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

I recently posted a question in this space asking people to name their favorite example of a wrong scientific belief. One of my favorite answers came from Clay Shirky. Here is an excerpt:

The existence of ether, the medium through which light (was thought to) travel. It was believed to be true by analogy — waves propagate through water, and sound waves propagate through air, so light must propagate through X, and the name of this particular X was ether.

It's also my favorite because it illustrates how hard it is to accumulate evidence for deciding something doesn't exist. Ether was both required by 19th century theories and undetectable by 19th century apparatus, so it accumulated a raft of negative characteristics: it was odorless, colorless, inert, and so on.

Several other entries (such as the "force of gravity") shared the primary function of ether: they were convenient fictions that were able to "explain" some otherwise ornery facts. Consider this quote from Max Pettenkofer, the German chemist and physician, is disputing the role of bacteria as a cause of the cholera. "Germs are of no account in cholera! The important thing is the disposition of the individual."

So in answer to the current question I am proposing that we now change the usage of the word Aether, using the old spelling, since there is no need for a term that refers to something that does not exist. Instead, I suggest we use that term to describe the role of any free parameter used in a similar way: that is, Aether is the thing that makes my theory work. Replace the word disposition with Aether in Pettenkofer's sentence above to see how it works.

Often Aetherists (theorists who rely on an Aether variable) think that their use of the Aether concept renders the theory untestable. This belief is often justified during their lifetimes, but then along comes clever empiricists such as Michelson and Morley and last year's tautology become this year's example of a wrong theory.

Aether variables are extremely common in my own field of economics. Utility is the thing you must be maximizing in order to render your choice rational.

Both risk and risk aversion are concepts that were once well defined, but are now in danger of becoming Aetherized. Stocks that earn surprisingly high returns are labeled as risky, because in the theory, excess returns must be accompanied by higher risk. If, inconveniently, the traditional measures of risk such as variance or covariance with the market are not high, then the Aetherists tell us there must be some other risk; we just don't know what it is.

Similarly, traditionally the concept of risk aversion was taken to be a primitive; each person had a parameter, gamma, that measured her degree of risk aversion. Now risk aversion is allowed to be time varying, and Aetherists can say with a straight face that the market crashes of 2001 and 2008 were caused by sudden increases in risk aversion. (Note the direction of the causation. Stocks fell because risk aversion spiked, not vice versa.)

So, the next time you are confronted with such a theory, I suggest substituting the word Aether for the offending concept. Personally, I am planning to refer to the time-varying variety of risk aversion as Aether aversion.

Not all concepts wielded by professional scientists would improve everybody's cognitive toolkit. We are here not looking for tools with which research scientists might benefit their science. We are looking for tools to help non-scientists understand science better, and equip them to make better judgments throughout their lives.

Why do half of all Americans believe in ghosts, three quarters believe in angels, a third believe in astrology, three quarters believe in Hell? Why do a quarter of all Americans and believe that the President of the United States was born outside the country and is therefore ineligible to be President? Why do more than 40 percent of Americans think the universe began after the domestication of the dog?

Let's not give the defeatist answer and blame it all on stupidity. That's probably part of the story, but let's be optimistic and concentrate on something remediable: lack of training in how to think critically, and how to discount personal opinion, prejudice and anecdote, in favour of evidence. I believe that the double-blind control experiment does double duty. It is more than just an excellent research tool. It also has educational, didactic value in teaching people how to think critically. My thesis is that you needn't actually do double-blind control experiments in order to experience an improvement in your cognitive toolkit. You only need to understand the principle, grasp why it is necessary, and revel in its elegance.

If all schools taught their pupils how to do a double-blind control experiment, our cognitive toolkits would be improved in the following ways:

1. We would learn not to generalise from anecdotes.

2. We would learn how to assess the likelihood that an apparently important effect might have happened by chance alone.

3. We would learn how extremely difficult it is to eliminate subjective bias, and that subjective bias does not imply dishonesty or venality of any kind. This lesson goes deeper. It has the salutary effect of undermining respect for authority, and respect for personal opinion.

4. We would learn not to be seduced by homeopaths and other quacks and charlatans, who would consequently be put out of business.

5. We would learn critical and sceptical habits of thought more generally, which not only would improve our cognitive toolkit but might save the 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.

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.

Pundits now invoke culture to explain all manner of tragedies and triumphs, from why a disturbed young man opens fire on a politician, to why African-American children struggle in school, to why the United States can't establish democracy in Iraq, to why Asian factories build better cars. A quick click through a single morning's media, for example, yields the following catch: gun culture, Twitter culture, ethical culture, Arizona culture, always-on culture, winner-take-all culture, culture of violence, culture of fear, culture of sustainability, culture of corporate greed.

Yet no one explains what, exactly, culture is, how it works, or how to change it for the better.

A cognitive tool that fills this gap is the culture cycle, a tool that not only simply describes how culture works, but also clearly prescribes how to make lasting change. The culture cycle is the iterative, recursive process by which 1) people create the cultures to which they later adapt, and 2) cultures shape people so that they act in ways that perpetuate their cultures. In other words, cultures and people (and some other primates) make each other up. This process involves four nested planes: individual selves (their thoughts, feelings, and actions); the everyday practices and artifacts that reflect and shape those selves; the institutions (such as education, law, and media) that afford or discourage certain everyday practices and artifacts; and pervasive ideas about what is good, right, and human that both influence and are influenced by all these levels. (See figure below). The culture cycle rolls for all types of social distinctions, from the macro (nation, race, ethnicity, region, religion, gender, social class, generation, etc.) to the micro (occupation, organization, neighborhood, hobby, genre preference, family, etc.)

One consequence of the culture cycle is that no action is caused by eitherindividual psychological features or external influences. Both are always at work. Just as there is no such thing as a culture without agents, there are no agents without culture. Humans are culturally-shaped shapers. And so, for example, in the case of a school shooting it is overly simplistic to ask whether the perpetrator shot because of either a mental illness or because of his interactions with a hostile and bullying school climate, or with a particularly deadly cultural artifact (i.e., a gun), or with institutions that encourage that climate and allow access to that artifact, or with pervasive ideas and images that glorify resistance and violence. The better question, and the one that the culture cycle requires, is how do these four levels of forces interact? Indeed, researchers at the vanguard of public health contend that neither social stressors nor individual vulnerabilities are enough to produce most mental illnesses. Instead, the interplay of biology and culture, of genes and environments, of nature and nurture is responsible for most psychiatric disorders.

Social scientists succumb to another form of this oppositional thinking. For example, in the face of Hurricane Katrina, thousands of poor African-American residents "chose" not to evacuate the Gulf Coast, to quote most news accounts. More charitable social scientists had their explanations ready, and struggled to get their variables into the limelight. Of course they didn't leave, said the psychologists, because poor people have an external locus of control, low intrinsic motivation, or low self-efficacy. Of course they didn't leave, said the sociologists and political scientists, because their cumulative lack of access to adequate income, banking, education, transportation, healthcare, police protection, and basic civil rights makes staying put is their only option. Of course they didn't leave, said the anthropologists, because their kin networks, religious faith, and historical ties held them there. Of course they didn't leave, said the economists, because they didn't have the material resources, knowledge, or financial incentives to get out.

The irony in the interdisciplinary bickering is that everyone is mostly right. But they are right in the same way that the blind men touching the elephant in the Indian proverb are right: the failure to integrate each field's contributions makes everyone wrong and, worse, not very useful.

The culture cycle captures how these different levels of analyses relate to each other. Granted, our four-level process explanation is not as zippy as the single-variable accounts that currently dominate most public discourse. But it's far simpler and accurate than the standard "it's complicated" and "it depends" answers that more thoughtful experts often supply.

Moreover, built into the culture cycle are the instructions for how to reverse engineer it: a sustainable change at one level usually requires change at all four levels. There are no silver bullets. The ongoing U.S. Civil Rights Movement, for example, requires the opening of individual hearts and mind; and the mixing of people as equals in daily life, along with media representations thereof; and the reform of laws and policies; and fundamental revision of our nation's idea of what a good human being is.

Just because people can change their cultures, however, does not mean that they can do so easily. A major obstacle is that most people don't even realize that they have cultures. Instead, they think that they are standard-issue humans—they are normal; it's all those other people who are deviating from the natural, obvious and right way to be.

Yet we are all part of multiple culture cycles. And we should be proud of that fact, for the culture cycle is our smart human trick. Because of it, we don't have to wait for mutation or natural selection to allow us to range farther over the face of the earth, to extract nutrition from a new food source, or to cope with a change in climate. And as modern life becomes more complex, and social and environmental problems become more widespread and entrenched, people will need to understand and use the culture cycle more skillfully.

As scientists, we're sympathetic to this question. We've asked it of ourselves before, many times, after fruitless days lost at the lab bench or computer seat. If only our brains could find a new way to process the delivered information faster, to interpret it better, to align the world's
noisy torrents of data in an crystalline moment of clarity. In a word, for our brains to forgo their familiar thought sequences, and innovate.

To be sure, the word "innovate" has become something of a badly overused cliche. Tenacious CEO's, clever engineers, and restless artists come to mind before the methodical, data-obsessed scientist. But how often do we consider the cognitive role of innovation in the supposedly bone-dry world of hypothesis-testing, mathematical constraints and data-dependent empiricism?

In the world of science, innovation stretches the mind to find an explanation when the universe wants to hold on to its secrets just a little longer. This can-do attitude is made all the more valuable, not less, in a world constrained by ultimate barriers like continuity of mass and energy, Absolute zero, or the Clausius-Clapeyron relation. Innovation is a critical enabler of discovery around and of these bounds. It is the occasional architect of that rare, wonderful breakthrough even when the tide of scientific opinion is against you.

A reexamination of this word from the scientific perspective reminds us of the extreme power of this cognitive tool, one that most people possess
already. Through innovation, we all can transcend social, professional, political, scientific, and most importantly, personal limits. Perhaps we might all put it to better and more frequent use.

When most people think about evolution by selection, they conjure up phrases such as "survival of the fittest" or "nature red in tooth and claw." These focus attention on the Darwinian struggle for survival. Many scientists, but few others, know that evolution by selection occurs through the process of differential reproductive success by virtue of heritable differences in design, not by differential survival success. And differential reproductive success often boils down to differential mating success, the focus of Darwin's 1871 theory of sexual selection.

Darwin identified two separate (but potentially related) causal processes by which sexual selection occurs. The first, intrasexual or same-sex competition, involves members of one sex competing with each other in various contests, physical or otherwise, the winners of which gain preferential sexual access to mates. Qualities that lead to success evolve. Those linked to failure bite the evolutionary dust. Evolution, change over time, occurs as a consequence of the process of intrasexual competition. The second, intersexual selection, deals with preferential mate choice. If members of one sex exhibit a consensus about qualities desired in mates, and those qualities are partially heritable, then those of the opposite sex possessing the desired qualities have a mating advantage. They get preferentially chosen. Those lacking desired mating qualities get shunned, banished, and remain mateless (or must settle for low quality mates). Evolutionary change over time occurs as a consequence of an increase in frequency of desired traits and a decrease in frequency of disfavored traits.

Darwin's theory of sexual selection, controversial in his day and relatively neglected for nearly a century after its publication, has mushroomed today into a tremendously important theory in evolutionary biology and evolutionary psychology. Research on human mating strategies has exploded over the past decade, as the profound implications of sexual selection become more deeply understood. Adding sexual selection to everyone's cognitive toolkit will provide profound insights into many human phenomena that otherwise remain baffling. In its modern formulations, sexual selection theory provides answers to weighty and troubling questions that elude many scientists and most non-scientists living today:

• Why do male and female minds differ?
• What explains the rich menu of human mating strategies?
• Why is conflict between the sexes so pervasive?
• Why does conflict between women and men focus so heavily on sex?
• What explains sexual harassment and sexual coercion?
• Why do men die earlier than women, on average, in every culture around the world?
• Why are most murderers men?
• Why are men so much keener than women on forming coalitions for warfare?
• Why are men so much more prone to becoming suicide terrorists?
• Why is suicide terrorism so much more prevalent in polygynous cultures that create a greater pool of mateless males?

Adding sexual selection theory to everyone's cognitive toolkit, in short, provides deep insight into the nature of human nature, people's obsession with sex and mating, the origins of sex differences, and many of the profound social conflicts that beset us all.

Everyone should know what proof feels like. It reduces all other species of belief to a distant second-class status. Proof is the far end on a cognitive scale of confidence that varies through levels of doubt. And most people never experience it. 

Feeling proof comes from finishing a proof. It does not come from pointing at a proof in a book or in the brain of an instructor. It comes when the prover himself takes the last logical step on the deductive staircase. Then he gets to celebrate that logical feat by declaring "Q. E. D." or "Quod Erat Demonstrandum" or just "Quite Easily Done." Q. E. D. states that he has proven or demonstrated the claim that he wanted to prove. The proof need not be original or surprising. It just needs to be logically correct to produce a Q. E. D. moment. A proof of the Pythagorean Theorem has always sufficed.

