in many respects due to the rise in tolerating failure. Indeed many immigrants trained in a failure-intolerant culture may blossom out of stagnancy once moved into a failure-tolerant culture. Failure liberates success.

The chief innovation that science brought to the state of defeat is a way to manage mishaps. Blunders are kept small, manageable, constant, and trackable. Flops are not quite deliberate, but they are channeled so that something is learned each time things fell. It becomes a matter of failing forward.

Science itself is learning how to better exploit negative results. Due to the problems of costly distribution most negative results have not been shared, thus limiting their potential to speed learning for others. But increasingly published negative results (which include experiments that succeed in showing no effects) are becoming another essential tool in the scientific method.

Wrapped up in the idea of embracing failure is the related notion of breaking things to make them better, particularly complex things. Often the only way to improve a complex system is to probe its limits by forcing it to fail in various ways. Software, among the most complex things we make, is usually tested for quality by employing engineers to systematically find ways to crash it. Similarly, one way to troubleshoot a complicated device that is broken is to deliberately force negative results (temporary breaks) in its multiple functions in order to locate the actual disfunction. Great engineers have a respect for breaking things that sometimes surprises non-engineers, just as scientists have a patience with failures that often perplexes outsiders. But the habit of embracing negative results is one of the most essential tricks to gaining success.

"I am large, I contain multitudes" wrote Walt Whitman. I have never met two people who were alike. I am an identical twin, and even we are not alike. Every individual has a distinct personality, a different cluster of thoughts and feelings that color all their actions. But there are patterns to personality: people express different styles of thinking and behaving — what psychologists call "temperament dimensions." I offer this concept of temperament dimensions as a useful new member of our cognitive tool kit.

Personality is composed of two fundamentally different types of traits: those of "character;" and those of "temperament." Your character traits stem from your experiences. Your childhood games; your family's interests and values; how people in your community express love and hate; what relatives and friends regard as courteous or perilous; how those around you worship; what they sing; when they laugh; how they make a living and relax: innumerable cultural forces build your unique set of character traits. The balance of your personality is your temperament, all the biologically based tendencies that contribute to your consistent patterns of feeling, thinking and behaving. As Spanish philosopher, Jose Ortega y Gasset, put it, "I am, plus my circumstances." Temperament is the "I am," the foundation of who you are.

Some 40% to 60% of the observed variance in personality is due to traits of temperament. They are heritable, relatively stable across the life course, and linked to specific gene pathways and/or hormone or neurotransmitter systems. Moreover,   our temperament traits congregate in constellations, each aggregation associated with one of four broad, interrelated yet distinct brain systems: those associated with dopamine, serotonin, testosterone and estrogen/oxytocin. Each constellation of temperament traits constitutes a distinct temperament dimension.

For example, specific alleles in the dopamine system have been linked with exploratory behavior, thrill, experience and adventure seeking, susceptibility to boredom and lack of inhibition. Enthusiasm has been coupled with variations in the dopamine system, as have lack of introspection, increased energy and motivation, physical and intellectual exploration, cognitive flexibility, curiosity, idea generation and verbal and non-linguistic creativity.  

The suite of traits associated with the serotonin system includes sociability, lower levels of anxiety, higher scores on scales of extroversion, and lower scores on a scale of "No Close Friends," as well as positive mood, religiosity, conformity, orderliness, conscientiousness, concrete thinking, self-control, sustained attention, low novelty seeking, and figural and numeric creativity.  

Heightened attention to detail, intensified focus, and narrow interests are some of the traits linked with prenatal testosterone expression. But testosterone activity is also associated with emotional containment, emotional flooding (particularly rage), social dominance and aggressiveness, less social sensitivity, and heightened spatial and mathematical acuity.  

Last, the constellation of traits associated with the estrogen and related oxytocin system include verbal fluency and other language skills, empathy, nurturing, the drive to make social attachments and other prosocial aptitudes, contextual thinking, imagination, and mental flexibility.  

We are each a different mix of these four broad temperament dimensions. But we do have distinct personalities  People are malleable, of course; but we are not blank slates upon which the environment inscribes personality. A curious child tends to remain curious, although what he or she is curious about changes with maturity. Stubborn people remain obstinate; orderly people remain punctilious; and agreeable men and women tend to remain amenable. 

We are capable of acting "out of character," but doing so is tiring. People are biologically inclined to think and act in specific patterns — temperament dimensions. But why would this concept of temperament dimensions be useful in our human cognitive tool kit? Because we are social creatures, and a deeper understanding of who we (and others) are can provide a valuable tool for understanding, pleasing, cajoling, reprimanding, rewarding and loving others — from friends and relatives to world leaders. It's also practical. 

Take hiring. Those expressive of the novelty-seeking temperament dimension are unlikely to do their best in a job requiring rigid routines and schedules. Biologically cautious individuals are not likely to be comfortable in high-risk posts. Decisive, tough minded high testosterone types are not well suited to work with those who can't get to the point and decide quickly. And those predominantly of the compassionate, nurturing high estrogen temperament dimension are not likely to excel at occupations that require them to be ruthless. 

Managers might form corporate boards containing all four broad types. Colleges might place freshman with roommates of a similar temperament, rather than similarity of background. Perhaps business teams, sports teams, political teams and teacher-student teams would operate more effectively if they were either more "like-minded" or more varied in their cognitive skills. And certainly we could communicate with our children, lovers, colleagues and friends more effectively. We are not puppets on a string of DNA. Those biologically susceptible to alcoholism, for example, often give up drinking. The more we come to understand our biology, the more we will appreciate how culture molds our biology.

The most important scientific concept is that an assertion is often an empirical question, settled by collecting evidence. The plural of anecdote is not data, and the plural of opinion is not facts. Quality, peer-reviewed scientific evidence accumulates into knowledge. People's stories are stories, and fiction keeps us going. But science should settle policy.

Nothing is more wonderful about human beings than their ability to abstract, infer, calculate, and produce rules, algorithms, and tables that enable them to work marvels. We are the only species that could even imagine taking on mother nature in a fight for control of the world. We may well lose that fight, but it's an amazing spectacle nonetheless.

But nothing is less wonderful about human beings than their ability to refuse to learn from their own discoveries. The edge to the Edge question this year is the implication that we are brilliant and stupid at the same time, capable of inventing wonders and still capable of forgetting what we've done and blundering stupidly on. Our poor cognitive toolkits are always missing a screwdriver when we need it and we're always trying to get a bolt off that wheel with our teeth when a perfectly serviceable wrench is in the kit over there unused.

So as classicist, I'll make my pitch for what is arguably the oldest of our "SHA" concepts, the one that goes back to the senior pre-Socratic philosopher, Heraclitus."You can't step in the same river twice," he said; putting it another way his mantra was "Everything flows." Remembering that everything is in motion — feverish, ceaseless, unbelievably rapid motion — is always hard for us. Vast galaxies dash apart at speeds that seem faster than is physically possible, while the subatomic particles of which we are composed beggar our ability to comprehend large numbers when we try to understand their motion — and at the same time, I lie here, sluglike, inert, trying to muster the energy to change channels, convinced that one day is just like another, reflecting on the deep truth that my idiot cousin will never change, and wondering why my favorite cupcake store has lost its magic touch.

Because we think and move at human scale in time and space, we can deceive ourselves. Pre-Copernican astronomies depended on the self-evident fact that the "fixed stars" orbited around the other in a slow annual dance; and it was an advance in science to declare that "atoms" (in Greek, literally "indivisibles") were the changeless building blocks of matter — until we split them. Edward Gibbon could be puzzled by the fall of the Roman Empire without realizing that its most amazing feature was that it lasted so long. Scientists discover magic disease-fighting compounds only to find that the disease changes faster than they can keep up.

Take it from Heraclitus and put it in your toolkit: change is the law, stability and consistency are illusions, temporary in any case, a heroic achievement of human will and persistence at best. When we want things to stay the same, we'll always wind up playing catch-up. Better to go with the flow.

It is the stuff of children's games. In the game of "telephone," a secret message is whispered from child to child until it is announced out loud by the final recipient. To the delight of all, the message is typically transformed into something new and bizarre, no matter the sincerity and care given to each retelling.

Humor seems to be the brain's way of motivating itself — through pleasure — to notice disparities and cleavages in its sense of the world. In the telephone game we find glee in the violation of expectation; what we think should be fixed turns out to be fluid.

