What happened before the big bang?

This is a frustrating question because first, it presumes that just because we know there was a big bang, we understand it back to t=0 or 'before'. This is like presuming evolutionary biology can explain the origin of life itself, which it cannot, since we need to know chemistry and the conditions of the early earth to address it. Similarly, we simply don't have a physical theory that works back to t=0 so we cannot answer the question. But more than that, all of our current ideas suggest, alas, that it isn't a good question!

Saying that always frustrates the listener, but science often tells us that naive questions aren't good questions because they make presumptions that are unwarranted. Because space and time are coupled to matter and energy in general relativity, it is eminently plausible that if space spontaneously popped into existence, so did time. Namely, time and space are both classical concepts that may have a limited domain of validity, and so the question what was 'before the big bang" may simply be a bad question because there was no 'before'! Time didn't exist.

That answer of course makes no one happy, but it may be true. As I often say, the business of the universe is not to make you happy.

"Who is the next Alan Turing?"

As a technology historian, I get asked "Who is the next Alan Turing?" or "Who is the next John von Neumann?" It is impossible to say! No one expected Alan Turing to be the next Alan Turing, or John von Neumann to be the next John von Neumann. Turing was 24, and von Neumann 26, upon arrival in the United States. I'm at a loss for an answer—except to point out that we gave both of them visas, during a 1930s job market that was just as bad as today. 

How does one justify having worked and continuing to work in the financial sector?

As a financial engineer for the past twenty-five years, the question I have increasingly come to hate being asked is: Wouldn't the world be better off if people like you used their skills on real scientific and engineering problems? Given the taxpayer-funded bailouts of the past few years, the crony capitalism, the refusal to prosecute banks for money laundering, I ask myself this question too. How does one justify having worked and continuing to work in the financial sector?

Some people answer this question by claiming that financial engineering makes markets more efficient. That may or may not be true, but to me efficiency isn't a goal. Here are some of my answers.

First, I think everything on earth is interesting and worthy of understanding, provided that you try to understand it honestly, provided that you try to see the world as it really is. 

Second, like most people I have some measure of ambition and vanity. In 1950, Norman Mailer commented in a letter to William Styron:

"I didn't write [The] Naked [and the Dead]  because I wanted to say war was horrible, or that history is complex, resistant, and almost inscrutable, or because I wanted to say that the coming battle between the naked fanatics and the dead mass was approaching, but because really what I wanted to say was, "Look at me, Norman Mailer, I'm alive, I'm a genius, I want people to know that; I'm a cripple, I want to hide that," and so forth."

Mailer's "Look at me" is a good part of what drives any writer or scientist, and what drives capitalists too, to do what they're good at.

Third, I feel it's my duty to try to convey what I understand from experience, namely that financial models are attempts to describe the relatively calm ripples on a vast and ill-understood sea of violent and ephemeral human passions, using variables such as volatility and liquidity that are crude but quantitative proxies for complex human behaviors. Such models are roughly reliable only as long as the world doesn't change too much. When it does, when crowds panic, anything can happen.  Models are therefore useful, but intrinsically fallible. To confuse a model with the world of humans is a form of idolatry, and dangerous. This understanding, I hope, will enable practitioners and academics to use models more wisely.

When will there be a single unified "behavioral" theory of economic activity?

Never.

If you want a single, unified theory of economic behavior we already have the best one available, the selfish, rational agent model. For simplicity and elegance this cannot be beat. Expected utility theory is a great example of such a theory. von Neumann was no dummy! And if you want to teach someone how to make good decisions under uncertainty, you should teach them to maximize expected utility.

The problem comes if, instead of trying to advise them how to make decisions, you are trying to predict what they will actually do. Expected utility theory is not as good for this task. That is the purpose for which Kahneman and Tversky's descriptive alternative, prospect theory, was invented. But of course prospect theory was incomplete. It did not tell us where the reference point comes from, and did not provide a theory of mental accounting to tell us how people will combine gains and losses in various domains.

As people make progress filling in the holes in prospect theory we can do a better job of understanding and predicting behavior, but prospect theory was already more complicated than expected utility theory, and adding these dimensions further adds to the complexity. Some people wrongly conclude from this that behavioral economists are just adding degrees of freedom, allowing us to "explain anything".

This is an incorrect conclusion to draw because these additional complexities are not coming out of thin air to deal with some empirical anomaly, they are based on evidence about how people actually behave.