The only such proofs that warrant the name are those in mathematics and formal logic. Each logical step has to come with a logically sufficient justification. That way each logical step comes with binary certainty. Then the final result itself follows with binary certainty. It is as if the prover multiplied the number 1 by itself for each step in the proof. The result is still the number 1. That is why the final result warrants a declaration of Q. E. D. That is also why the process comes to an unequivocal halt if the prover cannot justify a step. Any act of faith or guesswork or cutting corners will destroy the proof and its demand for binary certainty.

The catch is that we can really only prove tautologies.

The great binary truths of mathematics are still logically equivalent to the tautology "1 = 1" or "green is green." This differs from the factual statements we make about the real world — statements such as "Pine needles are green" or "Chlorophyll molecules reflect green light."

These factual statements are approximations. They are technically vague or fuzzy. And they often come juxtaposed with probabilistic uncertainty: "Pine needles are green with high probability." Note that this last statement involves triple uncertainty. There is first the vagueness of green pine needles because there is no bright line between greenness and non-greenness. It is a matter of degree. There is second only a probability whether pine needles have the vague property of greenness. And there is last the magnitude of the probability itself. The magnitude is the vague or fuzzy descriptor "high" because here too there is no bright line between high probability and not-high probability.

No one has ever produced a statement of fact that has the same 100% binary truth status as a mathematical theorem. Even the most accurate energy predictions of quantum mechanics hold only out to a few decimal places. Binary truth would require getting it right out to infinitely many decimal places.

Most scientists know this and rightly sweat it. The logical premises of a math model only approximately match the world that the model purports to model. It is not at all clear how such grounding mismatches propagate through to the model's predictions. Each infected inferential step tends to degrade the confidence of the conclusion as if multiplying fractions less than one. Modern statistics can appeal to confidence bounds if there are enough samples and if the samples sufficiently approximate the binary assumptions of the model. That at least makes us pay in the coin of data for an increase in certainty.

It is a big step down from such imperfect scientific inference to the approximate syllogistic reasoning of the law. There the disputant insists that similar premises must lead to similar conclusions. But this similarity involves its own approximate pattern matching of inherently vague patterns of causal conduct or hidden mental states such as intent or foreseeability. The judge's final ruling of "granted" or "denied" resolves the issue in practice. But it is technically a non sequitir. The product of any numbers between zero and one is again always less than one. So the confidence of the conclusion can only fall as the steps in the deductive chain increase. The clang of the gavel is no substitute for proof.

Such approximate reasoning may be as close as we can come to a Q. E. D. moment when using natural language. The everyday arguments that buzz in our brains hit far humbler logical highs. That is precisely why we all need to prove something at least once — to experience at least one true Q. E. D. moment. Those rare but god-like tastes of ideal certainty can help keep us from mistaking it for anything else.

The phrase "correlation is not a cause" (CINAC) may be familiar to every scientist but has not found its way into everyday language, even though critical thinking and scientific understanding would improve if more people had this simple reminder in their mental toolkit.

One reason for this lack is that CINAC can be surprisingly difficult to grasp. I learned just how difficult when teaching experimental design to nurses, physiotherapists and other assorted groups. They usually understood my favourite example: imagine you are watching at a railway station. More and more people arrive until the platform is crowded, and then — hey presto — along comes a train. Did the people cause the train to arrive (A causes B)? Did the train cause the people to arrive (B causes A)? No, they both depended on a railway timetable (C caused both A and B).

I soon discovered that this understanding tended to slip away again and again, until I began a new regime, and started every lecture with an invented example to get them thinking.

"Right", I might say "Suppose it's been discovered (I don't mean it's true) that children who eat more tomato ketchup do worse in their exams. Why could this be?" They would argue that it wasn't true (I'd explain the point of thought experiments again). "But there'd be health warnings on ketchup if it's poisonous" (Just pretend it's true for now please) and then they'd start using their imaginations.

"There's something in the ketchup that slows down nerves", "Eating ketchup makes you watch more telly instead of doing your homework", "Eating more ketchup means eating more chips and that makes you fat and lazy". Yes, yes, probably wrong but great examples of A causes B — go on. And so to "Stupid people have different taste buds and don't like ketchup", "Maybe if you don't pass your exams your Mum gives you ketchup". And finally " "Poorer people eat more junk food and do less well at school".

Next week: "Suppose we find that the more often people consult astrologers or psychics the longer they live." "But it can't be true — astrology's bunkum" (Sigh … just pretend it's true for now please.) OK. "Astrologers have a special psychic energy that they radiate to their clients", "Knowing the future means you can avoid dying", "Understanding your horoscope makes you happier and healthier" Yes, yes, excellent ideas, go on. "The older people get the more often they go to psychics", "Being healthy makes you more spiritual and so you seek out spiritual guidance". Yes, yes, keep going, all testable ideas, and finally "Women go to psychics more often and also live longer than men."

The point is that once you greet any new correlation with "CINAC" your imagination is let loose. Once you listen to every new science story Cinacally (which conveniently sounds like "cynically") you find yourself thinking: OK, if A doesn't cause B, could B cause A? Could something else cause them both or could they both be the same thing even though they don't appear to be? What's going on? Can I imagine other possibilities? Could I test them? Could I find out which is true? Then you can be critical of the science stories you hear. Then you are thinking like a scientist.

Stories of health scares and psychic claims may get people's attention but understanding that a correlation is not a cause could raise levels of debate over some of today's most pressing scientific issues. For example, we know that global temperature rise correlates with increasing levels of atmospheric carbon dioxide but why? Thinking Cinacally means asking which variable causes which or whether something else causes both, with important consequences for social action and the future of life on earth.

Some say that the greatest mystery facing science is the nature of consciousness. We seem to be independent selves having consciousness and free will, and yet the more we understand how the brain works, the less room there seems to be for consciousness to do anything. A popular way of trying to solve the mystery is the hunt for the "neural correlates of consciousness". For example, we know that brain activity in parts of the motor cortex and frontal lobes correlates with conscious decisions to act. But do our conscious decisions cause the brain activity, does the brain activity cause our decisions, or are both caused by something else?

The fourth possibility is that brain activity and conscious experiences are really the same thing, just as light turned out not to be caused by electromagnetic radiation but to be electromagnetic radiation, or heat turned out to be the movement of molecules in a fluid. At the moment we have no inkling of how consciousness could be brain activity but my guess is that it will turn out that way. Once we clear away some of our delusions about the nature of our own minds, we may finally see why there is no deep mystery and our conscious experiences simply are what is going on inside our brains. If this is right then there are no neural correlates of consciousness. But whether it is or not, remembering CINAC and working slowly from correlations to causes is likely to be how this mystery is finally solved.

As someone who just spent a term teaching freshman introductory biology, and will be doing it again in the coming months, I have to say that the first thing that leapt to my mind as an essential skill everyone should have was algebra. And elementary probability and statistics. That sure would make my life easier, anyway — there's something terribly depressing about seeing bright students tripped up by a basic math skill that they should have mastered in grade school.

But that isn't enough. Elementary math skills are an essential tool that we ought to be able to take for granted in a scientific and technological society. What idea should people grasp to better understand their place in the universe?

I'm going to recommend the mediocrity principle. It's fundamental to science, and it's also one of the most contentious, difficult concepts for many people to grasp — and opposition to the mediocrity principle is one of the major linchpins of religion and creationism and jingoism and failed social policies. There are a lot of cognitive ills that would be neatly wrapped up and easily disposed of if only everyone understood this one simple idea.

The mediocrity principle simply states that you aren't special. The universe does not revolve around you, this planet isn't privileged in any unique way, your country is not the perfect product of divine destiny, your existence isn't the product of directed, intentional fate, and that tuna sandwich you had for lunch was not plotting to give you indigestion. Most of what happens in the world is just a consequence of natural, universal laws — laws that apply everywhere and to everything, with no special exemptions or amplifications for your benefit — given variety by the input of chance. Everything that you as a human being consider cosmically important is an accident. The rules of inheritance and the nature of biology meant that when your parents had a baby, it was anatomically human and mostly fully functional physiologically, but the unique combination of traits that make you male or female, tall or short, brown-eyed or blue-eyed were the result of a chance shuffle of genetic attributes during meiosis, a few random mutations, and the luck of the draw in the grand sperm race at fertilization.

Don't feel bad about that, though, it's not just you. The stars themselves form as a result of the properties of atoms, the specific features of each star set by the chance distribution of ripples of condensation through clouds of dust and gas. Our sun wasn't required to be where it is, with the luminosity it has — it just happens to be there, and our existence follows from this opportunity. Our species itself is partly shaped by the force of our environment through selection, and partly by fluctuations of chance. If humans had gone extinct 100,000 years ago, the world would go on turning, life would go on thriving, and some other species would be prospering in our place — and most likely not by following the same intelligence-driven technological path we did.

And if you understand the mediocrity principle, that's OK.

The reason this is so essential to science is that it's the beginning of understanding how we came to be here and how everything works. We look for general principles that apply to the universe as a whole first, and those explain much of the story; and then we look for the quirks and exceptions that led to the details. It's a strategy that succeeds and is useful in gaining a deeper knowledge. Starting with a presumption that a subject of interest represents a violation of the properties of the universe, that it was poofed uniquely into existence with a specific purpose, and that the conditions of its existence can no longer apply, means that you have leapt to an unfounded and unusual explanation with no legitimate reason. What the mediocrity principle tells us is that our state is not the product of intent, that the universe lacks both malice and benevolence, but that everything does follow rules — and that grasping those rules should be the goal of science.

I invite you to pay attention to anything — the sight of this text, the sensation of breathing, the feeling of your body resting against your chair — for a mere sixty seconds without getting distracted by discursive thought. It sounds simple enough: Just pay attention. The truth, however, is that you will find the task impossible. If the lives of your children depended on it, you could not focus on anything — even the feeling of a knife at your throat — for more than a few seconds, before your awareness would be submerged again by the flow of thought. This forced plunge into unreality is a problem. In fact, it is the problem from which every other problem in human life appears to be made.

I am by no means denying the importance of thinking. Linguistic thought is indispensable to us. It is the basis for planning, explicit learning, moral reasoning, and many other capacities that make us human. Thinking is the substance of every social relationship and cultural institution we have. It is also the foundation of science. But our habitual identification with the flow of thought — that is, our failure to recognize thoughts as thoughts, as transient appearances in consciousness — is a primary source of human suffering and confusion.

Our relationship to our own thinking is strange to the point of paradox, in fact. When we see a person walking down the street talking to himself, we generally assume that he is mentally ill. But we all talk to ourselves continuously — we just have the good sense to keep our mouths shut. Our lives in the present can scarcely be glimpsed through the veil of our discursivity: We tell ourselves what just happened, what almost happened, what should have happened, and what might yet happen. We ceaselessly reiterate our hopes and fears about the future. Rather than simply exist as ourselves, we seem to presume a relationship with ourselves. It's as though we are having a conversation with an imaginary friend possessed of infinite patience. Who are we talking to?

While most of us go through life feeling that we are the thinker of our thoughts and the experiencer of our experience, from the perspective of science we know that this is a distorted view. There is no discrete self or ego lurking like a minotaur in the labyrinth of the brain. There is no region of cortex or pathway of neural processing that occupies a privileged position with respect to our personhood. There is no unchanging "center of narrative gravity" (to use Daniel Dennett's phrase). In subjective terms, however, there seems to be one — to most of us, most of the time.

Our contemplative traditions (Hindu, Buddhist, Christian, Muslim, Jewish, etc.) also suggest, to varying degrees and with greater or lesser precision, that we live in the grip of a cognitive illusion. But the alternative to our captivity is almost always viewed through the lens of religious dogma. A Christian will recite the Lord's Prayer continuously over a weekend, experience a profound sense of clarity and peace, and judge this mental state to be fully corroborative of the doctrine of Christianity; A Hindu will spend an evening singing devotional songs to Krishna, feel suddenly free of his conventional sense of self, and conclude that his chosen deity has showered him with grace; a Sufi will spend hours whirling in circles, pierce the veil of thought for a time, and believe that he has established a direct connection to Allah.

The universality of these phenomena refutes the sectarian claims of any one religion. And, given that contemplatives generally present their experiences of self-transcendence as inseparable from their associated theology, mythology, and metaphysics, it is no surprise that scientists and nonbelievers tend to view their reports as the product of disordered minds, or as exaggerated accounts of far more common mental states — like scientific awe, aesthetic enjoyment, artistic inspiration, etc.

Our religions are clearly false, even if certain classically religious experiences are worth having. If we want to actually understand the mind, and overcome some of the most dangerous and enduring sources of conflict in our world, we must begin thinking about the full spectrum of human experience in the context of science.

But we must first realize that we are lost in thought.

Paradoxes arise when one or more convincing truths contradiction either each other, clash with other convincing truths, or violate unshakeable intuitions. They are frustrating, yet beguiling. Many see virtue in avoiding, glossing over, or dismissing them. Instead we should seek them out, if we find one sharpen it, push it to the extreme, and hope that the resolution will reveal itself, for with that resolution will invariably come a dose of Truth.