When brains get something wrong commonly enough that noticing the failure becomes the fulcrum of a simple child's game, then you know there's a hitch in human cognition worth worrying about. Somehow, we expect information to be Platonic and faithful to its origin, no matter what history might have corrupted it.

The illusion of Platonic information is confounding because it can easily defeat our natural skeptical impulses. If a child in the sequence sniffs that the message seems too weird to be authentic, she can compare notes most easily with the children closest to her, who received the message just before she did. She might discover some small variation, but mostly the information will appear to be confirmed, and she will find an apparent verification of a falsity.

Another delightful pastime is over-transforming an information artifact through digital algorithms that are useful if used sparingly, until it turns into something quite strange. For instance, you can use one of the online machine translation services to translate a phrase through a ring of languages back to the original and see what you get.

The phrase, "The edge of knowledge motivates intriguing online discussions" transforms into "Online discussions in order to stimulate an attractive national knowledge" in four steps on Google's current translator. (English->German->Hebrew->Simplified Chinese->English)

We find this sort of thing funny, just like children playing "telephone," as well we should, because it sparks our recollection that our brains have unrealistic expectations of information transformation.

While information technology can reveal truths, it can also create stronger illusions than we are used to. For instance, sensors all over the world, connected through cloud computing, can reveal urgent patterns of change in climate data. But endless chains of online retelling also create an illusion for masses of people that the original data is a hoax.

The illusion of Platonic information plagues finance. Financial instruments are becoming multilevel derivatives of the real actions on the ground that finance is ultimately supposed to motivate and optimize. The reason to finance the purchasing of a house ought to be at least in part to get the house purchased. But an empire of specialists and giant growths of cloud computers showed, in the run up to the Great Recession, that it is possible for sufficiently complex financial instruments to become completely disconnected from their ultimate purpose.

In the case of complex financial instruments, the role of each child in the telephone game does not correspond to a horizontal series of stations that relay a message, but a vertical series of transformations that are no more reliable. Transactions are stacked on top of each other. Each transaction is based on a formula that transforms the data of the transactions beneath it on the stack. A transaction might be based on the possibility that a prediction of a prediction will have been improperly predicted.

The illusion of Platonic information reappears as a belief that a higher-level representation must always be better. Each time a transaction is gauged to an assessment of the risk of another transaction, however, even if it is in a vertical structure, at least a little bit of error and artifact is injected. By the time a few layers have been compounded, the information becomes bizarre.

Unfortunately, the feedback loop that determines whether a transaction is a success or not is based only on its interactions with its immediate neighbors in the phantasmagorical abstract playground of finance. So a transaction can make money based on how it interacted with the other transactions it referenced directly, while having no relationship to the real events on the ground that all the transactions are ultimately rooted in. This is just like the child trying to figure out if a message has been corrupted only by talking to her neighbors.

In principle, the Internet can make it possible to connect people directly to information sources, to avoid the illusions of the game of telephone. Indeed this happens. Millions of people had a remarkable direct experience of the Mars rovers.

The economy of the Internet as it has evolved incentivizes aggregators, however. Thus we all take seats in a new game of telephone, in which you tell the blogger who tells the aggregator of blogs, who tells the social network, who tells the advertiser, who tells the political action committee, and so on. Each station along the way finds that it is making sense, because it has the limited scope of the skeptical girl in the circle, and yet the whole systems becomes infused with a degree of nonsense.

A joke isn't funny anymore if it's repeated too much. It is urgent for the cognitive fallacy of Platonic information to be universally acknowledged, and for information systems to be designed to reduce cumulative error.

Some people like to build sand castles, and some like to tear them apart. There can be much joy in the latter. But it is the former that interests me. You can take a bunch of minute silica crystals, pounded for thousands of years by the waves, use your hands, and make an ornate tower. Tiny physical forces govern how each particle interacts with its neighbors, keeping the castle together, at least until the force majeur of a foot appears.

But, having built the castle, this is the part that I like the most: you step back and look at it. Across the expanse of beach, here is something new, something not present before among the endless sand grains, something risen from the ground, something that reflects the scientific principle of holism.

Holism is colloquially summarized as "the whole is greater than the sum of its parts." What is interesting to me, however, are not the artificial instantiations of this principle — when we deliberately form sand into ornate castles or metal into airborne planes or ourselves into corporations — but rather the natural instantiations. The examples are widespread and stunning. Perhaps the most impressive one is that carbon, hydrogen, oxygen, nitrogen, sulfur, phosphorus, iron, and a few other elements, when mixed in just the right way, yield life. And life has emergent properties not present in — nor predictable from — these constituent parts. There is a kind of awesome synergy between the parts.

Hence, I think that the scientific concept that would improve everyone's cognitive toolkit is holism: the abiding recognition that wholes have properties not present in the parts and not reducible to the study of the parts.

For example, carbon atoms have particular, knowable physical and chemical properties. But the atoms can be combined in different ways to make, say, graphite or diamond. The properties of those substances — properties such as darkness and softness and clearness and hardness — are not properties of the carbon atoms, but rather properties of the collection of carbon atoms. Moreover, which particular properties the collection of atoms has depends entirely on how they are assembled — into sheets or pyramids. The properties arise because of the connections between the parts. I think grasping this insight is crucial for a proper scientific perspective on the world. You could know everything about isolated neurons and not be able to say how memory works, or where desire originates.

It is also the case that the whole has a complexity that rises faster than the number of its parts. Consider social networks as a simple illustration. If we have ten people in a group, there are a maximum of 10x9/2=45 possible connections between them. If we increase the number of people to 1,000, the number of possible ties increases to 1,000x999/2=499,500. So, while the number of people has increased by 100-fold (from 10 to 1,000), the number of possible ties (and hence, this one measure of the complexity of the system), has increased by over 10,000-fold.

Holism does not come naturally. It is an appreciation not of the simple, but of the complex, or at least of the simplicity and coherence in complex things. Moreover, unlike curiosity or empiricism, say, holism takes a while to acquire and to appreciate. It is a very grown-up disposition. Indeed, for the last few centuries, the Cartesian project in science has been to break matter down into ever smaller bits, in the pursuit of understanding. And this works, to some extent. We can understand matter by breaking it down to atoms, then protons and electrons and neutrons, then quarks, then gluons, and so on. We can understand organisms by breaking them down into organs, then tissues, then cells, then organelles, then proteins, then DNA, and so on.

But putting things back together in order to understand them is harder, and typically comes later in the development of a scientist or in the development of science. Think of the difficulties in understanding how all the cells in our bodies work together, as compared with the study of the cells themselves. Whole new fields of neuroscience and systems biology and network science are arising to accomplish just this. And these fields are arising just now, after centuries of stomping on castles in order to figure them out.

The second law of thermodynamics, the so-called "arrow of time", popularly associated with entropy (and by association death), is the most widely misunderstood shorthand abstraction in human society today. We need to fix this.

The second law states that over time, closed systems will become more similar, eventually reaching systemic equilibrium. It is not a question of if a system will reach equilibrium; it is only a question of whena system will reach equilibrium.

Living on a single planet, we are all participants in a single physical system which has only one direction — towards systemic equilibrium. The logical consequences are obvious; our environmental, industrial and political systems (even our intellectual and theological systems) will become more homogenous over time. It's already started. The physical resources available to every person on earth, including air, food and water, have already been significantly degraded by the high burn rate of industrialization, just as the intellectual resources available to every person on earth have already been significantly increased by the high distribution rate of globalization.

Human societies are already far more similar than ever before (does anyone really miss dynastic worship?) and it would be very tempting to imagine that a modern democracy based on equal rights and opportunities is the system in equilibrium. That seems unlikely, given our current energy footprint. More likely, if the total system energy is depleted too fast, is that modern democracies will be compromised if the system crashes to its lowest equilibrium too quickly for socially equitable evolution.

Our one real opportunity is to use the certain knowledge of ever increasing systemic equilibrium to build a model for an equitable and sustainable future. The mass distribution of knowledge and access to information through the world wide web is our civilization's signal achievement. Societies that adopt innovative, predictive and adaptive models designed around a significant, on-going redistribution of global resources will be most likely to survive in the future.

But since we are biologically and socially programmed to avoid discussing entropy (death), we reflexively avoid the subject of systemic changes to our way of life, both as a society and individuals. We think it's a bummer. Instead of examining the real problems, we consume apocalyptic fantasies as "entertainment" and deride our leaders for their impotence. We really need to fix this.