Just as psychology has no unified theory but rather a multitude of findings and theories, so behavioral economics will have a multitude of theories and variations on those theories. You need to know both physics and engineering to be able to build a structurally sound bridge, and as far as I know there is no general theory of structural engineering. But (most) bridges are still standing. As economics becomes more like engineering, it will become more useful, but it will not have a unified theory.

Sorry.

"Where does it happen in the brain?"

I often get some version of this question when I give a public presentation on some aspect of psychology, and my heart sinks when I hear it. I usually don't know the answer. And I usually don't care. As an example, much of my recent work focuses on moral psychology. There is a lot of neuroscientific research in this area, and some of it is terrific. But the value of such research is that it helps us explore interesting and substantive issues, such as the precise influence that emotions such as disgust and anger have on moral judgment. The brute facts about brain localization—that the posterior cingulate gyrus is active during certain sorts of moral deliberation, say—are, in and of themselves, boring.

Most of all, the sort of person who asks this question usually knows nothing about the brain. I could make up an answer—"it's mostly in the flurbus murbus"—and my questioner would be satisfied. What the person really wants is some reassurance that there is true science going on, that the phenomenon that I'm discussing actually exists—and this means that I have to say something specific about the brain. This assumption reflects a fundamental and widespread confusion about the mind and how to study it, and so it's a question that I dread. 

"Isn't That Obvious?"

When I used to teach introductory social psychology, at some point in the semester I let it slip to my students that I was the oldest child in a family of five siblings. Their response was always the same: "You're totally the oldest child!" When I asked why, they came up with an impressive list of supporting attributes (I assume they were primarily positive because I was grading them): "You're smart" "hard-working" "funny" "successful" and on and on.

But then I played a little trick on my students. I told them the truth: I am in fact the youngest of five. Their response? A one-second pause, followed by: "You're totally the youngest child!" Why? "You're smart, hardworking, funny, successful" and on and on again.

Unlike some other branches of science, social science has an unusual curse: regardless of what social scientists discover, laypeople tend to feel that they "knew it all along" such that even the most counterintuitive finding quickly feels, well, obvious. (In contrast, people don't usually think that the general theory of relativity is obvious after their first—or fiftieth—encounter).

Another example from the classroom: I tell students to think of reasons why the phrase "birds of a feather flock together" is true, and then to think of reasons why the phrase "opposites attract" is true. Again, an impressive ability to support the veracity of the statements is evident—despite the fact that the two phrases are in direct contradiction, both feel completely obvious in isolation.

Social scientists refer to this phenomenon as hindsight bias: once we know something, we are unable to not know it—making it feel as though we've always known it. In fact, now that you've learned what hindsight bias is, you probably instantly had the feeling that you've always known about hindsight bias.

Some of the most brilliant and insightful findings in social science are brilliant and insightful precisely because once they are explained to us, they feel deeply and powerfully true—even though one moment before, we believed that the exact opposite was deeply and powerfully true.

An example. It makes perfect sense that the more someone is paid to complete a series of tasks, the more tasks they will complete. All else equal, who wouldn't work harder when being paid twice as much for completing the same task? Well, it turns out that in some cases, paying people more can actually make them work less. A lot less. In particular, when people love some task because they find it intrinsically rewarding, paying them to perform it "crowds out" their motivation. Students who love to study because they enjoy learning study less when they are suddenly paid for their grades; musicians frequently lose their love of music once they "sell out."

Crowding out seems obvious now, right? But it doesn't to the many people who continue to believe that higher and higher wages will invariably lead to better performance. Just like my students, once we hear one truism (birds of a feather) the whole world seems to align; but when we hear another truism (opposites attract), the world instantly realigns.

The thing is, it's rarely all A or all B, despite our inclination to see the world that way. Under some circumstances, birds of a feather do flock together; under others, opposites really do attract. Under some circumstances, paying more does improve performance; under others, it greatly harms it. Rather than see both A and B as true, social scientists explore the moderating and mitigating factors that allow us to predict when each is true. In part, social scientists test whether—and under what conditions—the theories laypeople have about the world are accurate.

Here's how to combat the "isn't this obvious?" dilemma. When I am ruining a party by describing some new research project, I often first describe the results exactly backwards: "People who we told to do X were far more likely to engage in behavior Y than people who we told to do Z." The response is invariable: "we already knew that." When I then inform people that in fact our results show that it was people told to do Z who are more likely to engage in Y, I can prevent hindsight bias—though inevitably, at the cost of further running the party.

"How can we have this much data and still not understand collective human behavior?"