History is replete with examples, and with failed opportunities. One of my favorites is Olber's paradox. Suppose the universe were filled with an eternal roughly uniform distribution of shining stars. Faraway stars would look dim because they take up a tiny angle on the sky; but within that angle they are as bright as the Sun's surface. Yet in an eternal and infinite (or finite but unbounded) space, every direction would lie within the angle taken up by some star. The sky would be alight like the surface of the sun. Thus, a simple glance at the dark night sky reveals that the universe must be dynamic: expanding, or evolving. Astronomers grappled with this paradox for several centuries, devising unworkable schemes for its resolution. Despite at least one correct view (by Edgar Allen Poe!), the implications never really permeated even the small community of people thinking about the fundamental structure of the universe. And so it was that Einstein, when he went to apply his new theory to the universe, sought an eternal and static model that could never make sense, introduced a term into his equations which he called his greatest blunder, and failed to invent the big-bang theory of cosmology.

Nature appears to contradict itself with the utmost rarity, and so a paradox can be opportunity for us to lay bare our cherished assumptions, and discover which of them we must let go. But a good paradox can take us farther, to reveal that the not just the assumptions but the very modes of thinking we employed in creating the paradox must be replaced. Particles and waves? Not truth, just convenient models. The same number of integers as perfect squares of integers? Not crazy, though you might be if you invent cardinality. This sentence is false. And so, says Godel, might be the foundations of any formal system that can refer to itself. The list goes on.

What next? I've got a few big ones I'm wrestling with. How can thermodynamics' second law arise unless cosmological initial conditions are fine-tuned in a way we would never accept in any other theory or explanation of anything? How do we do science if the universe is infinite, and every outcome of every experiment occurs infinitely many times?

What impossibility is nagging at you?

The Oracle of Delphi famously pronounced Socrates to be "the most intelligent man in the world because he knew that he knew nothing". Over 2000 years later the physicist-turned-historian Jacob Bronowski would emphasize — in the last episode of his landmark 1970s television series the "Ascent of Man" — the danger of our all-too-human conceit of thinking we know something. What Socrates knew and what Bronowski had come to appreciate is that knowledge — true knowledge — is difficult, maybe even impossible, to come buy, it is prone to misunderstanding and counterfactuals, and most importantly it can never be acquired with exact precision, there will always be some element of doubt about anything we come to "know"' from our observations of the world.

What is it that adds doubt to our knowledge? It is not just the complexity of life: uncertainty is built in to anything we measure. No matter how well you can measure something, you might be wrong by up to ½ of the smallest unit you can discern.

If you tell me I am 6 feet tall, and you can measure to the nearest inch, I might actually be 5' 11 and ½" or 6' and ½" and you (and I) won't know the difference. If something is really small you won't even be able to measure it, and if it is really really small a light microscope (and thus your eye, both of which can only see objects larger than the shortest wavelength of visible light) won't even know it is there. What if you measure something repeatedly?

This helps, but consider the plight of those charged with international standards of weights and measures. There is a lump of metal stored under a glass case in Sèvres, France. It is, by the decree of Le Système International d'Unités, the definition a kilogram. How much does it weigh? Well, by definition whatever it weighs is a kilogram. But the fascinating thing is that it has never weighed exactly the same twice. On those days it weighs less than a kilogram you are not getting such a good deal at the grocery store. On other days you are.

The often blithe way in which scientific "findings" are reported by the popular press can mask just how difficult it is to acquire reliable knowledge. Height and weight are — as far as we know — single dimensions. Consider then how much more difficult it is to measure something like intelligence, the risk of getting cancer from eating too much meat, whether cannibas should be legalized, whether the climate is warming and why, what a "shorthand abstraction" or even "science" is, the risk of developing psychosis from drug abuse, the best way to lose weight, whether it is better to force people receiving state benefits to work, whether prisons work, how to quit smoking, whether a glass of wine every day is good for you, whether it will hurt your children's eyes to use 3D glasses, or even just the best way to brush your teeth. In each case, what was actually measured, or who was measured, who were they compared to, for how long, are they like you and me, were there other factors that could explain the outcome?

The elusive nature of knowledge should remind us to be humble when interpreting it and acting on it, and this should grant us both a tolerance and skepticism towards others and their interpretations:knowledge should always be treated as a hypothesis.

It has only just recently emerged that Bronowski himself was involved in the Second World War project to design nuclear weapons — vicious projectiles of death that don't discriminate between good guys and bad guys. Maybe Bronowski's later humility was borne of this realization — that our views can be wrong and they can have consequences for others' lives.

Eager detractors of science as a way of understanding the world will jump on these ideas with glee, waving them about as proof that "nothing is real" and that science and its outputs are as much a human construct as art or religion. This is facile, ignorant and naïve.

Measurement and the "science" or theories it spawns must be treated with humility precisely because they are powerful ways of understanding and manipulating the world. Their observations can be replicated — even if imperfectly — and others can agree on how to make the measurements on which they depend, be they of intelligence, the mass of the Higgs boson, poverty, the speed at which proteins can fold into their three dimensional structures, or how big gorillas are.

No other system for acquiring knowledge even comes close to science, but this is precisely why we must treat its conclusions with humility. Einstein knew this when he said "all our science measured against reality is primitive and childlike" and yet he added "it is the most precious thing we have".

At every level in the vast and dynamic world of living things lies diversity.  From biomes to biomarkers, the complex array of solutions to the most basic problems regarding survival in a given environment afforded to us by nature is riveting. In the world of humans alone, diversity is apparent in the genome, in the brain and in our behavior. 

The mark of multiple populations lies in the fabric of our DNA. The signature of selfhood in the brain holds dual frames, one for thinking about one's self as absolute, the other in context of others. From this biological diversity in humans arises cultural diversity directly observable in nearly every aspect of how people think, feel and behavior. From classrooms to conventions across continents, the range and scope of human activities is stunning. 

Recent centuries have seen the scientific debate regarding the nature of human nature cast as a dichotomy between diversity on the one hand and universalism on the other. Yet a seemingly paradoxical, but tractable, scientific concept that may enhance our cognitive toolkit over time is the simple notion that diversity is universal.

To improve everybody's cognitive toolkit, the required scientific concept has to be applicable to all humans. It needs to make a difference to us as a species, or, more to the point I am going to make, as a key factor in defining our collective role. This concept must impact the way we perceive who we are and why we are here. Hopefully, it will redefine the way we live our lives and plan for our collective future. This concept must make it clear that we matter.

A concept that might grow into this life-redefining powerhouse is the notion that we, humans in a rare planet, are unique and uniquely important. But what of Copernicanism — the notion that the more we learn about the universe the less important we become? I will argue that modern science, traditionally considered guilty of reducing our existence to a pointless accident in an indifferent universe, is actually saying the opposite. While it does say that we are an accident in an indifferent universe, it also says that we are a rare accident and thus not pointless.

But wait! Isn't it the opposite? Shouldn't we expect life to be common in the cosmos and us to be just one of many creatures out there? After all, as we discover more and more worlds circling other suns, the so-called exoplanets, we find an amazing array of possibilities. Also, given that the laws of physics and chemistry are the same across the universe, we should expect life to be ubiquitous: if it happened here, it must've happened in many other places. So why am I claiming that we are unique?

There is an enormous difference between life and intelligent life. By intelligent life I don't mean clever crows or dolphins, but minds capable of self-awareness and the ability to develop advanced technologies, that is, not just use what is at hand but transform materials into new devices that can perform a multitude of tasks. Keeping this definition in mind, I agree that single-celled life, although dependent on a multitude of physical and biochemical factors, shouldn't be an exclusive property of our planet. First, because life on Earth appeared almost as quickly as it could, no more than a few hundred million years after things quieted down enough; second, due to the existence of extremophiles, life forms capable of surviving in extreme conditions (very hot or cold, very acidic or/and radioactive, no oxygen, etc.), showing that life is very resilient and spreads into every niche that it can.

However, the existence of single-celled organisms doesn't necessarily lead to that of multicellular ones, much less to that of intelligent multicellular ones. Life is in the business of surviving the best way it can in a given environment. If the environment changes, those creatures that can survive under the new conditions will. Nothing in this dynamics supports the notion that once there is life all you have to do is wait long enough and puff, there pops a clever creature. (This smells of biological teleology, the concept that life's purpose is to create intelligent life, a notion that seduces many people for obvious reasons: it makes us the special outcome of some grand plan.) The history of life on Earth doesn't support this evolution toward intelligence: there have been many transitions toward greater complexity, none of them obvious: prokaryotic to eukaryotic unicellular creatures (and nothing more for 3 billion years!), unicellular to multicellular, sexual reproduction, mammals, intelligent mammals, edge.org...Play the movie differently, and we wouldn't be here.

As we look at planet Earth and the factors that came into play for us to be here, we quickly realize that our planet is very special. Here is a short list: the long-term existence of a protective and oxygen-rich atmosphere; Earth's axial tilt, stabilized by a single large moon; the ozone layer and the magnetic field that jointly protect surface creatures from lethal cosmic radiation; plate tectonics that regulate the levels of carbon dioxide and keep the global temperature stable; the fact that our sun is a smallish, fairly stable star not too prone to releasing huge plasma burps. Consequently, it's rather naive to expect life — at the complexity level that exists here — to be ubiquitous across the universe.

A further point: even if there is intelligent life elsewhere and, of course, we can't rule that out (science is much better at finding things that exist than at ruling out things that don't), it will be so remote that for all practical purposes we are alone. Even if SETI finds evidence of other cosmic intelligences, we are not going to initiate a very intense collaboration. And if we are alone, and alone have the awareness of what it means to be alive and of the importance of remaining alive, we gain a new kind of cosmic centrality, very different — and much more meaningful — from the religiously-inspired one of pre-Copernican days, when Earth was the center of Creation: we matter because we are rare and we know it.

The joint realization that we live in a remarkable cosmic cocoon and that we are able to create languages and rocket ships in an otherwise apparently dumb universe ought to be transformative. Until we find other self-aware intelligences, we are how the universe thinks. We might as well start enjoying each other's company.

We are powerfully influenced by irrational processes such as unconscious priming, conformity, groupthink, and self-serving biases. These affect the most trivial aspects of our lives, such as how quickly we walk down a city street, and the most important, such as who we choose to marry. The political and moral realms are particularly vulnerable to such influences. While many of us would like to think that our views on climate change or torture or foreign policy are the result of rational deliberation, we are more affected than we would like to admit by considerations that have nothing to do with reason. 

But this is not inevitable. Consider science. Plainly, scientists are human and possess the standard slate of biases and prejudices and mindbugs. This is what skeptics emphasize when they say that science is "just another means of knowing" or "just like religion". But science also includes procedures — such as replicable experiments and open debate — that cultivate the capacity for human reason. Scientists can reject common wisdom, they can be persuaded by data and argument to change their minds. It is through these procedures that we have discovered extraordinary facts about the world, such as the structure of matter and the evolutionary relationship between monkey and man.

The cultivation of reason isn't unique to science; other disciplines such as mathematics and philosophy possess it as well. But it is absent in much of the rest of life. So I admit to twisting the question a bit: The concept that people need to add to their toolkit isn't a scientific discovery; it is science itself. Wouldn't the world be better off if, as we struggle with moral and political and social problems, we adopted those procedures that make science so successful?

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.

Modern societies waste billions on protective measures whose real aim is to reassure rather than to reduce risk. Those of us who work in security engineering refer to this as "security theatre", and there are examples all around us. We're searched going into buildings that no terrorist would attack. Social network operators create the pretence of a small intimate group of "friends" in order to inveigle users into disclosing personal information that can be sold to advertisers; the users don't get privacy but privacy theatre. Environmental policy is a third example: cutting carbon emissions would cost lots of money and votes, so governments go for gesture policies that are highly visible even if their effect is negligible. Specialists know that most of the actions that governments claim will protect the security of the planet are just theatre.

Theatre thrives on uncertainty. Wherever risks are hard to measure, or their consequences hard to predict, appearance can be easier to manage than reality. Reducing uncertainty and exposing gaps between appearance and reality are among the main missions of science.

Our traditional approach was the painstaking accumulation of knowledge that enables people to understand risks, options and consequences. But theatre is a deliberate construct rather than just an accidental side-effect of ignorance, so perhaps we need to become more sophisticated about theatrical mechanisms too. Science communicators need to become adept at disrupting the show, illuminating the dark corners of the stage and making the masks visible for what they are.

The very idea of a "cognitive toolkit" is one of the most important items in our cognitive toolkit. It is far more than just a metaphor, for the relationship between actual physical tools and the way we think is profound and of immense antiquity.

Ideas such as evolution and a deep prehistory for humanity are as factually well-established as the idea of a round earth, or gravity as a force pulling apples from trees. Only bigots and the misled can doubt them. But the idea that the first chipped stone tool pre-dates, by at least half a million years, the expansion of mind that is so characteristic of humans, should also be knowable by all.