Unfortunately, even facing this basic concept faces an uphill battle today. In earlier, expansionist phases of society, various metaphorical engines such as "progress" and "destiny" allowed the metaphorical "arrow" to supplant the previously (admittedly spirit-crushing) "wheel" of time. Intellectual positions that supported scientific experimentation and causality were tolerated, even endorsed, as long as they contributed to the arrow's cultural momentum. But in a more crowded and contested world, the limits of projected national power and consumption control have become more obvious. Resurgent strands of populism, radicalism and magical thinking have found mass appeal in their rejection of many rational concepts. But perhaps most significant is the rejection of undisputed physical laws.

The practical effect of this denial on the relationship between the global economy and the climate change debate (for example) is obvious. Advocates propose continuous "good" (green) growth, while denialists propose continuous "bad" (brown) growth. Both sides are more interested in backing winners and losers in a future economic environment predicated on the continuation of today's systems, than accepting the physical inevitability of increasing systemic equilibrium in any scenario.

Of course, any system can temporarily cheat entropy. Hotter particles (or societies) can "steal" the stored energy of colder (or weaker) ones, for a while. But in the end, the rate at which the total energy is burned and redistributed will still determine the speed at which the planetary system will reach its true systemic equilibrium. Whether we extend the lifetime of our local "heat" through war, or improved window insulation, is the stuff of politics. But even if in reality we can't beat the house, its worth a try, isn't it?

French for handyman or do-it-yourselfer, this word has migrated into art and philosophy recently and savants would do well tossing it into their cognitive toolbox. A Bricoleur is a talented tinkerer, the sort who can build anything out of anything: whack off a left-over drain pipe, fasten a loop of tin roofing, dab some paint, and presto a mailbox. If one peers closely all the parts are still there, still a piece of roofing, a piece of pipe, but now the assembly exceeds the sum of the parts and is useful in a different way. In letters a Bricoleur is viewed as an intellectual MacGyver tacking bits of his heritage to sub-cultures about him for a new meaning-producing pastiche.

A Bricoleur is not a new thing, but it has become a new way of understanding old things: Epistemology, the Counter-Enlightenment, and the endless parade of "isms" of the 19th and 20th Centuries: Marxism, Modernism, Socialism, Surrealism, Abstract Expressionism, Minimalism — the list is endless, and often exclusive, each insisting that the other cannot be. The exegesis of these grand theories by deconstruction — substituting trace for presence — and similar activities during the past century shows these worldviews not as discoveries, instead but assemblies, by creative Bricoleurs who had been working in the background, stapling together meaning producing scenarios from textual bric-a-brac lying about.

Presently, encompassing worldviews in philosophy have been shelved, and master art movements of style and conclusion folded along side it, no more "isms" are being run up the flagpole, because no one is saluting. Pluralism and modest descriptions of the world have become the activity of fine arts and letters, personalization and private worlds the Zeitgeist. The common prediction was that the loss of grand narrative would result in a descent into end-of-history purposelessness, instead everywhere the Bricoleurs are busy manufacturing meaning-eliciting metaphor.

Motion Graphics, Bio-art, Information Art, Net Art, Systems Art, Glitch Art, Hacktivism, Robotic Art, Relational Esthetics and others, all current art movements tossed up by contemporary Bricoleurs in an endless salad. Revisit 19th Century Hudson River landscape painting? Why not. Neo-Rodin, Post-New Media? A Mormon dabbling with the Frankfurt School. Next month. With the quest for universal validity suspended there is a pronounced freedom to assemble lives filled with meaning from the nearby and at-hand, one just needs a Bricoleur.

The scientist Nicolaus Copernicus recognized that Earth is not in any particularly privileged position in the solar system. This elegant fact can be extended to encompass a powerful idea, known as the Copernican Principle, that we are not in a special or favorable place of any sort. By looking at the world through the eyes of this principle, we can remove certain blinders and preconceptions about ourselves and re-examine our relationship with the universe.

The Copernican Principle can be used in the traditional spatial sense, providing awareness of our mediocre place in the galaxy, and our galaxy's place in the universe. We now recognize that our solar system, once thought to be the center of the galaxy, is actually in the suburban portions of the Milky Way. And the Copernican Principle helps guide our understanding of the expanding universe, allowing us to see that anywhere in the cosmos one would also view other galaxies moving away at rapid speeds, just as we see here on Earth. We are not anywhere special.

The Copernican Principle has also been extended to our temporal position by astrophysicist J. Richard Gott to help provide estimates for lifetimes of events, independent of additional information. As Gott elaborated, other than the fact that we are intelligent observers, there is no reason to believe we are in any way specially located in time. It allows us to quantify our uncertainty and recognize that we are often neither at the beginning of things, nor at the end. This allowed Gott to estimate correctly when the Berlin Wall would fall, and has even provided meaningful numbers on the lifetime of humanity.

This principle can even anchor our location within the many orders of magnitude of our world: we are far smaller than most of the cosmos, far larger than most chemistry, far slower than much that occurs at subatomic scales, and far faster than geological and evolutionary processes. Through this principle, we are compelled to study successively larger and smaller orders of magnitude of our world, because we need not assume that everything interesting is at the same scale as ourselves.

And yet, despite this regimented approach to our mediocrity, we need not have cause for despair: as far as we know, we're the only species that can actually recognize its place in the universe. The paradox of the Copernican Principle is that, by properly understanding our place, even if it be rather humbling, we can only then truly understand our surroundings. And by being able to do that, we don't seem so small or insignificant after all.

The word "science" itself might be the best answer to this year's Edge question. The idea that we can systematically understand certain aspects of the world and make predictions based on what we've learned — while appreciating and categorizing the extent and limitations of what we know — plays a big role in how we think. Many words that summarize the nature of science such as "cause and effect," "predictions," and " experiments," as well as words that describe probabilistic results such as "mean," "median," "standard deviation," and the notion of "probability" itself help us understand more specifically what this means and how to interpret the world and behavior within it.

"Effective theory" is one of the more important notions within and outside of science. The idea is to determine what you can actually measure and decide — given the precision and accuracy of your measuring tools — and to find a theory appropriate to those measurable quantities. The theory that works might not be the ultimate truth—but it's as close an approximation to the truth as you need and is also the limit to what you can test at any given time. People can reasonably disagree on what lies beyond the effective theory, but in a domain where we have tested and confirmed it, we understand the theory to the degree that it's been tested.

An example is Newton's Laws, which work as well as we will ever need when they describe what happens to a ball when we throw it. Even though we now know quantum mechanics is ultimately at play, it has no visible consequences on the trajectory of the ball. Newton's Laws are part of an effective theory that is ultimately subsumed into quantum mechanics. Yet Newton's Laws remain practical and true in their domain of validity. It's similar to the logic you apply when you look at a map. You decide the scale appropriate to your journey — are you traveling across the country, going upstate, or looking for the nearest grocery store — and use the map scale appropriate to your question.

Terms that refer to specific scientific results can be efficient at times but they can also be misleading when taken out of context and not supported by true scientific investigation. But the scientific methods for seeking, testing, and identifying answers and understanding the limitations of what we have investigated will always be reliable ways of acquiring knowledge. A better understanding of the robustness and limitations of what science establishes, as well as probabilistic results and predictions, could make the world a better place.

The notion of uncertainty is perhaps the least well understood concept in science. In the public parlance, uncertainty is a bad thing, implying a lack of rigor and predictability. The fact that global warming estimates are uncertain, for example, has been used by many to argue against any action at the present time.

In fact, however, uncertainty is a central component of what makes science successful. Being able to quantify uncertainty, and incorporate it into models, is what makes science quantitative, rather than qualitative. Indeed, no number, to measurement, no observable in science is exact. Quoting numbers without attaching an uncertainty to them implies they have, in essence, no meaning.

One of the things that makes uncertainty difficult for members of the public to appreciate is that the significance of uncertainty is relative. Take, for example, the distance between the earth and sun, 1.49597 x 108 km. This seems relatively precise, after all using six significant digits means I know the distance to an accuracy of one part in a million or so. However, if the next digit is uncertain, that means the uncertainty in knowing the precise earth-sun distance is larger than the distance between New York and Chicago!

Whether or not the quoted number is 'precise' therefore depends upon what I am intending to do with it. If I only care about what minute the Sun will rise tomorrow then the number quoted above is fine. If I want to send a satellite to orbit just above the Sun, however, then I would need to know distances more accurately.

This is why uncertainty is so important. Until we can quantify the uncertainty in our statements and our predictions, we have little idea of their power or significance. So too in the public sphere. Public policy performed in the absence of understanding quantitative uncertainties, or even understanding the difficulty of obtaining reliable estimates of uncertainties usually means bad public policy.