If you want to study the inner workings of a giant organization distributed around the globe, here are two approaches you could follow—each powerful, but very different from each other. First, you could take the functioning of a large multinational corporation as your case study, embed yourself within it, watch people in different roles, and assemble a picture from these interactions. Alternately, you could do something very different: take the production of articles on Wikipedia as your focus, and download the site's complete edit-by-edit history back to the beginning: every revision to every page, and every conversation between two editors, time-stamped and labeled with the people involved. Whatever happened in the sprawling organization that we think of as Wikipedia—whatever process of distributed self-organization on the Internet it took to create this repository of knowledge—a reflection of it should be present and available in this dataset. And you can study it down to the finest resolution without ever getting up from your couch. 

These Wikipedia datasets—and many other sources like them—are completely public; the same story plays out with restricted access if you're a data scientist at Facebook, Amazon, Google, or any of a number of other companies: every conversation within people's interlocking social circles, every purchase, every expression of intent or pursuit of information. And with this hurricane of digital records, carried along in its wake, comes a simple question: How can we have this much data and still not understand collective human behavior? 

There are several issues implicit in a question like this. To begin with, it's not about having the data, but about the ideas and computational follow-through needed to make use of it—a distinction that seems particularly acute with massive digital records of human behavior. When you personally embed yourself in a group of people to study them, much of your data-collection there will be guided by higher-level structures: hypotheses and theoretical frameworks that suggest which observations are important. When you collect raw digital traces, on the other hand, you enter a world where you're observing both much more and much less—you see many things that would have escaped your detection in person, but you have much less idea what the individual events mean, and have no a priori framework to guide their interpretation. How do we reconcile such radically different approaches to these questions? 

In other words, this strategy of recording everything is conceptually very simple in one sense, but it relies on a complex premise: that we must be able to take the resulting datasets and define richer, higher-level structures that we can build on top of them. 

What could a higher-level structure look like? Consider one more example—suppose you have a passion for studying the history of the Battle of Gettysburg, and I offer to provide you with a dataset containing the trajectory of every bullet fired during that engagement, and all the movements and words uttered by every soldier on the battlefield. What would you do with this resource? For example, if you processed the final day of the data, here are three distinct possibilities. First, maybe you would find a cluster of actions, movements, and words that corresponded closely to what we think of as Pickett's Charge, the ill-fated Confederate assault near the close of the action. Second, maybe you would discover that Pickett's Charge was too coarse a description of what happened—that there is a more complex but ultimately more useful way to organize what took place on the final day at Gettysburg. Or third, maybe you wouldn't find anything interesting at all; your analysis might spin its wheels but remain mired in a swamp of data that was recorded at the wrong granularity. 

We don't have that dataset for the Battle of Gettysburg, but for public reaction to the 2012 U.S. Presidential Election, or the 2012 U.S. Christmas shopping season, we have a remarkable level of action-by-action detail. And in such settings, there is an effort underway to try defining what the consequential structures might be, and what the current datasets are missing—for even with their scale, they are missing many important things. It's a convergence of researchers with backgrounds in computation, applied mathematics, and the social and behavioral sciences, at the start of what is by every indication a very hard problem. We see glimpses of the structures that can be found—Trending Topics on Twitter, for example, is in effect a collection of summary news events induced by computational means from the sheer volume of raw tweets—but a general attack on this question is still in its very early stages. 

And you shouldn't stop there—you can and should ask about the potential dangers of recording, stockpiling, and analyzing so much data on human behavior. In doing so you'd tap into an ongoing conversation bringing together scientists, policy-makers, large Internet sites, and the public, focused on a changing landscape around notions of individual privacy. It's important to push forward on all these questions, because in a world where both the possibilities and the risks are evolving rapidly, the worst thing you can do is not ask. 

"What about me?"

I am willing to bet that every psychologist, no matter what aspect of the human mind he or she studies, has experienced something like the following more than once. You meet a stranger, mention that you work as a psychologist, and then the strangers asks, "Are you analyzing me?"

I dread this question because it reflects both a superficial misunderstanding of what many psychologists do for a living, but it also highlights a deeper limitation in psychological science. The superficial misunderstanding comes from the long shadow that Sigmund Freud still casts on our field. Although many psychologists working as clinicians and therapists do indeed analyze individuals, trying carefully to understand their problems, many work as scientists trying to understand how the mind works just as a chemist tries to understand how chemical bonds work. I, and every other psychologist you'll see on the Edge site, is of the latter type.