The idea that technology came before humanity and, evolutionarily, paved the way for it, is the scientific concept that I believe should be part of everybody's cognitive toolkit. We could then see that thinking through things and with things, and manipulating virtual things in our minds, is an essential part of critical self-consciousness. The ability to internalize our own creations, by abstracting them, and converting "out-there" tools into mental mechanisms, is what allows the entire scientific project.

We like to draw clear lines between normal and abnormal behavior. It's reassuring, for those who think they're normal. But it's not accurate. Psychology, psychiatry, and behavior genetics are converging to show that there's no clear line between "normal variation" in human personality traits and "abnormal" mental illnesses. Our instinctive way of thinking about insanity — our intuitive psychiatry — is dead wrong.

To understand insanity, we have to understand personality. There's a scientific consensus that personality traits can be well-described by five main dimensions of variation. These "Big Five" personality traits are called openness, conscientiousness, extraversion, agreeableness, and emotional stability. The Big Five are all normally distributed in a bell curve, statistically independent of each other, genetically heritable, stable across the life-course, unconsciously judged when choosing mates or friends, and found in other species such as chimpanzees. They predict a wide range of behavior in school, work, marriage, parenting, crime, economics, and politics.

Mental disorders are often associated with maladaptive extremes of the Big Five traits. Over-conscientiousness predicts obsessive-compulsive disorder, whereas low conscientiousness predicts drug addiction and other "impulse control disorders". Low emotional stability predicts depression, anxiety, bipolar, borderline, and histrionic disorders. Low extraversion predicts avoidant and schizoid personality disorders. Low agreeableness predicts psychopathy and paranoid personality disorder. High openness is on a continuum with schizotypy and schizophrenia. Twin studies show that these links between personality traits and mental illnesses exist not just at the behavioral level, but at the genetic level. And parents who are somewhat extreme on a personality trait are much more likely to have a child with the associated mental illness.

One implication is that the "insane" are often just a bit more extreme in their personalities than whatever promotes success or contentment in modern societies — or more extreme than we're comfortable with. A less palatable implication is that we're all insane to some degree. All living humans have many mental disorders, mostly minor but some major, and these include not just classic psychiatric disorders like depression and schizophrenia, but diverse forms of stupidity, irrationality, immorality, impulsiveness, and alienation. As the new field of positive psychology acknowledges, we are all very far from optimal mental health, and we are all more or less crazy in many ways. Yet traditional psychiatry, like human intuition, resists calling anything a disorder if its prevalence is higher than about 10%.

The personality/insanity continuum is important in mental health policy and care. There are angry and unresolved debates over how to revise the 5th edition of psychiatry's core reference work, the Diagnostic and Statistic Manual of Mental Disorders (DSM-5), to be published in 2013. One problem is that American psychiatrists dominate the DSM-5 debates, and the American health insurance system demands discrete diagnoses of mental illnesses before patients are covered for psychiatric medications and therapies. Also, the U.S. Food and Drug Administration approves psychiatric medications only for discrete mental illnesses. These insurance and drug-approval issues push for definitions of mental illnesses to be artificially extreme, mutually exclusive, and based on simplistic checklists of symptoms. Insurers also want to save money, so they push for common personality variants — shyness, laziness, irritability, conservatism — not to be classed as illnesses worthy of care. But the science doesn't fit the insurance system's imperatives. It remains to be seen whether DSM-5 is written for the convenience of American insurers and FDA officials, or for international scientific accuracy.

Psychologists have shown that in many domains, our instinctive intuitions are fallible (though often adaptive). Our intuitive physics — ordinary concepts of time, space, gravity, and impetus — can't be reconciled with relativity, quantum mechanics, or cosmology. Our intuitive biology — ideas of species essences and teleological functions — can't be reconciled with evolution, population genetics, or adaptationism. Our intuitive morality — self-deceptive, nepotistic, clannish, anthropocentric, and punitive — can't be reconciled with any consistent set of moral values, whether Aristotelean, Kantian, or utilitarian. Apparently, our intuitive psychiatry has similar limits. The sooner we learn those limits, the better we'll be able to help people with serious mental illnesses, and the more humble we'll be about our own mental health.

The Game of Life is a cellular automaton, invented by the British mathematician John Horton Conway in 1970.

Many will already be acquainted with Conway's invention. For those aren't, the best way to familiarize oneself with it is to experiment with one of the many free implementations that can be found on the Internet (or better yet, if you have at least rudimentary programming skills, make one yourself).

Basically, there is a grid and each cell can be in either of two states: dead or alive. One starts by seeding the grid with some initial distribution of live cells. Then one lets the system evolve according to three simple rules.

Why is this interesting? Certainly, the Game of Life is not biologically realistic. It doesn't do anything useful. It isn't even really game in the ordinary sense of the word.

But it's a brilliant demonstration platform for several important concepts — a virtual 'philosophy of science laboratory'. (The philosopher Daniel Dennett has expressed the view that it should be incumbent on every philosophy student to be acquainted with it.) It gives us the microcosm, simple enough that we can easily understand how things are happening, yet with sufficient generative power to produce interesting phenomena.

By playing with the Game of Life for an hour, one can develop an intuitive understanding of the following concepts and ideas:

• Emergent complexity — How complex patterns can arise from very simple rules.

• Basic dynamics concepts — such as the distinction between laws of nature and initial conditions.

• Levels of explanation — One quickly notices patterns arising that can be efficiently described in higher-level terms (such as "gliders", a specific kind of pattern that crawls across the screen) but that are quite cumbersome to describe in the language of the basic physics upon which the patterns supervene (i.e., in terms of individual pixels being alive or dead)

• Supervenience — This leads one to think about the relation between different sciences in the real world… Does chemistry, likewise, supervene on physics? Biology on chemistry? The mind on the brain?

• Concept formation, and carving nature at its joints — how and why we recognize certain types of pattern and give them names. For instance, in the Game of Life one distinguishes "still lives", small local patterns that are stable and unchanging; "oscillators", local patterns that perpetually cycle through a fixed sequence of states; "spaceships", patterns that move across the grid (such as gliders); "guns", stationary patterns that send out an incessant stream of spaceships; and "puffer trains", patterns that move themselves across the grid leaving debris behind. As one begins to form these and other concepts, the chaos on the screen gradually becomes more comprehensible. Developing concepts that carve nature at its joints is the first crucial step towards understanding, not only in the Game of Life but in science and in ordinary life as well.

At a more advanced level, one discovers that the Game of Life is Turing complete. That is, it's possible to build a pattern that acts like a universal Turing machine. Thus, any computable function could be implemented in the Game of Life — including perhaps a function that describes a universe like the one we inhabit. It's also possible to build a universal constructor in the Game of Life, a pattern which can build many types of complex objects, including copies of itself. Nonetheless, it seems that the structures that evolve into Game of Life are different from the ones who find in the real world: Game of Life structures tend to be very fragile in the sense that changing a single cell will often cause them to dissolve. It is interesting to try to figure out exactly what it is about the rules of the Game of Life and the laws of physics that govern our own universe that accounts for these differences.

Conway's Life is perhaps best viewed not as a single shorthand abstraction, but rather as a generator of such abstractions. We get a whole bunch of useful abstractions — or at least a recipe for how to generate them — all for the price of one.

And this, in fact, points us to one especially useful shorthand abstraction: the strategy of Looking for Generators. We confront many problems. We can try to solve them one by one. But alternatively, we can try to create a generator that produces solutions to multiple problems.

Consider, for example, the challenge of advancing scientific understanding. We might make progress by directly tackling some random scientific problem. But perhaps we can make more progress by Looking for Generators and focusing our efforts on certain subsets of scientific problems, namely those whose solutions would do most to facilitate the discovery of many other solutions. On this approach, we would pay most attention to innovations in methodology that can be widely applied; and to the development of scientific instruments that can enable many new experiments; and to improvements in institutional processes, such as peer review, that can make many decisions about whom to hire, fund, and promote more closely reflecting true merit.

In the same vein, we would be extremely interested in developing effective biomedical cognitive enhancers and other ways of improving the human thinker — the brain being, after all, the generator par excellence.

Half a century ago, while advising a UK Metals company, Elliott Jaques had a deep and controversial insight. He noticed that workers at different levels of the company had very different time horizons. Line workers focused on tasks that could be completed in a single shift, while managers devoted their energies to tasks requiring six months or more to complete. Meanwhile, their CEO was pursuing goals realizable only over the span of several years.

After several decades of empirical study, Jaques concluded that just as humans differ in intelligence, we differ in our ability to handle time-dependent complexity. We all have a natural time horizon we are comfortable with, what Jaques called "Time span of discretion," or the length of the longest task an individual can successfully undertake. Jaques observed that organizations implicitly recognize this fact in everything from titles to salary: line workers are paid hourly, managers annually, and senior executives compensated with longer-term incentives such as stock options.

Jaques also noted that effective organizations were comprised of workers of differing time spans of discretion, each working at a level of natural comfort. If a worker's job was beyond their natural time span of discretion, they would fail. If it was less, they would be insufficiently challenged, and thus unhappy.

Time span of discretion is about achieving intents that have explicit time frames. And in Jaques model, one can rank discretionary capacity in a tiered system. Level 1 encompasses jobs such as sales associates or line workers handling routine tasks with a time horizon of up to three months. Levels 2 to 4 encompass various managerial positions with time horizons between one to five years. Level 5 crosses over to five to 10 years and is the domain of small company CEOs and large company executive vice presidents. Beyond Level 5, one enters the realm of statesmen and legendary business leaders comfortable with innate time horizons of 20 years (Level 6), 50 years (Level 7) or beyond. Level 8 is the realm of 100 year thinkers like Henry Ford, while Level 9 is the domain of the Einsteins, Gandhis, and Galileos, individuals capable of setting grand tasks into motion that continue centuries into the future.

Jaques' ideas enjoyed currency into the 1970s and then fell into eclipse, assailed as unfair stereotyping or worse, a totalitarian stratification evocative of Huxley's Brave New World. It is now time to reexamine Jaques theories and revive time span of discretion as a tool for understanding our social structures and matching them to the overwhelming challenges facing global society. Perhaps problems like climate change are intractable because we have a political system that elects Level 2 thinkers to Congress when we really need Level 5s in office. As such, Jaques ideas might help us realize that the old saying, "he who thinks longest wins" is only half the story, and that the society in which everyone explicitly thinks about tasks in the context of time will be the most effective.

Think back to the world 10 years ago. Google had just gotten started; Facebook and Twitter didn't exist. There were no smart phones, no one remotely conceived of the possibility of the 100,000 iPhone apps that exist today. The few large impact technologies (versus slightly incremental advances in technologies) that occurred in the past 10 years were black swan technologies. In his book, Nassim Taleb defines a Black Swan as an event of low probability, extreme impact, and with only retrospective predictability. Black swans can be positive or negative in their impact and are found in every sector. Still, the most pressing reason I believe "black swan technology" is a conceptual tool that should be added to everyone's cognitive toolkit today is simply because the challenges of climate change and energy production we face today are too big to be tackled by known solutions and safe bets.

I recall fifteen years ago when we were starting Juniper networks, there was absolutely no interest in replacing traditional telecommunications infrastructure (ATM was the mantra) with Internet protocols. After all, there were hundreds of billions of dollars invested in the legacy infrastructure, and it looked as immovable as today's energy infrastructure. Conventional wisdom would say to make incremental improvement to maximize the potential of the existing infrastructure. The fundamental flaw in the conventional wisdom is the failure to acknowledge the possibility of a black swan. Improbable is not unimportant. I believe the likely future is not a traditional econometric forecast but rather one of today's improbable becoming tomorrow's conventional wisdom! Who would be crazy enough t forecast in 2000 that by 2010 almost twice as many people in India would have access to cell phones than latrines? Wireless phones were once only for the very rich. With a black swan technology shot you need not be constrained with the limits of the current infrastructure, projections or market. You simply change the assumptions.

Many argue that since we already have some alternative energy technology today, we should quickly deploy it. They fail to see the potential of the Black Swan technology possibilities; they discount them because they mistake improbable for unimportant and cannot imagine the art of the possible which technology enables. I believe doing this alone runs the risk of spending vast amounts of money on outdated conventional wisdom. Even more importantly, it won't solve the problems we face. Any time focused on short-term, incremental solutions will only distract from working on the homeruns that could change the assumptions around energy and society's resources. While there is no shortage of existing technology providing incremental improvements today — whether today's thin film solar cells, wind turbines, or lithium ion batteries — even summed, they are simply irrelevant to the scale of our problems. They may even make interesting and sometimes large businesses, but will not impact the prevailing energy and resource issues at scale. For that we must look for and invest in quantum jumps in technology with low probability of success; we must create in Black Swan technologies. We must enable the multiplication of resources that only technology can do.