Recursive structure is a simple idea (or shorthand abstraction) with surprising applications beyond science.

A structure is recursive if the shape of the whole recurs in the shape of the parts: for example, a circle formed of welded links that are circles themselves. Each circular link might itself be made of smaller circles, and in principle you could have an unbounded nest of circles made of circles made of circles.

The idea of recursive structure came into its own with the advent of computer science (that is, software science) in the 1950s. The hardest problem in software is controlling the tendency of software systems to grow incomprehensibly complex. Recursive structure helps convert impenetrable software rainforests into French gardens — still (potentially) vast and complicated, but much easier to traverse and understand than a jungle.

Benoit Mandelbrot famously recognized that some parts of nature show recursive structure of a sort: a typical coastline shows the same shape or pattern whether you look from six inches or sixty feet or six miles away.

But it also happens that recursive structure is fundamental to the history of architecture, especially to the gothic, renaissance and baroque architecture of Europe — covering roughly the 500 years between the 13th and 18th centuries. The strange case of "recursive architecture" shows us the damage one missing idea can create. It suggests also how hard it is to talk across the cultural Berlin Wall that separates science and art. And the recurrence of this phenomenon in art and nature underlines an important aspect of the human sense of beauty.

The re-use of one basic shape on several scales is fundamental to medieval architecture. But, lacking the idea (and the term) "recursive structure," art historians are forced to improvise ad hoc descriptions each time they need one. This hodgepodge of improvised descriptions makes it hard, in turn, to grasp how widespread recursive structure really is. And naturally, historians of post-medieval art invent their own descriptions—thus obfuscating a fascinating connection between two mutually alien aesthetic worlds.

For example: One of the most important aspects of mature gothic design is tracery — the thin, curvy, carved stone partitions that divide one window into many smaller panes. Recursion is basic to the art of tracery.

Tracery was invented at the cathedral of Reims circa 1220, and used soon after at the cathedral of Amiens. (Along with Chartres, these two spectacular and profound buildings define the High Gothic style.) To move from the characteristic tracery design of Reims to that of Amiens, just add recursion. At Reims, the basic design is a pointed arch with a circle inside; the circle is supported on two smaller arches. At Amiens, the basic design is the same — except that now, the window recurs in miniature inside each smaller arch. (Inside each smaller arch is a still-smaller circle supported on still-smaller arches.)

In the great east window at Lincoln Cathedral, the recursive nest goes one step deeper. This window is a pointed arch with a circle inside; the circle is supported on two smaller arches — much like Amiens. Within each smaller arch is a circle supported on two still-smaller arches. Within each still-smaller arch, a circle is supported on even-smaller arches.

There are other recursive structures throughout medieval art.

Jean Bony and Erwin Panofsky were two eminent 20th century art historians. Naturally they both noticed recursive structure. But neither man understood the idea in itself. And so, instead of writing that the windows of Saint-Denis show recursive structure, Bony said that they are "composed of a series of similar forms progressively subdivided in increasing numbers and decreasing sizes." Describing the same phenomenon in a different building, Panofsky writes of the "principle of progressive divisibility (or, to look at it the other way, multiplicability)." Panofsky's "principle of progressive divisiblity" is a fuzzy, roundabout way of saying "recursive structure."

Louis Grodecki noticed the same phenomenon—a chapel containing a display-platform shaped like the chapel in miniature, holding a shrine shaped like the chapel in extra-miniature. And he wrote that "This is a common principle of Gothic art." But he doesn't say what the principle is; he doesn't describe it in general or give it a name. William Worringer, too, had noticed recursive structure. He described gothic design as "a world which repeats in miniature, but with the same means, the expression of the whole."

So each historian makes up his own name and description for the same basic idea—which makes it hard to notice that all four descriptions actually describe the same thing. Recursive structure is a basic principle of medieval design; but this simple statement is hard to say or even think if we don't know what "recursive structure" is.

If the literature makes it hard to grasp the importance of recursive structure in medieval art, it's even harder to notice that exactly the same principle recurs in the radically different world of Italian Renaissance design.

George Hersey wrote astutely of Bramante's design (ca 1500) for St Peter's in the Vatican that it consists of "a single macrochapel…, four sets of what I will call maxichapels, sixteen minichapels, and thirty-two microchapels." "The principle [he explains] is that of Chinese boxes — or, for that matter, fractals."

If he had only been able to say that "recursive structure is fundamental to Bramante's thought," the whole discussion would have been simpler and clearer — and an intriguing connection between medieval and renaissance design would have been obvious.

Using instead of ignoring the idea of recursive structure would have had other advantages too.

It helps us understand the connections between art and technology; helps us see the aesthetic principles that guide the best engineers and technologists, and the ideas of clarity and elegance that underlie every kind of successful design. These ideas have practical implications. For one, technologists must study and understand elegance and beauty as design goals; any serious technology education must include art history. And we reflect, also, on the connection between great art and great technology on the one hand and natural science on the other.

But without the right intellectual tool for the job, new instances of recursive structure make the world more complicated instead of simpler and more beautiful.

This year, Edge is asking us to identify a scientific concept that "would improve everybody's cognitive toolkit." Not clever enough to invent a concept of my own, I am voting for a winning candidate. It might be called the Swiss Army knife of scientific concepts, a term containing a remarkable number of useful tools for exploring cognitive conundrums. I am thinking of open systems, an idea that passes through thermodynamics and physics, before heading into anthropology, linguistics, history, philosophy, and sociology, until arriving, finally, into the world of computers, where it branches into other ideas such as open source and open standards.

Open standards allow knowledgeable outsiders access to the design of computer systems, to improve, interact with, or otherwise extend them. These standards are public, transparent, widely accessible, and royalty-free for developers and users. Open standards have driven innovation on the Web and allowed it to flourish as both a creative and commercial space.

Unfortunately, the ideal of an open web is not embraced by companies that prefer walled gardens, silos, proprietary systems, aps, tiered levels of access, and other metered methods for turning citizens into consumers. Their happy-face web contains tracking systems useful for making money, but these systems are also appreciated by the police states of the world, for they, too, have a vested interest in surveillance and closed systems.

Now that the Web has frothed through twenty years of chaotic inventiveness, we have to push back against the forces that would close it down. A similar push should be applied to other systems veering toward closure. "Citoyens, citoyennes, arm yourselves with the concept of openness."

The concept comes from early ethologists, scientists such as Oskar Heinroth and Konrad Lorenz, who defined it as an instinctive response — usually a series of predictable behavior patterns — that would occur reliably in the presence of a specific bit of input, often called a "releaser". FAPs, as they were known, were thought to be devoid of cognitive processing. As it turned out, FAPs were not nearly as fixed as the ethologists believed, but the concept has remained as part of the historical literature, as a way of scientifically describing what in the vernacular might be called "knee-jerk responses". The concept of a FAP, despite its simplicity, might prove quite valuable as a metaphorical means to study and change human behavior.

If we look into the literature on FAPs, we see that many such instinctive responses were actually learned, based on the most elementary of signals. For example, the newly-hatched herring gull chicks' supposed FAP of hitting the red spot on its parents' beak for food was far more complex: Hailman demonstrated that what was innate was only a tendency to peck at an oscillating object in the field of view. The ability to target the beak, and the red spot on the beak, though a pattern that developed steadily and fairly quickly, was acquired experientially. Clearly, certain sensitivities must be innate, but the specifics of their development into various behavioral acts likely depend on how the organism interacts with its surroundings and what feedback it receives. The system need not, especially for humans, be simply a matter of conditioning Response R to Stimulus S, but rather of evaluating as much input as possible.

The relevance is that, if we wish to understand why as humans we often act in certain predictable ways, and particularly if there is a desire or need to change these behavioral responses, we can remember our animal heritage and look for the possible releasers that appear to stimulate our FAPs. Might the FAP actually be a response learned over time, initially with respect to something even more basic than we expect? The consequences could affect aspects of our lives from our social interactions to what we see as our quick decision-making processes in our professional lives. Given an understanding of our FAPs, and those of the other individuals with whom we interact, we — as humans with cognitive processing powers — could begin to re-think our behavior patterns.