This misconception is easy to fix, and the great popular writing of many authors from this very site is shortening Freud's shadow. But I dread this question more because of the deeper issue it raises. "Are you analyzing me?" implies that I, as a psychologist, could indeed analyze you. The problem is that psychological science has, and always will be, a group-based enterprise. We randomly assign volunteers in our experiments to one condition or another and then analyze the average differences between our conditions relative to the variability within those conditions. We do not analyze individuals in our experiments, nor we do know why some people within a given condition in an experiment behavior differently than others. Our understanding of our own research participants is relatively coarse.

All sciences work this way. In medicine, for instance, doctors prescribe drugs because the average outcome of those in the treatment group of a drug trial were better than the outcome of those in the placebo group. Not everyone in the treatment group improved and some improved more than others, but enough got better that doctors think it's likely that you'll get better if you take this drug as well. But as a psychologist, I often field questions that call on me to offer more individualized answers than our science can warrant. I'm asked to give precise advice and recommend exact solutions when what we can offer is general advice and broad solutions that may or may not apply exactly to your particular problem. I dread having to explain all of this to people. In fact, I dreaded trying to explain it in this little essay.

So if you should happen to sit next to me on a train or a plane, I'll happily start up a conversation with you and explain that I'm a psychologist. Just rest assured that I am not, in fact, analyzing you. 

To Michael Norton:

I love your way of countering this problem. Here's another I have seen used.

I once did a paper on discrimination and faced the same problem. After the fact, people said, "Oh yes, of course you find that level of discrimination".  The thing was that when I asked people before running the experiment, they all said "You will never find that". What I should have done—and what people like Dan Gilbert do brilliantly—is to run another study, a prediction study. Ask a group of people—experts, lay people—to predict what I will find. Then I would have two cells: the reality and the prediction. The "that was obvious" criticism falls by the wayside pretty quickly. 

To Lawrence Krauss:

As a naïve reader, is this partly a problem of framing? If we had scaled time t as T = log(t), then we'd have a scale that went from negative to positive infinity and then no one would ask "what happened before negative infinity"?  Or am I only further obscuring the issue? 

To Jon Kleinberg:

Your story of Gettysburg makes a persuasive case that we have "too much data". But consider this: if I went to one of my colleagues and said, "The real problem of social science is that we have way too much data," they'd laugh at me (more than they already do!).  And they'd proceed to list off dozens (hundreds) of things we have few measurements of. 

So I am torn: do we have too much data or too little data? This conundrum is at the heart of so many of the issues surrounding "big data". Yes, Twitter has a firehose of data. And yes it is "too much" in the way you describe. But isn't it too little in another sense? It seems like a very thin (but very, very long) slice of social life. 

"My Granny saw a real ghost—how do you explain that?"

For more than twenty years my field was parapsychology. I started out as a wide-eyed student, convinced of the reality of telepathy, clairvoyance, precognition, spirits, ghosts and all things psychic. A few years of increasingly careful experimentation convinced me that most of the phenomena did not exist. So I turned to investigating some genuinely puzzling experiences.

This was far more satisfying. For example, alien abductions can turn out to be sleep paralysis; a very scary experience that can easily be misinterpreted, and the creepy feeling of déjà vu is an inappropriate feeling of familiarity related to temporal lobe lability.

So if someone says "I had an out of body experience – how do you explain that?' I can help them. I can explain about the construction of the body image in areas near the temporo-parietal junction and how research has discovered that disturbances to this part of the brain induce sensations of flying, floating and leaving the body. If they say "I keep waking up terrified and I can't move and I know there's someone—or something—in the room with me. Am I possessed?" I can explain about sleep paralysis; how the normal paralysis that occurs during dreaming can carry on into a half-waking state that includes buzzing and humming noises, crawly feelings on the skin, and sexual arousal. If they say "When I was a kid I used to be able to wake up in my dreams and control them but I can't do it anymore. Why not?" I can explain about lucid dreaming; the physiological state in which it occurs, the people most likely to have it, and I can even suggest ways of inducing it.

But those wretched ghost stories drive me mad. The problem is not that they cannot be explained but that I am always given far too much completely irrelevant information (My Granny's house is a hundred years old and an old man once died there and it was the full moon and she'd been thinking about …..) and none of the information I need. Indeed the information needed is usually unobtainable so long after the event. So I have to reply "I'm sorry I cannot explain what happened to your Granny," and accept the smug look that says "Ha Ha, I thought so. You scientists won't accept that there are more things in heaven and earth ……"

I want to scream. And this is one of the reasons I eventually left the field for good, unwilling any longer to be "renta-skeptic" for all those TV and radio shows; no longer willing to go on explaining why coincidences are not what they seem to be; no longer able to endure the hate mail from true believers or the cruel accusations of "If only you had an open mind ….."