So what are these next generation technologies, these black swan technologies of energy? These are risky investments that stand a high chance of failure, but enable larger technological leaps that promise earthshaking impact if successful: making solar power cheaper than coal or viable without subsidies, economically making lighting and air conditioning 80 percent more efficient. Consider 100 percent more efficient vehicle engines, ultra-cheap energy storage, and countless other technological leaps that we can't yet imagine. It's unlikely that any single shot works, of course. But even 10 Google-like disruptions out of 10,000 shots will completely upend conventional wisdom, econometric forecasts, and, most importantly, our energy future.

To do so we must reinvent the infrastructure of society by harnessing and motivating bright minds with a whole new set of future assumptions, asking "what could possibly be?" rather than "what is." We need to create a dynamic environment of creative contention and collective brilliance that will yield innovative ideas from across disciplines to allow innovation to triumph. We must encourage a social ecosystem that encourages taking risks on innovation. Popularization of the concept of the "Black Swan Technology," is essential to incorporate the right mindset into the minds of entrepreneurs, policymakers, investors and the public: that anything (maybe even everything) is possible. If we harness and motivate these bright new minds with the right market signals and encouragement, a whole new set of future assumptions, unimaginable today, will be tomorrow's conventional wisdom.

I first heard the words "absence of evidence is not evidence of absence" as a first-year archaeology undergraduate. I now know it was part of Carl Sagan's retort against evidence from ignorance, but at the time the non-ascribed quote was part of the intellectual toolkit offered by my professor to help us make sense of the process of excavation.

Philosophically this is a challenging concept, but at an archaeological site all became clear in the painstaking tasks of digging, brushing and trowelling. The concept was useful to remind us, as we scrutinised what was there, to take note of the possibility of what was not there. What we were finding, observing, and lifting, were the material remains, the artifacts which had survived, usually as a result of their material or the good fortune of their deposition. There were barely recordable traces of what was there — the charcoal layer of a prehistoric hearth for example — and others recovered in the washing, or the lab, but this was still tangible evidence. What the concept brought home to us was the invisible traces, the material which had gone from our reference point in time, but which still had a bearing in the context.

It was powerful stuff which stirred my imagination. I looked for more examples outside philosophy. I learned about the great near-Eastern archaeologist, Sir Leonard Woolley who, when excavating the 3rd millennium BC Mesopotamian palace at Ur, modern day Iraq. There, he conjured up musical instruments from their absence. The evidence was the holes left in the excavation layers, the ghosts of wooden objects which had long since disappeared into time. He used this absence to advantage by making casts of the holes and realising the instruments as reproductions. It struck me at the time that he was creating works of art. The absent lyres were installations which he rendered as interventions, and transformed into artifacts. More recently the British artist Rachel Whiteread has made her name through an understanding of the absent form, from the cast of a house to the undersides and spaces of domestic interiors.

Recognising the evidence of absence is not about forcing a shape on the intangible, but acknowledging a potency in the not-thereness. Taking the absence concept to be a positive idea, I suggest interesting things happen. For years middle-eastern archaeologists puzzled over the numerous, isolated bath-houses and other structures in the deserts of North Africa. Where was the evidence of habitation? The clue was in the absence: the buildings were used by nomads who left only camel prints in the sand. Their habitations were ephemeral, tents which, if not taken away with them, were of such material that they would too disappear into the sand. Observed again in this light, the ariel photos of desert ruins are hauntingly repopulated.

The absent evidence of ourselves is all around us, beyond the range of digital traces.

When my parents died and I inherited their house, the task of clearing their rooms was both emotional, and archaeological. The last mantelpiece in the sitting room had accreted over 35 years of married life, a midden of photos, ephemera, beach-combing trove and containers of odd buttons and old coins. I wondered what a stranger — maybe a forensic scientist, or traditional archaeologist — would make of this array if the narrative was woven simply from the tangible evidence. But as I took the assemblage apart in a charged moment, I felt there was a whole lot of no-thing which was coming away with it. Something invisible, and unquantifiable, which had been holding these objects in that context.

I recognised the feeling, and cast my memory back to my first archaeological excavation. It was of a long-limbed hound, one of those 'fine, hunting dogs' the classical writer, Strabo, described as being traded from ancient Britain into the Roman world. As I knelt in the 2000 year old grave, carefully removing each tiny bone, as if engaged with a sculptural process, I felt the presence of something absent. I could not quantify it, but it was that unseen 'evidence' which, it seemed, had given the dog its dog-ness.

To get rid of garbage is essential, also of mental garbage. Cognitive toolkits are filled with such garbage, simply because we are victims of ourselves. We should regularly empty this garbage can, or in case we enjoy to sit in garbage, we better check how "shorthand abstractions" (SHA's) limit our creativity (certainly an SHA). Why is the cognitive toolkit filled with garbage?

Let us look back in history (SHA): Modern science (SHA) can be said to have started in 1620 with "Novum Organum" ("New Instrument") by Francis Bacon. It should impress us today that his analysis (SHA) begins with a description (SHA) of four mistakes we run into when we do science. Unfortunately, we usually forget these warnings. Francis Bacon argued that we are — first — victims of evolution (SHA), i.e. that our genes (SHA), define constraints that necessarily limit insight (SHA). Second — we suffer from the constraints of imprinting (SHA); the culture (SHA) we live in provides a frame for epigenetic programs (SHA) that ultimately define the structure (SHA) of neuronal processing (SHA). Third — we are corrupted by language (SHA) as thoughts (SHA) cannot be easily transformed into verbal expressions . Fourth — we are guided or even controlled by theories (SHA), may they be explicit or implicit.

What are the implications for a cognitive toolkit? We are caught for instance in a language trap. On the basis of our evolutionary heritage we have the power of abstraction (SHA), but this has inspite of some advantages we brag about (to make us superior to other creatures) a disastrous consequence: Abstractions are usually represented in words; apparently we cannot do otherwise; we have to "ontologize"; we invent nouns to extract knowledge (SHA) from processes (SHA). ( I do not refer to the powerful pictorial shorthand abstractions). Abstraction is obviously complexity reduction (SHA). We make it simple. Why do we do this? Evolutionary heritage dictates to be fast. However, speed may give an advantage for a "survival toolkit", but not for a "cognitive toolkit". It is a categorical error (SHA) to confuse speed in action with speed in thinking. The selection pressure for speed invites to neglect the richness of facts. This pressure allows the invention (SHA) of a simple, clear, easy to understand, easy to refer to, easy to communicate shorthand abstraction. Thus, because we are a victim of our biological past and as a consequence a victim of ourselves we end up with shabby SHA's having left behind reality. If there is one disease all humans share, it is "monocausalitis", i.e. the motivation (SHA) to explain everything on the basis of just one cause. This may be a nice intellectual exercise but it is simply misleading.

Of course we depend on communication (SHA), and this requires verbal references usually tagged with language. But if we do not understand within the communicative frame or reference system (SHA) that we are a victim of ourselves by "ontologizing" and continuously creating "practical" SHA's, we simply use a cognitive toolkit of mental garbage. Is there a pragmatic way out other than to radically get rid of mental garbage? Yes, perhaps: Simply not using the the key SHA's explicitly in one's toolkit. Working on "consciousness", don't use (at least for one year) the SHA consciousness; if you work on the "self", never refer explicitly to self. Going through the own garbage one discovers many misleading SHA's, like just a few in my focus of attention (SHA): the brain as a net, localization of function, representation, inhibition, threshold, decision, the present, .... An easy way out is of course to refer to some of these SHA's as metaphors (SHA), but this again is evaiding the problem (SHA). I am aware of the fact (SHA) that I am also a victim of evolution, and to suggest "garbage" as a SHA also suffers from the same problem; even the concept of garbage required a discovery (SHA). But we cannot do otherwise than simply being aware of this challenge (SHA) that the content of the cognitive toolkit is characterized by self–referentiality (SHA), i.e. by the fact that the SHA's define themselves by their unreflected use.

I think that an important concept to understand why does life suck so often is Kakonomics, or the weird preference for Low-quality payoffs.

Standard game-theoretical approaches posit that, whatever people are trading (ideas, services, or goods), each one wants to receive High-quality work from others. Let's stylize the situation so that goods can be exchanged only at two qualitylevels: High and Low. Kakonomics describes cases where people not only have standard preferences to receive a High-quality good and deliver a Low-quality one (the standard sucker's payoff) but they actually prefer to deliver a Low-quality good and receive a Low-quality one, that is, they connive on a Low-Low exchange.

How can it ever be possible? And how can it be rational? Even when we are lazy, and prefer to deliver a Low-quality outcome (like prefer to write a piece for a mediocre journal provided that they do not ask one to do too much work), we still would have preferred to work less and receive more, that is deliver Low-quality and receive High-quality. Kakonomics is different: Here, we not only prefer to deliver a Low-quality good, but also, prefer to receive a Low-quality good in exchange!

Kakonomics is the strange — yet widespread — preference for mediocre exchanges insofar as nobody complains about. Kakonomic worlds are worlds in which people not only live with each other's laxness, but expect it: I trust you not to keep your promises in full because I want to be free not to keep mine and not to feel bad about it. What makes it an interesting and weird case is that, in all kakonomic exchanges, the two parties seem to have a double deal: an official pact in which both declare their intention to exchange at a High-quality level, and a tacit accord whereby discounts are not only allowed but expected. It becomes a form of tacit mutual connivance. Thus, nobody is free-riding:Kakonomics is regulated by a tacit social norm of discount on quality, a mutual acceptance for a mediocre outcome that satisfies both parties, as long as they go on saying publicly that the exchange is in fact at a High-quality level.

Take an example: A well-established best-seller author has to deliver his long overdue manuscript to his publisher. He has a large audience, and knows very well that people will buy his book just because of his name and anyway, the average reader doesn't read more than the first chapter. His publisher knows it as well…Thus, the author decides to deliver to the publisher the new manuscript with a stunning incipit and a mediocre plot (the Low-quality outcome): she is happy with it, congratulates him as she had received a masterpiece (the High-quality rhetoric) and they are both satisfied. The author's preference is not only to deliver a Low-quality work, but also that the publisher gives back the same, for example by avoiding to provide a too serious editing and going on publishing. They trust each other's untrustworthiness, and connive on a mutual advantageous Low outcome. Whenever there is a tacit deal to converge to Low-quality with mutual advantages, we are dealing with a case of Kakonomics.

Paradoxically, if one of the two parties delivers a High-quality outcome instead of the expected Low-quality one, the other party resents it as a breach of trust, even if he may not acknowledge it openly. In the example, the author may resent the publisher if she decides to deliver a High-quality editing. Her being trustworthy in this relation means to deliver Low-quality too. Contrary to the standard Prisoner Dilemma game, the willingness to repeat an interaction with someone is ensured if he or she delivers Low-quality too rather than High-quality.

Kakonomics is not always bad. Sometimes it allows a certain tacitly negotiated discount that makes life more relaxing for everybody. As one friend who was renovating a country house in Tuscany told me once: "Italian builders never deliver when they promise, but the good thing is they do not expect you to pay them when you promise either."

But the major problem of Kakonomics — that in ancient Greek means the economics of the worst — and the reason why it is a form of collective insanity so difficult to eradicate, is that each Low-quality exchange is a local equilibrium in which both parties are satisfied, but each of these exchanges erodes the overall system in the long run. So, the threat to good collective outcomes doesn't come only from free riders and predators, as mainstream social sciences teach us, but also from well-organized norms of Kakonomics that regulate exchanges for the worse. The cement of society is not just cooperation for the good: in order to understand why life sucks, we should look also at norms of cooperation for a local optimum and a common worse.

In an ideal world all people would spontaneously understand that what political scientists call path dependence explains much more of how the world works than is apparent. Path dependence refers to the fact that often, something that seems normal or inevitable today began with a choice that made sense at a particular time in the past, but survived despite the eclipse of the justification for that choice, because once established, external factors discouraged going into reverse to try other alternatives.

The paradigm example is the seemingly illogical arrangement of letters on typewriter keyboards. Why not just have the letters in alphabetical order, or arrange them so that the most frequently occurring ones are under the strongest fingers? In fact, the first typewriter tended to jam when typed on too quickly, so its inventor deliberately concocted an arrangement that put A under the ungainly little finger. In addition, the first row was provided with all of the letters in the word typewriter so that salesmen, new to typing, could wangle typing the word using just one row.

Quickly, however, mechanical improvements made faster typing possible, and new keyboards placing letters according to frequency were presented. But it was too late: there was no going back. By the 1890s typists across America were used to QWERTY keyboards, having learned to zip away on new versions of them that did not stick so easily, and retraining them would have been expensive and, ultimately, unnecessary. So QWERTY was passed down the generations, and even today we use the queer QWERTY configuration on computer keyboards where jamming is a mechanical impossibility.

The basic concept is simple, but in general estimation tends to be processed as the province of "cute" stories like the QWERTY one, rather than explaining a massive weight of scientific and historical processes. Instead, the natural tendency is to seek explanations for modern phenomena in present-day conditions.

One may assume that cats cover their waste out of fastidiousness, when the same creature will happily consume its own vomit and then jump on your lap. Cats do the burying as an instinct from their wild days when the burial helped avoid attracting predators, and there is no reason for them to evolve out of the trait now (to pet owners' relief). I have often wished there were a spontaneous impulse among more people to assume that path dependence-style explanations are as likely as jerry-rigged present-oriented ones.