Most people tend to think of science in one of two ways. It is a body of knowledge and understanding about the world: gravity, photosynthesis and evolution. Or it is the technology that has emerged from the fruits of that knowledge: vaccines, computers and cars. Science is both of these things, yet as Carl Sagan so memorably explained in The Demon-Haunted World, it is something else besides. It is a way of thinking, the best approach yet devised (if still an imperfect one) to discovering progressively better approximations of how things really are.

Science is provisional, always open to revision in light of new evidence. It is anti-authoritarian: anybody can contribute, and anybody can be wrong. It seeks actively to test its propositions. And it is comfortable with uncertainty. These qualities give the scientific method unparalleled strength as a way of finding things out. Its power, however, is too often confined to an intellectual ghetto: those disciplines that have historically been considered "scientific".

Science as a method has great things to contribute to all sorts of pursuits beyond the laboratory. Yet it remains missing in action from far too much of public life. Politicians and civil servants too seldom appreciate how tools drawn from both the natural and social sciences can be used to design more effective policies, and even to win votes.
In education and criminal justice, for example, interventions are regularly undertaken without being subjected to proper evaluation. Both fields can be perfectly amenable to one of science's most potent techniques — the randomised controlled trial — yet these are seldom required before new initiatives are put into place. Pilots are often derisory in nature, failing even to collect useful evidence that could be used to evaluate a policy's success.

Sheila Bird of the Medical Research Council, for instance, has criticised the UK's introduction of a new community sentence called the Drug Treatment and Testing Order, following pilots designed so poorly as to be worthless. They included too few subjects; they were not randomised; they did not properly compare the orders with alternatives; and judges were not even asked to record how they would otherwise have sentenced offenders.

The culture of public service could also learn from the self-critical culture of science. As Jonathan Shepherd, of the University of Cardiff, has pointed out, policing, social care and education lack the cadre of practitioner-academics that has served medicine so well. There are those who do, and there are those who research: too rarely are they the same people. Police officers, teachers and social workers are simply not encouraged to examine their own methods in the same way as doctors, engineers and bench scientists. How many police stations run the equivalent of a journal club?

The scientific method and the approach to critical thinking it promotes are too useful to be kept back for "science" alone. If it can help us to understand the first microseconds of creation and the structure of the ribosome, it can surely improve understanding of how best to tackle the pressing social questions of our time.

You're sprawled on the couch in your living room, watching a new episode of Justified on the tube, when you think of something you need to do in the kitchen. You get up, take ten quick steps across the carpet, and then, just as you reach the kitchen door — poof! — you realize you've already forgotten what it was you got up to do. You stand befuddled for a moment, then shrug your shoulders and head back to the couch.

Such memory lapses happen so often that we don't pay them much heed. We write them off as "absentmindedness" or, if we're getting older, "senior moments." But the incidents reveal a fundamental limitation of our minds: the tiny capacity of our working memory. Working memory is what brain scientists call the short-term store of information where we hold the contents of our consciousness at any given moment — all the impressions and thoughts that flow into our mind as we go through a day. In the 1950s, Princeton psychologist George Miller famously argued that our brains can hold only about seven pieces of information simultaneously. Even that figure may be too high. Some brain researchers now believe that working memory has a maximum capacity of just three or four elements.

The amount of information entering our consciousness at any instant is referred to as our cognitive load. When our cognitive load exceeds the capacity of our working memory, our intellectual abilities take a hit. Information zips into and out of our mind so quickly that we never gain a good mental grip on it. (Which is why you can't remember what you went to the kitchen to do.) The information vanishes before we've had an opportunity to transfer it into our long-term memory and weave it into knowledge. We remember less, and our ability to think critically and conceptually weakens. An overloaded working memory also tends to increase our distractedness. After all, as the neuroscientist Torkel Klingberg has pointed out, "we have to remember what it is we are to concentrate on." Lose your hold on that, and you'll find "distractions more distracting."

Developmental psychologists and educational researchers have long used the concept of cognitive load in designing and evaluating pedagogical techniques. When you give a student too much information too quickly, they know, comprehension degrades and learning suffers. But now that all of us — thanks to the incredible speed and volume of modern digital communication networks and gadgets — are inundated with more bits and pieces of information than ever before, everyone would benefit from having an understanding of cognitive load and how it influences memory and thinking. The more aware we are of how small and fragile our working memory is, the more we'll be able to monitor and manage our cognitive load. We'll become more adept at controlling the flow of the information coming at us.

There are times when you want to be awash in messages and other info-bits. The resulting sense of connectedness and stimulation can be exciting and pleasurable. But it's important to remember that, when it comes to the way your brain works, information overload is not just a metaphor; it's a physical state. When you're engaged in a particularly important or complicated intellectual task, or when you simply want to savor an experience or a conversation, it's best to turn the information faucet down to a trickle.

Creativity is a fragile flower, but perhaps it can be fertilized with systematic doses of serendipity. Sarnoff Mednick showed decades ago that some people are better than others at detecting the associations that connect seemingly random concepts: Asked to name a fourth idea that links "wheel," "electric," and "high," people who score high on other measures of creativity will promptly answer "chair."

More recently, research in Mark Jung-Beeman's lab at Northwestern has found that sudden bursts of insight — the Aha! or Eureka! moment — comes when brain activity abruptly shifts its focus. The almost ecstatic sense that makes us cry "I see!" appears to come when the brain is able to shunt aside immediate or familiar visual inputs.

That may explain why so many of us close our eyes (often unwittingly) just before we exclaim "I see!" It also suggests, at least to me, that creativity can be enhanced deliberately through environmental variation. Two techniques seem promising: varying what you learn and varying where you learn it. I try each week to read a scientific paper in a field that is new to me — and to read it in a different place.

New associations often leap out of the air at me this way; more intriguingly, others seem to form covertly and then to lie in wait for the opportune moment when they can click into place. I do not try to force these associations out into the open; they are like shrinking mimosa plants that crumple if you touch them but bloom if you leave them alone.

Robert Merton argued that many of the greatest discoveries of science have sprung from serendipity. As a layman and an amateur, all I hope to accomplish by throwing myself in serendipity's path is to pick up new ideas, and combine old ones, in ways that haven't quite occurred to other people yet. So I let my curiosity lead me wherever it seems to want to go, like that heart-shaped piece of wood that floats across a Ouija board.

I do this remote-reading exercise on my own time, since it would be hard to justify to newspaper editors during the work day. But my happiest moments this autumn came as I reported an investigative article on how elderly investors are increasingly being scammed by elderly con artists. I later realized, to my secret delight, that the article had been enriched by a series of papers I had been reading on altruistic behavior among fish (Lambroides dimidiatus).

If I do my job right, my regular readers will never realize that I spend a fair amount of my leisure time reading Current Biology, the Journal of Neuroscience, and Organizational Behavior and Human Decision Processes. If that reading helps me find new ways to understand the financial world, as I suspect it does, my readers will indirectly be smarter for it. If not, the only harm done is my own spare time wasted.

In my view, we should each invest a few hours a week in reading research that ostensibly has nothing to do with our day jobs, in a setting that has nothing in common with our regular workspaces. This kind of structured serendipity just might help us become more creative, and I doubt that it can hurt.

The scientific concept of the "multiverse" has already entered popular imagination. But the full implications of the idea that every possible universe has been and will be actualised have yet to sink in. One of these, which could do more to change our view of things than anything is that we are all destined to be immortal.

This welcome news (if indeed it is welcome) follows on two quite different grounds. First, death normally occurs to human bodies in due time either as the result of some kind of macro-accident — for example a car crash, or a homicide; or a micro-one — a heart attack, a stroke; or, if those don't get us, a nano-one — accidental errors in cell division, cancer, old age. Yet, in the multiverse, where every alternative is realised, the wonderful truth is that there has to be at least one particular universe in which by sheer luck each of us as individuals have escaped any and all of these blows.

Second, we live in a world where scientists are, in any case, actively searching for ways of combatting all such accidents: seat belts to protect us in the crash, aspirin to prevent stroke, red wine oxidants to counter heart attacks, antibiotics against disease. And in one or more of the possible universes to come these measures will surely have succeeded in making continuing life rather than death the natural thing.

Taking these possibilities — nay certainties — together, we can reasonably conclude that there will surely be at least one universe in which I — and you — will still find ourselves living in a thousand years, or a million years time.

Then, when we get there, should we, the ultimate survivors, the one in a trillion chancers, mourn our alter-egos who never made it? No, probably no more than we do now. We are already, as individuals, statistically so improbable as to be a seeming miracle. Having made it so far, shouldn't we look forward to more of the same?

Each of us is ordinary, and yet one of a kind.