For one, that the present is based on a dynamic mixture of extant and ancient conditions is simply more interesting than assuming that the present (mostly) all there is, with history as merely "the past," interesting only for seeing whether something that happened then could now happen again, which is different from path dependence.

For example, path dependence explains a great deal about language which is otherwise attributed to assorted just-so explanations. Much of the public embrace of the idea that one's language channels how one thinks is based on this kind of thing. Robert McCrum celebrates English as "efficient" in its paucity of suffixes of the kind that complexify most European languages. The idea is that this is rooted in something in its speakers' spirit, which would have propelled them to lead the world via exploration and the Industrial Revolution.

But English lost its suffixes starting in the eighth century, A.D. when Vikings invaded Britain and so many of them learned the language incompletely that children started speaking it that way. After that, you can't create gender and conjugation out of thin air — there's no going back until gradual morphing recreates such things over eons of time. That is, English's current streamlined syntax has nothing to do with any present-day condition of the spirit, nor with any even four centuries ago. The culprit is path dependence, as are most things about how a language is structured.

Or, we hear much lately about a crisis in general writing skills, suposedly due to email and texting. But there is a circularity here — why, precisely, could people not write emails and texts with the same "writerly" style that people used to couch letters in? Or, we hear of a vaguely defined effect of television, despite that kids were curled up endlessly in front of the tube starting in the fifties, long before the eighties when outcries of this kind first took on their current level of alarm in the report A Nation at Risk.

Once again, the presentist explanation does not cohere, whereas one based on an earlier historical development that there is no turning back from does. Public American English began a rapid shift from cosseted to less formal "spoken" style in the sixties, in the wake of cultural changes amidst the counterculture. This sentiment directly affected how language arts textbooks were composed, the extent to which any young person was exposed to an old-fashioned formal "speech," and attitudes towards the English language heritage in general. The result: a linguistic culture stressing the terse, demotic, and spontaneous. After just one generation minted in this context, there was no way to go back. Anyone who decided to communicate in the grandiloquent phraseology of yore would sound absurd and be denied influence or exposure. Path dependence, then, identifies this cultural shift as the cause of what dismays, delights, or just interests us in how English is currently used, and reveals television, email and other technologies as merely epiphenomenal.

Most of life looks path dependent to me. If I could create a national educational curriculum from scratch, I would include the concept as one taught to young people as early as possible.

Humanity's cognitive toolkit would greatly benefit from adoption of "interbeing," a concept that comes from Vietnamese Buddhist monk Thich Nhat Hanh. In his words:

"If you are a poet, you will see clearly that there is a cloud floating in [a] sheet of paper. Without a cloud, there will be no rain; without rain, the trees cannot grow; and without trees, we cannot make paper. The cloud is essential for the paper to exist. If the cloud is not here, the sheet of paper cannot be here either . . . "Interbeing" is a word that is not in the dictionary yet, but if we combine the prefix "inter-" with the verb to be," we have a new verb, inter-be. Without a cloud, we cannot have a paper, so we can say that the cloud and the sheet of paperinter-are. . . . "To be" is to inter-be. You cannot just be by yourself alone. You have to inter-be with every other thing. This sheet of paper is, because everything else is."

Depending on your perspective, the above passage may sound like profound wisdom or New Age mumbo-jumbo. I would like to propose that interbeing is a robust scientific fact — at least insomuch as such things exist — and, further, that this concept is exceptionally critical and timely.

Arguably the most cherished and deeply ingrained notion in the Western mindset is the separateness of our skin-encapsulated selves — the belief that we can be likened to isolated, static machines. Having externalized the world beyond our bodies, we are consumed with thoughts of furthering our own ends and protecting ourselves. Yet this deeply rooted notion of isolation is illusory, as evidenced by our constant exchange of matter and energy with the "outside" world. At what point did your last breath of air, sip of water, or bite of food cease to be part of the outside world and become you? Precisely when did your exhalations and wastes cease being you? Our skin is as much permeable membrane as barrier, so much so that, like a whirlpool, it is difficult to discern where "you" end and the remainder of the world begins. Energized by sunlight, life converts inanimate rock into nutrients, which then pass through plants, herbivores, and carnivores before being decomposed and returned to the inanimate Earth, beginning the cycle anew. Our internal metabolisms are intimately interwoven with this Earthly metabolism; one result is the replacement of every atom in our bodies every seven years or so.

You might counter with something like, "Ok, sure, everything changes over time. So what? At any given moment, you can still readily separate self from other."

Not quite. It turns out that "you" are not one life form — that is, one self — but many. Your mouth alone contains more than 700 distinctkinds of bacteria. Your skin and eyelashes are equally laden with microbes and your gut houses a similar bevy of bacterial sidekicks. Although this still leaves several bacteria-free regions in a healthy body — for example, brain, spinal cord, and blood stream — current estimates indicate that your physical self possesses about a trillion human cells and about 10 trillion bacterial cells. In other words, at any given moment, your body is about 90% nonhuman, home to many more life forms than the number of people presently living on Earth; more even than the number of stars in the Milky Way Galaxy! To make things more interesting still, microbiological research demonstrates that we are utterly dependent on this ever-changing bacterial parade for all kinds of "services," from keeping intruders at bay to converting food into useable nutrients.

So, if we continually exchange matter with the outside world, if our bodies are completely renewed every few years, and if each of us is a walking colony of trillions of largely symbiotic life forms, exactly what is this self that we view as separate? You are not an isolated being. Metaphorically, to follow current bias and think of your body as a machine is not only inaccurate but destructive. Each of us is far more akin to a whirlpool, a brief, ever-shifting concentration of energy in a vast river that's been flowing for billions of years. The dividing line between self and other is, in many respects, arbitrary; the "cut" can be made at many places, depending on the metaphor of self one adopts. We must learn to see ourselves not as isolated but as permeable and interwoven — selves within larger selves, including the species self (humanity) and the biospheric self (life). The interbeing perspective encourages us to view other life forms not as objects but subjects, fellow travelers in the current of this ancient river. On a still more profound level, it enables us to envision ourselves and other organisms not as static "things" at all, but as processes deeply and inextricably embedded in the background flow.

One of the greatest obstacles confronting science education is the fact that the bulk of the universe exists either at extremely large scales (e.g., planets, stars, and galaxies) or extremely small scales (e.g., atoms, genes, cells) well beyond the comprehension of our (unaided) senses. We evolved to sense only the middle ground, or "mesoworld," of animals, plants, and landscapes. Yet, just as we have learned to accept the non-intuitive, scientific insight that the Earth is not the center of the universe, so too must we now embrace the fact that we are not outside or above nature, but fully enmeshed within it. Interbeing, an expression of ancient wisdom backed by science, can help us comprehend this radical ecology, fostering a much-needed transformation in mindset.

It is one of the stranger ideas to emerge from recent physics. Take two theories that describe utterly dissimilar worlds — worlds with different numbers of dimensions, different geometries of spacetime, different building blocks of matter. Twenty years ago, we'd say those are indisputably disparate and mutually exclusive worlds. Today, there's another option: two radically different theories might be dual to one another — that is, they might be two very different manifestations of the same underlying reality.

Dualities are as counterintuitive a notion as they come, but physics is riddled with them. When physicists looking to unite quantum theory with gravity found themselves with five very different but equally plausible string theories, it was an embarrassment of riches — everyone was hoping for one "theory of everything", not five. But duality proved to be the key ingredient. Remarkably, all five string theories turned out to be dual to one another, different expressions of a single underlying theory.

Perhaps the most radical incarnation of duality was discovered in 1997 by Juan Maldacena. Maldacena found that a version of string theory in a bizarrely shaped universe with five large dimensions is mathematically dual to an ordinary quantum theory of particles living on that universe's four-dimensional boundary. Previously, one could argue that the world was made up of particles or that the world was made up of strings. Duality transformed or into and — mutually exclusive hypotheses, both equally true.

In everyday language, duality means something very different. It is used to connote a stark dichotomy: male and female, east and west, light and darkness. Embracing the physicist's meaning of duality, however, can provide us with a powerful new metaphor, a one-stop shorthand for the idea that two very different things might be equally true. As our cultural discourse is becoming increasingly polarized, the notion of duality is both more foreign and more necessary than ever. If accessible in our daily cognitive toolkit, it could serve as a potent antidote to our typically Boolean, two-valued, zero-sum thinking — where statements are either true or false, answers are yes or no, and if I'm right, then you are wrong. With duality, there's a third option. Perhaps my argument is right and yours is wrong; perhaps your argument is right and mine is wrong; or, just maybe, our opposing arguments are dual to one another. 

That's not to say that we ought to descend into some kind of relativism, or that there are no singular truths. It is to say, though, that truth is far more subtle than we once believed, and that it shows up in many guises. It is up to us to recognize it in all its varied forms.

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.

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 recent controversy about the potential dangers to health of the back-scatter radiation devices being introduced at the nation's airports and the intrusive pat-downs offered as the only alternative by the TSA might well have been avoided had citizens been aware of, and understood, the probabilistic notion of base rate.

Whenever a statistician wants to predict the likelihood of some event based on the available evidence, there are two main sources of information that have to be taken into account:

1. The evidence itself, for which a reliability figure has to be calculated;

2. The likelihood of the event calculated purely in terms of relative incidence.

The second figure here is the base rate. Since it is just a number, obtained by the seemingly dull process of counting, it frequently gets overlooked when there is new information, particularly if that new information is obtained by "clever experts" using expensive equipment. In cases where the event is dramatic and scary, like a terrorist attack on an airplane, failure to take account of the base rate can result in wasting massive amounts of effort on money trying to prevent something that is very unlikely.

For example, suppose that you undergo a medical test for a relatively rare cancer. The cancer has an incidence of 1% among the general population. (That is the base rate.) Extensive trials have shown that the reliability of the test is 79%. More precisely, although the test does not fail to detect the cancer when it is present, it gives a positive result in 21% of the cases where no cancer is present — what is known as a "false positive." When you are tested, the test produces a positive diagnosis. The question is: What is the probability that you have the cancer?

If you are like most people, you will assume that if the test has a reliability rate of nearly 80%, and you test positive, then the likelihood that you do indeed have the cancer is about 80% (i.e., the probability is approximately 0.8). Are you right?

The answer is no. You have focused on the test and its reliability, and overlooked the base rate, Given the scenario just described, the likelihood that you have the cancer is a mere 4.6% (i.e., the probability is 0.046). That's right, there is a less than 5% chance that you have the cancer. Still a worrying possibility, of course. But hardly the scary 80% you thought at first.

In the case of the back-scatter radiation devices at the airports, the base rate for dying in a terrorist attack is lower than many other things we do every day without hesitation. In fact, according to some reports, it is about the same as the likelihood of getting cancer as a result of going through the device.

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.

Findex (n): The degree to which a desired piece of information can be found online.

We are the first humans in history to be able to form just about any question in our minds and know that very likely the answer can be before us in minutes, if not seconds. This omnipresent information abundance is a cognitive toolkit entirely in itself. The actuality of this continues to astonish me.

Although some have written about information overload, data smog, and the like, my view has always been the more information online, the better, so long as good search tools are available. Sometimes this information is found by directed search using a web search engine, sometimes by serendipty by following links, and sometimes by asking hundreds of people in our social network or hundreds of thousands of people on a question answering website such as Answers.com, Quora, or Yahoo Answers.

I do not actually know of a real findability index, but tools in the field of information retrieval could be applied to develop one. One of the unsolved problems in the field is how to help the searcher to determine if the information simply is not available.

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.

When it comes to common resources, a failure to cooperate is a failure to control consumption. In Hardin's classic tragedy, everyone overconsumes and equally contributes to the detriment of the commons. But a relative few can also ruin a resource for the rest of us.

Biologists are familiar with the term 'keystone species', coined in 1969 after Bob Paine's intertidal exclusion experiments. Paine found that by removing the few five-limbed carnivores, Pisaster ochraceus, from the seashore, he could cause an overabundance of its prey, mussels, and a sharp decline in diversity. Without seastars, mussels outcompeted sponges. No sponges, no nudibranchs. Anenomes were also starved out because they eat what the seastars dislodge. Pisaster was the keystone that kept the intertidal community together. Without it, there were only mussels, mussels, mussels. The term keystone species, inspired by the purple seastar, refers to a species that has a disproportionate effect relative to its abundance.

In human ecology, I imagine diseases and parasites play a similar role to Pisaster in Paine's experiment. Remove disease (and increase food) and Homo sapiens takeover. Humans inevitably restructure their environment. But not all human beings consume equally. While a keystone species refers to a specific species that structures an ecosystem, I consider keystone consumers to be a specific group of humans that structures a market for a particular resource. Intense demand by a few individuals can bring flora and fauna to the brink.  