Each of us is standard-issue, conceived by the union of two germ cells, nurtured in a womb, and equipped with a developmental program that guides our further maturation and eventual decline.

Each of us is also unique, the possessor of a particular selection of gene variants from the collective human genome, and immersed in a particular family, culture, era, and peer group. With inborn tools for adaptation to the circumstances of our personal world we keep building our own ways of being and the sense of who we are.

This dual view of each of us, as both run-of-the-mill and special, has been so well established by biologists and behavioral scientists that it may now seem self-evident. But it still deserves conscious attention as a specific cognitive chunk because it has such important implications. Recognizing how much we share with others promotes compassion, humility, respect, and brotherhood. Recognizing that we are each unique promotes pride, self-development, creativity, and achievement.

Embracing these two aspects of our personal reality can enrich our daily experience. It allows us to simultaneously enjoy the comfort of being ordinary and the excitement of being one of a kind.

The world consists of things, which obey rules.  If you keep asking "why" questions about what happens in the universe, you ultimately reach the answer "because of the state of the universe and the laws of nature."

This isn't an obvious way for people to think.  Looking at the universe through our anthropocentric eyes, we can't help but view things in terms of causes, purposes, and natural ways of being.  In ancient Greece, Plato and Aristotle saw the world teleologically — rain falls because water wants to be lower than air, animals (and slaves) are naturally subservient to human citizens.

From the start, there were skeptics.  Democritus and Lucretius were early naturalists, who urged us to think in terms of matter obeying rules rather than chasing final causes and serving underlying purposes.  But it wasn't until our understanding of physics was advanced by thinkers such as Avicenna, Galileo, and Newton that it became reasonable to conceive of the universe evolving under its own power, free of guidance and support from anything beyond itself.

Theologians sometimes invoke "sustaining the world" as a function of God.  But we know better; the world doesn't need to be sustained, it can simply be.  Pierre-Simon Laplace articulated the very specific kind of rule that the world obeys:  if we specify the complete state of the universe (or any isolated part of it) at some particular instant, the laws of physics tell us what its state will be at the very next moment.  Applying those laws again, we can figure out what it will be a moment later.  And so on, until (in principle, obviously) we can build up a complete history of the universe.  This is not a universe that is advancing toward a goal; it is one that is caught in the iron grip of an unbreakable pattern.

This view of the processes at the heart of the physical world has important consequences for how we come to terms with the social world. Human beings like to insist that there are reasons why things happen. The death of a child, the crash of an airplane, or a random shooting must be explained in terms of the workings of a hidden plan.  When Pat Robertson suggested that Hurricane Katrina was caused in part by God's anger at America's failing morals, he was attempting to provide an explanatory context for a seemingly inexplicable event.

Nature teaches us otherwise.  Things happen because the laws of nature say they will — because they are the consequences of the state of the universe and the path of its evolution.  Life on Earth doesn't arise in fulfillment of a grand scheme, but rather as a byproduct of the increase of entropy in an environment very far from equilibrium.  Our impressive brains don't develop because life is guided toward greater levels of complexity and intelligence, but from the mechanical interactions between genes, organisms, and their surroundings.

None of which is to say that life is devoid of purpose and meaning. Only that these are things we create, not things we discover out there in the fundamental architecture of the world. The world keeps happening, in accordance with its rules; it's up to us to make sense of it and give it value.

A shorthand abstraction I find to be particularly useful in my own cognitive toolkit comes from the world of computer science, and applies broadly in my experience to science and scientists. GIGO means "garbage in, garbage out." Its application in the computer world is straightforward and easy to understand, but I have found much broader applications throughout my career in paleoanthropology.

In computer work, garbage results can arise from bad data or from poorly conceived algorithms applied to analysis — I don't expect that the results from both of these combined are a different order of garbage because bad is bad enough. The science I am used to practicing has far too many examples of mistaken, occasionally fraudulent data and inappropriate, even illogical analysis, and it is all too often impossible to separate conclusions from assumptions.

I don't mean to denigrate paleoanthropology, which I expect is quite like other sciences in these respects, and wherein most work is superbly executed and cannot be described this way. The value of GIGO is to sharpen the skeptical sense and the critical facility because the truth behind GIGO is simple: science is a human activity.

When I go about doing what I do, frequently I affect you as an incidental side effect. In many such cases, I don't have to pay you to compensate for any inadvertent harm done; symmetrically, you frequently don't have to pay me for any inadvertent benefits I've bestowed upon you. The term — externalities — refers to these cases, and they are pervasive and important because, especially in the modern, interconnected world, when I go about pursuing my own goals, I wind up affecting you in any number of different ways.

Externalities can be small or large, negative and positive. When I lived in Santa Barbara, many people with no goal other than working on their tans generated (small, true) positive externalities for passersby, who benefitted from the enhanced scenery. These onlookers didn't have to pay for this improvement to the landscape but, on the same beach, rollerbladers, traveling at high speed and distracted by this particular positive externality, occasionally produced a negative one in the form of a risk of collision for pedestrians trying to enjoy the footpath.

Externalities loom large in the present era, when actions in one place can potentially affect others half a world away. When I manufacture widgets for you to buy, to make them I might, as a side effect of the process, produce waste that makes the people around my factory — and maybe around the world — worse off. As long as I don't have to compensate anyone for polluting their water and air, it's unlikely I'll make much of an effort to stop doing it.

At a smaller, more personal scale, we all impose externalities on one another as we go through our daily lives. I drive to work, increasing the amount of traffic you face. You feel the strange compulsion that infects people in theaters these days to check your text messages on your cell phone during the film, and the bright glow peeking over your shoulder reduces my enjoyment of the movie.

The concept of externalities is useful because it directs our attention to unintended side effects. If you weren't focused on externalities, you might think that the way to reduce traffic congestion was to build more roads. That might work, but another way, and a potentially more efficient way, is to implement policies that force drivers to pay the cost of their negative externalities by charging a price to use roads, particularly at peak times. Congestion charges, such as those implemented in London and Singapore, are designed to do exactly this. If I have to pay to go into town during rush hour, I might stay home unless my need is pressing.

Keeping externalities firmly in mind also reminds us to be vigilant about the fact that in complex, integrated systems, simple interventions designed to bring about a particular desirable effect will potentially have many more consequences, both positive and negative. Consider, as an example, the history of DDT. When first used, it had its intended effect, which was to reduce the spread of malaria through the control of mosquito populations. However, its use also had two unintended consequences. First, it poisoned a number of animals (including humans) and, second, it selected for resistance among mosquitoes. Subsequently, policies to reduce the use of DDT probably were effective in their goals of preventing these two negative consequences. However, while there is some debate about the details, these policies might themselves have had an important side effect, increasing rates of malaria, carried by the mosquitoes no longer suppressed by DDT.

The key point is that the notion of externalities forces us to think about unintended (positive and negative) effects of actions, an issue that looms larger as the world gets smaller. It highlights the need to balance not only the intended costs and benefits of a given candidate policy, but also the unintended effects of the policy. Further, it helps focus attention on one type of solution to the problems of unintended harms, which is to think about using prices to provide incentives for people and firms to produce more positive externalities and fewer negative ones.

Considering externalities in our daily lives directs our attention to ways in which we harm, albeit inadvertently, the other people around us, and can be used to guide our own decision making, including waiting until after the credits have rolled to check our messages.

Our very brains revolt at the idea of randomness. We have evolved as a species to become exquisite pattern-finders — long before the advent of science, we figured out that a salmon-colored sky heralds a dangerous storm, or that a baby's flushed face likely means a difficult night ahead. Our minds automatically try to place data in a framework that allows us to make sense of our observations and use them to understand events and predict them.

Randomness is so difficult to grasp because it works against our pattern-finding instincts. It tells us that sometimes there is no pattern to be found. As a result, randomness is fundamental limit to our intuition; it says that there are processes that we can't predict fully. It's a concept that we have a hard time accepting even though it is an essential part of the way the cosmos works. Without an understanding of randomness, we are stuck in a perfectly predictable universe that simply doesn't exist outside of our own heads.

I would argue that only once we understand three dicta — three laws of randomness — can we break out of our primitive insistence on predictability and appreciate the universe for what it is rather than what we want it to be.

The First Law of Randomness: There is such a thing as randomness.

We use all kinds of mechanisms to avoid confronting randomness. We talk about karma, in a cosmic equalization that ties seemingly unconnected events together. We believe in runs of luck, both good and ill, and that bad things happen in threes. We argue that we are influenced by the stars, by the phases of the moon, and by the motion of the planets in the heavens. When we get cancer, we automatically assume that something — or someone — is to blame.