There are keystone consumers in the markets for caviar, slipper orchids, tiger penises, plutonium, pet primates, diamonds, antibiotics, Hummers, and seahorses. Niche markets for frog legs in pockets of the U.S., Europe, and Asia are depleting frog populations in Indonesia, Ecuador, and Brazil. Seafood lovers in high-end restaurants are causing stocks of long-lived fish species like Orange roughy or toothfish in Antarctica to crash. The desire for shark fin soup by wealthy Chinese consumers has led to the collapse of several shark species.

One in every four mammals (1,141 of the 5,487 mammals on Earth) is threatened with extinction. At least 76 mammals have become extinct since the 16th century, many, like the Tasmanian tiger, the great auk, and the Steller sea cow, due to hunting by a relatively small group. It is possible for a small minority of humans to precipitate the disappearance of an entire species.

The consumption of non-living resources is also imbalanced. The 15% of the world's population that lives in North America, Western Europe, Japan and Australia consumes 32 times more resources, like fossil fuels and metals, and produces 32 times more pollution than the developing world, where the remaining 85% of humans live. City-dwellers consume more than people living in the countryside. A recent study determined the ecological footprint for an average resident of Vancouver, British Columbia was 13 times higher than his suburban/rural counterpart.

Developed nations, urbanites, ivory collectors: the keystone consumer depends on the resource in question. In the case of water, agriculture accounts for 80% of use in the U.S., i.e. large-scale farms are the keystone consumers. So why do many conservation efforts focus on households rather than water efficiency on farms? The keystone consumer concept helps focus conservation efforts where returns on investments are highest.  

Like keystone species, keystone consumers also have a disproportionate impact relative to their abundance.  Biologists identify keystone species as conservation priorities because their disappearance could cause the loss of many other species. In the marketplace, keystone consumers should be priorities because their disappearance could lead to the recovery of the resource. Humans should protect keystone species and curb keystone consumption. The lives of others depend on it.

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.

The archenemy of scientific thinking is conversation. As in typical human conversational discourse, much of which is BS. Personally I have become rather fed up with talking to people. Seriously, it is something a problem. Fact is, folks are prone to getting pet opinions into their heads and then actually thinking they are true to the point of obstinacy, even when they have little or no idea of what they are talking about in the first place. We all do it. It is part of how the sloppy mind generating piece of meat between our ears we call the human brain is prone to work. Humans may be the most rational beings on the planet these days — but that's not saying much considering that the next most rational are chimpanzees.

Take creationism. Along with the global climate issue and parental fear of vaccination, the fact that a big chunk of the American body politic deny evolutionary and paleontological science and actually think a god created humans in near historical times is causing scientists to wonder just what is wrong with the thinking of so many people — mass creationism has been used as a classic example of mass anti-scientific thinking by others responding to this question. But I am not going to focus so much on the usual problem of why creationism is popular, but more on what many who promote science over creationism think they know about those who deny the reality of Darwin's theory.

A few years back an anti-creationist documentary came out, A Flock of Dodos. Nicely done in many regards, it scored some points against the anti-evolution crowd, and when it came to trying to explain why many Americans are repelled by evolution was way off base. The reason it was so wrong was because the creator of the film, Randy Olson, went to the wrong people to find out where the problem lies. A (seeming) highlight of the picture featured a bunch of poker playing Harvard evolutionary scientists gathered around a table to converse and opine on why the yahoos don't like the results of their research. This was a very bad mistake for the simple reason that evolutionary scientists are truly knowledgeable only about their area of expertise, evolutionary science.

If you really want to know why regular folk think the way they do then you go to the experts on that subject, sociologists. Because A Flock of Dodos never does that, its viewers never find out why creationism thrives in the age of science, and what needs to be done to tame the pseudoscientific beast.

This is not an idle problem. In the last decade big strides have been made in understanding the psychosociology of popular creationism — basically, it flourishes only in seriously dysfunctional societies, and the one sure why to suppress the errant belief is to run countries well enough that the religion creationism depends upon withers to minority status, dragging creationism down with it.

In other words better societies result in mass acceptance of evolution. Yet getting the word out is proving disturbingly difficult. So the chatty pet theories abut why creationism is a problem and what to do about it continue to dominant the national conversation, and pro-creationist opinion remains rock steady (although those who favor evolution without a God is rising along with the general increase of nonbelievers).

It's not just evolution. A classic example of conversational thinking by a scientist causing trouble was Linus Pauling's obsession with vitamin C. Many ordinary citizens are skeptical of scientists in general. When researchers offer up poorly sustained opinions on matters outside their firm knowledge base it does not help the general situation.

So what can be done? In principle it is simple enough. Scientists should be scientists. We should know better than to cough up committed but dubious opinion on subjects outside our expertise. This does not mean a given scientist has to limit their observations solely to their official field of research. Say a scientist is also a self-taught authority on baseball. By all means ardently discuss that subject the way Stephen Gould used to.

I have long had an intense interest in the myths of World War II, and can offer an excellent discourse on why the atom bombing of Hiroshima and Nagasaki had pretty much nothing to do with ending the war in case you are interested (it was the Soviet attack on Japan that forced Hirohito to surrender to save his war criminal's neck and keep Japan from being split into occupation zones like Germany and Korea). But if a scientist finds him or herself being asked about something they do not know a lot about either decline to opine, or qualify the observations by stating that the opinion is tentative and nonexpert.

In practical terms the problem is, of course, that scientists are human beings like everyone else. So I am not holding my breath waiting for us to achieve a level of factual discourse that will spread enlightenment to the masses. It's too bad but very human. I have tried to cut down on throwing out idle commentary without qualifying its questionable reality, while being ardent about my statements only when I know I can back them up. Me thinks I am fairly successful in this endeavor, and it does seem to keep me out of trouble.

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?

When I first took up the piano, merely hitting each note required my concentrated attention. With practice, however, I began to work in phrases and chords. Eventually I was able to produce much better music with much less conscious effort.

Evidently, something powerful had happened in my brain.

That sort of experience is very common, of course. Something similar occurs whenever we learn a new language, master a new game, or get comfortable in a new environment. It seems very likely that a common mechanism is involvedf. I think it's possible to identify, in broad terms, what that mechanism is: We create hidden layers.

The scientific concept of a hidden layer arose from the study of neural networks. Here a little picture is worth a thousand words:

In this picture, the flow of information runs from top to bottom. Sensory neurons — the eyeballs at the top — take input from the external world and encode it into a convenient form (which is typically electrical pulse trains for biological neurons, and numerical data for the computer "neurons" of artificial neural networks). They distribute this encoded information to other neurons, in the next layer below. Effector neurons — the stars at the bottom — send their signals to output devices (which are typically muscles for biological neurons, and computer terminals for artificial neurons). In between are neurons that neither see nor act upon the outside world directly. These inter-neurons communicate only with other neurons. They are the hidden layers.

The earliest artificial neural networks lacked hidden layers. Their output was, therefore, a relatively simple function of their input. Those two-layer, input-output "perceptrons" had crippling limitations. For example, there is no way to design a perceptron that, faced with pictures of a few black circles on a white background, counts the number of circles. It took until the 1980s, decades after the pioneering work, for people to realize that including even one or two hidden layers could vastly enhance the capabilities of their neural networks. Nowadays such multilayer networks are used, for example, to distill patterns from the explosions of particles that emerge from high-energy collisions at the Large Hadron Collider. They do it much faster and more reliably than humans possibly could.

David Hubel and Torstein Wiesel were awarded the 1981 Nobel Prize in physiology or medicine for figuring out what neurons in the visual cortex are doing. They showed that successive hidden layers first extract features of visual scenes that are likely to be meaningful (for example, sharp changes in brightness or color, indicating the boundaries of objects), and then assemble them into meaningful wholes (the underlying objects).

In every moment of our adult waking life, we translate raw patterns of photons impacting our retinas — photons arriving every which way from a jumble of unsorted sources, and projected onto a two-dimensional surface — into the orderly, three-dimensional visual world we experience. Because it involves no conscious effort, we tend to take that everyday miracle for granted. But when engineers tried to duplicate it, in robotic vision, they got a hard lesson in humility. Robotic vision remains today, by human standards, primitive. Hubel and Wiesel exhibited the architecture of Nature's solution. It is the architecture of hidden layers.

Hidden layers embody, in a concrete physical form, the fashionable but rather vague and abstract idea of emergence. Each hidden layer neuron has a template. It becomes activated, and sends signals of its own to the next layer, precisely when the pattern of information it's receiving from the preceding layer matches (within some tolerance) that template. But this is just to say, in precision-enabling jargon, that the neuron defines, and thus creates, a new emergent concept.

In thinking about hidden layers, it's important to distinguish between the routine efficiency and power of a good network, once that network has been set up, and the difficult issue of how to set it up in the first place. That difference is reflected in the difference between playing the piano, say, or riding a bicycle, or swimming, once you've learned (easy), and learning to do those things in the first place (hard). Understanding exactly how new hidden layers get laid down in neural circuitry is a great unsolved problem of science. I'm tempted to say it's the greatest.

Liberated from its origin in neural networks, the concept of hidden layers becomes a versatile metaphor, with genuine explanatory power. For example, in my own work in physics I've noticed many times the impact of inventing names for things. When Murray Gell-Mann invented "quarks", he was giving a name to a paradoxical pattern of facts. Once that pattern was recognized, physicists faced the challenge of refining it into something mathematically precise and consistent; but identifying the problem was the crucial step toward solving it! Similar, when I invented "anyons" I knew I had put my finger on a coherent set of ideas, but I hardly anticipated how wonderfully those ideas would evolve and be embodied in reality. In cases like this, names create new nodes in hidden layers of thought.

I'm convinced that the general concept of hidden layers captures deep aspects of the way minds — whether human, animal, or alien; past, present, or future — do their work. Minds mobilize useful concepts by embodying them in a specific way, namely as features recognized by hidden layers. And isn't it pretty that "hidden layers" is itself a most useful concept, worthy to be included in hidden layers everywhere?

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 exponential explosion of information and our ability to access it make our ability to validate its truthfulness not only more important but also more difficult. Information has importance in proportion to its relevance and meaning. Its ultimate value is how we use it to make decisions and put it in a framework of knowledge

Our perceptions are crucial in appreciating truth. However, we do not apprehend objective reality. Perception is based on recognition and interpretation of sensory stimuli derived from patterns of electrical impulses. From this data, the brain creates analogues and models that simulate tangible, concrete objects in the real world. Experience, though, colors and influences all of our perceptions by anticipating and predicting everything we encounter and meet. It is the reason Goethe advised that "one must ask children and birds how cherries and strawberries taste." This preferential set of intuitions, feelings, and ideas, less poetically characterized by the term bias, poses a challenge to the ability to weigh evidence accurately to arrive at truth. Bias is the non-dispassionate thumb which experience puts on the scale.

Our brains evolved having to make the right bet with limited information. Fortune, it has been said, favors the prepared mind. Bias in the form of expectation, inclination and anticipatory hunches helped load the dice in our favor and for that reason is hardwired into our thinking.

Bias is an intuition, sensitivity, receptiveness which acts as a lens or filter on all our perceptions. "If the doors of perception were cleansed," Blake said, "everything would appear to man as it is, infinite." But without our biases to focus our attention, we would be lost in that endless and limitless expanse. We have at our disposal an immeasurable assortment of biases and their combination in each of us is as unique as a fingerprint. These biases mediate between our intellect and emotions to help congeal perception into opinion, judgment, category, metaphor, analogy, theory, and ideology which frame how we see the world.

Bias is tentative. Bias adjusts as the facts change. Bias is a provisional hypothesis. Bias is normal.

Although bias is normal in the sense that it is a product of how we select and perceive information, its influence on our thinking cannot be ignored. Medical science has long been aware of the inherent bias, which occurs in collecting and analyzing clinical data. The double blind, randomized controlled study, the gold standard of clinical design, was developed in an attempt to nullify its influence.

We live in the world, however, not in a laboratory and bias cannot be eliminated. Bias critically utilized sharpens the collection of data by knowing when to look, where to look, and how to look. It is fundamental to both inductive and deductive reasoning. Darwin didn't collect his data to formulate the theory of evolution randomly or disinterestedly. Bias is the nose for the story.

Truth needs continually to be validated against all evidence, which challenges it fairly and honestly. Science with its formal methodology of experimentation and reproducibility of its findings is available to anyone who plays by its rules. No ideology, religion, culture or civilization is awarded special privileges or rights. The truth, which survives this ordeal, has another burden to bear. Like the words in a multi-dimensional crossword puzzle, it has to fit together with all the other pieces already in place. The better and more elaborate the fit, the more certain the truth. Science permits no exceptions. It is inexorably revisionary, learning from its mistakes, erasing and rewriting, even their most sacred texts, until the puzzle is complete.

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.

People like to think of technologies and media as neutral and that only their use or content determines their impact. Guns don't kill people, after all, people kill people. But guns are much more biased toward killing people than, say, pillows — even though many a pillow has been utilized to smother an aging relative or adulterous spouse.