But many events are not fully predictable or explicable. Disasters happen randomly, to good people as well as to bad ones, to star-crossed individuals as well as those who have a favorable planetary alignment. Sometimes you can make a good guess about the future, but randomness can confound even the most solid predictions — don't be surprised when you're outlived by the overweight, cigar-smoking, speed-fiend motorcyclist down the block.

What's more, random events can mimic non-random ones. Even the most sophisticated scientists can have difficulty telling the difference between a real effect and a random fluke. Randomness can make placebos seem like miracle cures, harmless compounds appear to be deadly poisons, and can even create subatomic particles out of nothing.

The Second Law of Randomness: Some events are impossible to predict.

If you walk into a Las Vegas casino and observe the crowd gathered around the craps table, you'll probably see someone who thinks he's on a lucky streak. Because he's won several rolls in a row, his brain tells him that he's going to keep winning, so he keeps gambling. You'll probably also see someone who's been losing. The loser's brain, like the winner's, tells him to keep gambling. Since he's been losing for so long, he thinks he's due for a stroke of luck; he won't walk away from the table for fear of missing out.

Contrary to what our brains are telling us, there's no mystical force that imbues a winner with a streak of luck, nor is there a cosmic sense of justice that ensures that a loser's luck will turn around. The universe doesn't care one whit whether you've been winning or losing; each roll of the dice is just like every other.

No matter how much effort you put into observing how the dice have been behaving or how meticulously you have been watching for people who seem to have luck on their side, you get absolutely no information about what the next roll of a fair die will be. The outcome of a die roll is entirely independent of its history. And, as a result, any scheme to gain some sort of advantage by observing the table will be doomed to fail. Events like these — independent, purely random events — defy any attempts to find a pattern because there is none to be found.

Randomness provides an absolute block against human ingenuity; it means that our logic, our science, our capacity for reason can only penetrate so far in predicting the behavior of cosmos. Whatever methods you try, whatever theory you create, whatever logic you use to predict the next roll of a fair die, there's always a 5/6 chance you are wrong. Always.

The Third Law of Randomness: Random events behave predictably in aggregate even if they're not predictable individually

Randomness is daunting; it sets limits where even the most sophisticated theories can not go, shielding elements of nature from even our most determined inquiries. Nevertheless, to say that something is random is not equivalent to saying that we can't understand it. Far from it.

Randomness follows its own set of rules — rules that make the behavior of a random process understandable and predictable.

These rules state that even though a single random event might be completely unpredictable, a collection of independent random events is extremely predictable — and the larger the number of events, the more predictable they become. The law of large numbers is a mathematical theorem that dictates that repeated, independent random events converge with pinpoint accuracy upon a predictable average behavior. Another powerful mathematical tool, the central limit theorem, tells you exactly how far off that average a given collection of events is likely to be. With these tools, no matter how chaotic, how strange a random behavior might be in the short run, we can turn that behavior into stable, accurate predictions in the long run.

The rules of randomness are so powerful that they have given physics some of its most sacrosanct and immutable laws. Though the atoms in a box full of gas are moving at random, their collective behavior is described by a simple set of deterministic equations. Even the laws of thermodynamics derive their power from the predictability of large numbers of random events; they are indisputable only because the rules of randomness are so absolute.

Paradoxically, the unpredictable behavior of random events has given us the predictions that we are most confident in.

The famous Canadian physician William Osler once wrote, "In science the credit goes to the man who convinced the world, not to the man to whom the idea first occurs." When we examine discoveries in science and mathematics, in hindsight we often find that if one scientist did not make a particular discovery, some other individual would have done so within a few months or years of the discovery. Most scientists, as Newton said, stood on the shoulders of giants to see the world just a bit further along the horizon. Often, more than one individual creates essentially the same device or discovers the same scientific law at about the same time, but for various reasons, including sheer luck, history sometimes remembers only the more famous discoverer.

In 1858 the German mathematician August Möbius simultaneously and independently discovered the Möbius strip along with a contemporary scholar, the German mathematician Johann Benedict Listing. Isaac Newton and Gottfried Wilhelm Leibniz independently developed calculus at roughly the same time. British naturalists Charles Darwin and Alfred Wallace both developed the theory of evolution independently and simultaneously. Similarly, Hungarian mathematician János Bolyai and Russian mathematician Nikolai Lobachevsky seemed to have developed hyperbolic geometry independently and at the same time.

The history of materials science is replete with simultaneous discoveries. For example, in 1886, the electrolytic process for refining aluminum, using the mineral cryolite, was discovered simultaneously and independently by American Charles Martin Hall and Frenchman Paul Héroult. Their inexpensive method for isolating pure aluminum from compounds had an enormous effect on industry. The time was "ripe" for such discoveries, given humanity's accumulated knowledge at the time the discoveries were made. On the other hand, mystics have suggested that a deeper meaning exists to such coincidences. Austrian biologist Paul Kammerer wrote, "We thus arrive at the image of a world-mosaic or cosmic kaleidoscope, which, in spite of constant shufflings and rearrangements, also takes care of bringing like and like together." He compared events in our world to the tops of ocean waves that seem isolated and unrelated. According to his controversial theory, we notice the tops of the waves, but beneath the surface there may be some kind of synchronistic mechanism that mysteriously connects events in our world and causes them to cluster.

We are reluctant to believe that great discoveries are part of a discovery kaleidoscope and mirrored in numerous individuals at once. However, as further examples, there were several independent discoveries of sunspots in 1611, even though Galileo gets most of the credit today. Halley's Comet, named after English astronomer Edmond Halley, was not first discovered by Halley because it had actually seen by countless observers even before the time of Jesus. However, Halley's useful calculations enabled earlier references to the comet's appearance to be found in the historical record. Alexander Graham Bell and Elisha Gray filed their own patents on telephone technologies on the same day. As sociologist of science Robert Merton remarked, "The genius is not a unique source of insight; he is merely an efficient source of insight."

Robert Merton suggested that "all scientific discoveries are in principle 'multiples'." In other words, when a scientific discovery is made, it is made by more than one person. Sometimes a discovery is named after the person who develops the discovery rather than the original discoverer.

The world is full of difficulties in assigning credit for discoveries. Some of us have personally seen this in the realm of patent law, in business ideas, and in our daily lives. Fully appreciating the concept of the kaleidoscope discovery engine adds to our cognitive toolkits because the kaleidoscope succinctly captures the nature of innovation and the future of ideas. If schools taught more about kaleidoscopic discovery, even in the context of everyday experience, then innovators might enjoy the fruits of their labor and still become "great" without a debilitating concern to be first or to crush rivals. The great anatomist William Hunter frequently quarreled with his brother about who was first in making a discovery. But even Hunter admitted, "If a man has not such a degree of enthusiasm and love of the art, as will make him impatient of unreasonable opposition, and of encroachment upon his discoveries and his reputation, he will hardly become considerable in anatomy, or in any other branch of natural knowledge."

When Mark Twain was asked to explain why so many inventions were invented independently, he said "When it's steamboat time, you steam."

TANSTAAFL is the acronym for "There ain't no such thing as a free lunch," a universal truth having broad and deep explanatory power in science and daily life.

The expression originated from the practice of saloons offering "free lunch" if you buy their overpriced drinks. Science fiction master Robert Heinlein introduced me to TANSTAAFL in The Moon is a Harsh Mistress, his 1966 classic, in which a character warns of the hidden cost of a free lunch.

The universality of the fact that you can't get something for nothing has found application in sciences as diverse as physics (Laws of Thermodynamics) and economics, where Milton Friedman used a grammatically upgraded variant as title of his 1975 book There's No Such Thing as a Free Lunch. Physicists are clearly on board with TANSTAAFL, less so many political economists in their smoke and mirrors world.

My students hear a lot about TANSTAAFL, from the biological costs of the peacock's tail, to our nervous system that distorts physical reality to emphasize changes in time and space. When the final tally is made, peahens cast their ballot for the sexually exquisite plumage of the peacock and its associated vigor, and it is more adaptive for humans to detect critical sensory events than to be high fidelity light and sound meters. In such cases, lunch is not free but comes at reasonable cost as determined by the grim but honest accounting of natural selection, a process without hand-waving and incantation.

Thanks to Karl Popper, we have a simple and powerful tool: the phrase "How Would You Disprove Your Viewpoint?!" 

In a democratic and demotic society like ours, the biggest challenge to scientific thinking is the tendency to embrace views on the basis of faith or of ideology. A majority of Americans doubt evolution because it goes against their religious teachings; and at least a sizeable minority are skeptical about global warming — or more precisely, the human contributions to global change — because efforts to counter climate change would tamper with the 'free market'. 

Popper popularized the notion that a claim is scientific only to the extent that it can be disproved — and that science works through perpetual efforts to disprove claims. 

If American citizens, or, for that matter, citizens anywhere were motivated to decribe the conditions under which they would relinquish their beliefs, they would begin to think scientifically. And if they admitted that empirical evidence would not change their minds, then at least they'd have indicated that their views have a religious or an ideological, rather than a scientific basis.

I

Antifragility

Just as a package sent by mail can bear a stamp "fragile", "breakable" or "handle with care", consider the exact opposite: a package that has stamped on it "please mishandle" or "please handle carelessly". The contents of such package are not just unbreakable, impervious to shocks, but have something more than that , as they tend to benefit from shocks. This is beyond robustness.

So let us coin the appellation "antifragile" for anything that, on average, (i.e., in expectation) benefits from variability. Alas, I found no simple, noncompound word in any of the main language families that expresses the point of such fragility in reverse. To see how alien the concept to our minds, ask around what's the antonym of fragile. The likely answer will be: robust, unbreakable, solid, well-built, resilient, strong, something-proof (say waterproof, windproof, rustproof), etc. Wrong — and it is not just individuals, but branches of knowledge that are confused by it; this is a mistake made in every dictionary. Ask the same person the opposite of destruction, they will answer construction or creation. And ask for the opposite of concavity, they will answer convexity.

A verbal definition of convexity is: benefits more than it loses from variations; concavity is its opposite. This is key: when I tried to give a mathematical expression of fragility (using sums of path-dependent payoffs), I found that "fragile" could be described in terms of concavity to a source of variation (random or nonrandom), over a certain range of variations. So the opposite of that is convexity — tout simplement.

A grandmother's health is fragile, hence concave, with respect to variations in temperature, if you find it preferable to make her spend two hours in 70? F instead of an hour at 0? F and another at 140? F for the exact 70? F on average. (A concave function of a combination f(½ x1+½ x2) is higher than the combination ½ f(x1)+ ½ f(x2).

Further, one could be fragile to certain events but not others: A portfolio can be slightly concave to a small fall in the market but not to extremely large deviations (Black Swans).

Evolution is convex (up to a point) with respect to variations since the DNA benefits from disparity among the offspring. Organisms benefit, up to a point, from a spate of stressors. Trial and error is convex since errors cost little, gains can be large.

Now consider the Triad in the Table. Its elements are those for which I was able to find general concavities and convexities and catalogue accordingly.

The Triad

The larger the corporation, the more concave to some squeezes (although on the surface companies they claim to benefit from economies of scale, the record shows mortality from disproportionate fragility to Black Swan events). Same with government projects: big government induces fragilities. So does overspecialization (think of the Irish potato famine). In general most top-down systems become fragile (as can be shown with a simple test of concavity to variations).

Worst of all, an optimized system becomes quickly concave to variations, by construction: think of the effect of absence of redundancies and spare parts. So about everything behind the mathematical economics revolution can be shown to fragilize.

Further we can look at the unknown, just like model error, in terms of antifragility (that is, payoff): is what you are missing from a model, or what you don't know in real life, going to help you more than hurt you? In other words are you antifragile to such uncertainty (physical or epistemic)? Is the utility of your payoff convex or concave? Pascal was first to express decisions in terms of these convex payoffs. And economics theories produce models that fragilize (except rare exceptions), which explains why using their models is vastly worse than doing nothing. For instance, financial models based on "risk measurements" of rare events are a joke. The smaller the probability, the more convex it becomes to computational error (and the more concave the payoff): an 25% error in the estimation of the standard deviation for a Gaussian can increase the expected shortfall from remote events by a billion (sic) times! (Missing this simple point has destroyed the banking system).

II

Jensen's Inequality as the Hidden Engine of History

Now the central point. By a simple mathematical property, one can show why, under a model of uncertainty, items on the right column will be likely to benefit in the long run, and thrive, more than shown on the surface, and items on the left are doomed to perish. Over the past decade managers of companies earned in, the aggregate, trillions while retirees lost trillions (the fact that executives get the upside not the downside gives them a convex payoff "free option"). And aggressive tinkering fares vastly better than directed research. How?

Jensen's inequality says the following: for a convex payoff, the expectation of an average will be higher than the average of expectations. For a concave one, the opposite (grandmother's health is worse if on average the temperature is 70 than in an average temperature of 70).

Squaring is a convex function. Take a die (six sides) and consider a payoff equal to the number it lands on. You expect 3½. The square of the expected payoff will be 12¼ (square 3½). Now assume we get the square of the numbers on the die, 15.1666, so, the average of a square payoff is higher than the square of the average payoff.

The implications can be striking as this second order effect explains so much of hidden things in history. In expectation, anything that loves Black Swans will be present in the future. Anything that fears it will be eventually gone — to the extent of its concavity.

Is global well being possible?

Scientists commonly predict dystopias: nuclear war, overpopulation, energy shortage, dysgenic selection, widespread soundbyte mentality, and the like. You don't get much attention predicting that the human future will work out. I am not, however, going to predict that a positive human future will in fact occur, but it becomes more likely if we think about it systematically. We can begin by laying out the measurable elements of well being and then asking how those elements might be achieved. I address only measurement.

Well being is about what individuals and societies choose for its own sake , that which is north of indifference. The elements of well being must be exclusive, measurable independently of each other, and ideally, exhaustive. I believe there are five such elements and they have a handy acronym, PERMA:

P Positive Emotion

E Engagement

R Positive Relationships

M Meaning and Purpose

A Accomplishment

There has been forward movement in the measurement of these over the last decade. Taken together PERMA forms a more comprehensive index of well being than "life satisfaction" and it allows for the combining of objective and subjective indicators. PERMA can index the well being of individuals, of corporations, and of cities. The United Kingdom has now undertaken the measurement of well being for the nation and as one criterion — in addition to Gross Domestic Product — of the success of its public policy.

PERMA is a shorthand abstraction for the enabling conditions of life.

How do the disabling conditions, such as poverty, disease, depression, aggression, and ignorance, relate to PERMA? The disabling conditions of life obstruct PERMA, but they do not obviate it. Importantly the correlation of depression to happiness is not minus 1.00, it is only about minus 0.35, and effect of income on life satisfaction is markedly curvilinear, with increased income producing less and less life satisfaction the further above the safety net you are.

Science and public policy have traditionally been focused solely on remediating the disabling conditions, but PERMA suggests that this is insufficient. If we want global well being, we should also measure and try to build PERMA. The very same principal seems to be true in your own life: if you wish to flourish personally, getting rid of depression, anxiety, and anger and getting rich is not enough, you also need to build PERMA directly.

What is known about how PERMA can be built?

Perhaps the Edge Question for 2012 will be "How Can Science Contribute To Building Global Well Being"?

The notion of a probability distribution would, I think, be a most useful addition to the intellectual toolkits of most people.

Most quantities of interest, most projections, most numerical assessments are not point estimates. Rather they are rough distributions — not always normal, sometimes bi-modal, sometimes exponential, sometimes something else.

Related ideas of mean, median, and variance are also important, of course, but the simple notion of a distribution implicitly suggests these and weans people from the illusion that certainty and precise numerical answers are always attainable.

Anthropomorphism means attributing the characteristics of human beings to inanimate things or animals.

I have invented the word pragmamorphism as a short-hand extension for the attribution of the properties of inanimate things to human beings.

One of the meanings of the Greek word pragma is a material object.

Being pragmamorphic sounds as though it would be equivalent to taking a scientific attitude to the world, but it easily evolves into dull scientism.

It's pragmamorphic to equate material correlates with human psychological states, to equate PET scans with emotion. It's also pragmamorphic to ignore human qualities you cannot measure.

We have discovered useful metrics for material objects -- length, temperature, pressure, volume, kinetic energy, etc. Pragmamorphism is a good word for the attempt to assign such one-dimensional thing-metrics to the mental qualities of humans.

IQ, a length scale for intelligence, is a result of pragmamorphism. Intelligence is more diffuse than linear.

The utility function in economics is similar. It's clear that people have preferences. But is it clear that there is a function that describes their preferences?