Our widespread inability to recognize or even acknowledge the biases of the technologies we use renders us incapable of gaining any real agency through them. We accept our iPads, Facebook accounts and automobiles at face value — as pre-existing conditions — rather than tools with embedded biases.

Marshall McLuhan exhorted us to recognize that our media have impacts on us beyond whatever content is being transmitted through them. And while his message was itself garbled by the media through which he expressed it (the medium is the what?) it is true enough to be generalized to all technology. We are free to use any car we like to get to work — gasoline, diesel, electric, or hydrogen — and this sense of choice blinds us to the fundamental bias of the automobile towards distance, commuting, suburbs, and energy consumption.

Likewise, soft technologies from central currency to psychotherapy are biased in their construction as much as their implementation. No matter how we spend US dollars, we are nonetheless fortifying banking and the centralization of capital. Put a psychotherapist on his own couch and a patient in the chair, and the therapist will begin to exhibit treatable pathologies. It's set up that way, just as Facebook is set up to make us think of ourselves in terms of our "likes" and an iPad is set up to make us start paying for media and stop producing it ourselves.

If the concept that technologies have biases were to become common knowledge, we would put ourselves in a position to implement them consciously and purposefully. If we don't bring this concept into general awareness, our technologies and their effects will continue to threaten and confound us.

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.

One very old and pervasive habit of thought is to imagine that the true answer to whatever question we are wondering about lies out there in some eternal domain of "timeless truths." The aim of re-search is then to "discover" the answer or solution in that already existing timeless domain. For example, physicists often speak as if the final theory of everything already exists in a vast timeless Platonic space of mathematical objects. This is thinking outside of time.

Scientists are thinking in time when we conceive of our task as the invention of genuinely novel ideas to describe newly discovered phenomena, and novel mathematical structures to express them. If we think outside of time, we believe these ideas somehow "existed" before we invented them. If we think in time we see no reason to presume that.

The contrast between thinking in time and thinking outside of time can be seen in many domains of human thought and action. We are thinking outside of time when, faced with a technological or social problem to solve, we assume the possible approaches are already determined by a set of absolute pre-existing categories. We are thinking in time when we understand that progress in technology, society and science happens by the invention of genuinely novel ideas, strategies, and novel forms of social organization.

The idea that truth is timeless and resides outside the universe was the essence of Plato's philosophy, exemplified in the parable of the slave boy that was meant to argue that discovery is merely remembering. This is reflected in the philosophy of mathematics called Platonism, which is the belief that there are two ways of existing. Regular physical things exist in the universe and are subject to time and change, while mathematical objects exist in a timeless realm. The division of the world into a time-drenched Earthly realm of life, death, change and decay, surrounded by a heavenly sphere of perfect eternal truth, framed both ancient science and Christian religion.

If we imagine that the task of physics is the discovery of a timeless mathematical object that is isomorphic to the history of the world, then we imagine that the truth to the universe lies outside the universe. This is such a familiar habit of thought that we fail to see its absurdity: if the universe is all that exists then how can something exist outside of it for it to be isomorphic to?

On the other hand, if we take the reality of time as evident, then there can be no mathematical object that is perfectly isomorphic to the world, because one property of the real world that is not shared by any mathematical object is that it is always some moment. Indeed, as Charles Sanders Pierce first observed, the hypothesis that the laws of physics evolved through the history of the world is necessary if we are to have a rational understanding of why one particular set of laws hold, rather than others.

Thinking outside of time often implies the existence of an imagined realm outside the universe where the truth lies. This is a religious idea, because it means that explanations and justifications ultimately refer to something outside of the world we experience ourselves to be a part of. If we insist there is nothing outside the universe, not even abstract ideas or mathematical objects, we are forced to find the causes of phenomena entirely within our universe. So thinking in time is also thinking within the one universe of phenomena our observations show us to inhabit.

Among contemporary cosmologists and physicists, proponents of eternal inflation and timeless quantum cosmology are thinking outside of time. Proponents of evolutionary and cyclic cosmological scenarios are thinking in time. If you think in time you worry about time ending at space-time singularities. If you think outside of time this is an ignorable problem because you believe reality is the whole history of the world at once.

Darwinian evolutionary biology is the prototype for thinking in time because at its heart is the realization that natural processes developing in time can lead to the creation of genuinely novel structures. Even novel laws can emerge when the structures to which they apply come to exist. Evolutionary dynamics has no need of abstract and vast spaces like all the possible viable animals, DNA sequences, sets of proteins, or biological laws. Exaptations are too unpredictable and too dependent on the whole suite of living creatures to be analyzed and coded into properties of DNA sequences. Better, as Stuart Kauffman proposes, to think of evolutionary dynamics as the exploration, in time, by the biosphere, of the adjacent possible.

The same goes for the evolution of technologies, economies and societies. The poverty of the conception that economic markets tend to unique equilibria, independent of their histories, shows the danger of thinking outside of time. Meanwhile the path dependence that Brian Arthur and others show is necessary to understand real markets illustrates the kind of insights that are gotten by thinking in time.

Thinking in time is not relativism, it is a form of relationalism. Truth can be both time bound and objective, when it is about objects that only exist once they are invented, by evolution or human thought.

When we think in time we recognize the human capacity to invent genuinely novel constructions and solutions to problems. When we think about the organizations and societies we live and work in outside of time we unquestioningly accept their strictures, and seek to manipulate the levers of bureaucracy as if they had an absolute reason to be there. When we think about organizations in time we recognize that every feature of them is a result of their history and everything about them is negotiable and subject to improvement by the invention of novel ways of doing things.

It's always worth to take a few cues from mid-20th-century avant-garde. So when it comes to improving your cognitive toolkit Free Jazz is perfect. It is a highly evolved new take on an art that has (at least in the West) been framed by a strict set of twelve notes played in accurate factions of bars. It is also the pinnacle of a genre that had begun with the Blues just a half century before Ornette Coleman assembled his infamous double quartet in the A&R Studio in New York City one December day in 1960. In science terms that would mean an evolutionary leap from elementary school math to game theory and fuzzy logic in a mere fifty years.

If you really want to appreciate the mental prowess of Free Jazz players and composers you should start just one step behind. A half a year before Ornette Coleman's Free Jazz session let loose the improvisational genius of eight of the best musicians of their times, John Coltrane recorded what is still considered the most sophisticated Jazz solo ever — his tour de force through the rapid chord progressions of his composition "Giant Steps".

The film student Daniel Cohen has recently animated the notation for Coltrane's solo in a YouTube video. You don't have to be able to read music to grasp the intellectual firepower of Coltrane. After the deceivingly simple main theme the notes start to race up and down the five lines of the stave in dizzying speeds and patterns. If you also take into consideration that Coltrane used to record unrehearsed music to keep it fresh, you know that he was endowed with a cognitive toolkit way beyond normal.

Now take these almost 4:43 minutes, multiply Coltrane's firepower by eight, stretch it into 37 minutes and deduct all traditional musical structures like chord progressions or time. The session that gave the genre it's name in the first place foreshadowed not just the radical freedom the album's title implied. It was a precursor to a form of communication that has left linear conventions and entered the realm of multiple parallel interactions.

It is admittedly still hard to listen to the album "Free Jazz: A Collective Improvisation by the Ornette Coleman Double Quartet". It is equally taxing to listen to recordings of Cecil Taylor, Pharoah Sanders, Sun Ra, Anthony Braxton or Gunter Hampel. It has always been easier to understand the communication processes of this music in a live setting. One thing is a given — it is never anarchy, never was meant to be.

If you're able to play music and you manage to get yourself invited to a Free Jazz session, there is an incredible moment, when all musicians find what is considered "The Pulse". It is a collective climax of creativity and communication that can leap to the audience and create an electrifying experience. It's hard to describe, but might be comparable to the moment when a surfer finds the point when the catalyst of a surfboard bring together the motor skills of his body and the forces of the swell of an ocean start in these few seconds of synergy on top of a wave. It is a fusion of musical elements though that defies common musical theory.

Of course there is a lot of Free Jazz that merely confirms prejudice. Or as the vibraphonist and composer Gunter Hampel phrased it: "At one point it was just about being the loudest on stage." But all the musicians mentioned above have found new forms and structures, Ornette Coleman's music theory called Harmolodics being just one of them. In the perceived cacophony of their music there is a multilayered clarity to discover that can serve as a model for a cognitive toolkit for the 21st century. The ability to find cognitive, intellectual and communication skills that work in parallel contexts rather than linear forms will be crucial. Just as Free Jazz abandoned harmonic structures to find new forms in polyrhythmic settings, one might just have to enable himself to work beyond proven cognitive patterns.

Mistakes, errors, false starts — accept them all. The basis of creativity.

My reference point (as a playwright, not a scientist) was Keat's notion of negative capability (from his letters). Being able to exist with lucidity and calm amidst uncertainty, mystery and doubt, without "irritable (and always premature) reaching out" after fact and reason.

This toolkit notion of negative capability is a profound therapy for all manner of ills — intellectual, psychological, spiritual and political. I reflect it (amplify it) with Emerson's notion that "Art (any intellectual activity?) is (best thought of as but) the path of the creator to his work."

Bumpy, twisting roads. (New York City is about to repave my cobblestone street with smooth asphalt. Evil bureaucrats and tunnel-visioned 'scientists" — fast cars and more tacky up-scale stores in Soho.)

Wow! I'll bet my contribution is shorter than anyone else's. Is this my inadequacy or an important toolkit item heretofore overlooked?

The notion of gedankenexperiment, or thought experiment, has been integral to theoretical physics' toolkit ever since the discipline came into existence. It involves setting up an imagined piece of apparatus and then running a simple experiment with it in your mind for the purpose of proving or disproving a hypothesis. In many cases agedankenexperiment is the only approach. An actual experiment to examine retrieval of information falling into a black hole cannot be carried out.

The notion was particularly important during the development of quantum mechanics when legendary gedankenexperiments were conducted by the likes of Bohr and Einstein to test such novel ideas as the Uncertainty Principle and wave-particle duality. Examples, like that of Schrodinger's Cat, have even come into the popular lexicon. Is the cat simultaneously dead and alive? Others, particularly the classic double slit through which a particle/wave passes, were part of the first attempt to understand quantum mechanics and have remained as tools for understanding its meaning.

However, the subject need not be an esoteric one for agedankenexperiment  to be fruitful. My own favorite is Galileo's proof that, contrary to Aristotle's view, more and less massive objects fall in a vacuum with the same acceleration. One might think that a real experiment needs to be conducted to test the hypothesis. But Galileo simply said: consider a large and a small stone tied together by a very light string. If Aristotle was right, he large stone should drag the smaller one and the smaller one retard the larger one if they fell at different rates. However, if you let the string length approach zero, you have a single object with a mass equal to the sum of their masses and hence it should fall at a higher rate than either. This is nonsensical. The conclusion is that all objects fall in vacuum at the same rate.

Consciously or unconsciously we carry out gedankenexperiments of one sort or another in our everyday life and are even trained to do perform them in a variety of disciplines, but I do think it would be useful to have a greater awareness of how they are conducted and how they could be positively applied. We could ask, when confronted with a puzzling situation, how can I set up a gedankenexperiment to resolve the issue? Perhaps our financial, political and military experts would benefit from following such a tactic and arrive at happier outcomes.

Conceptual Metaphor is at the center of a complex theory of how the brain gives rise to thought and language, and how cognition is embodied. All concepts are physical brain circuits deriving their meaning via neural cascades that terminate in linkage to the body. That is how embodied cognition arises.

Primary metaphors are brain mappings linking disparate brain regions, each tied to the body in a different way. For example, More Is Up (as in "prices rose") links a region coordinating quantity to another coordinating verticality. The neural mappings are directional, linking frame structures in each region. The directionality is determined by First-Spike-Dependent Plasticity. Primary metaphors are learned automatically and unconsciously by the hundreds prior to metaphoric language, just by living in the world and having disparate brain regions activated together when two experiences repeatedly co-occur.

Complex conceptual metaphors arise via neural bindings, both across metaphors and from a given metaphor to a conceptual frame circuit. Metaphorical reasoning arises when source domain inference structures are used for target domain reasoning via neural mappings. Linguistic metaphors occur when words for source domain concepts are used for target domain concepts via neural metaphoric mappings.

Because conceptual metaphors unconsciously structure the brain's conceptual system, much of normal everyday thought is metaphoric, with different conceptual metaphors used to think with on different occasions or by different people.

A central consequence is the huge range of concepts that use metaphor cannot be defined relative to the outside world, but are instead embodied via interactions of the body and brain with the world.

There are consequences in virtually every area of life. Marriage, for example, is understood in many ways, as a journey, a partnership, a means for grown, a refuge, a bond, a joining together, and so on. What counts as a difficulty in the marriage is defined by the metaphor used. Since it is rare for spouses to have the same metaphors for their marriage, and since the metaphors are fixed in the brain but unconscious, it is not surprising that so many marriages encounter difficulties.

In politics, conservatives and progressives have ideologies defined by different metaphors. Various concepts of morality around the world are constituted by different metaphors. These results show the inadequacy of experimental approaches to morality in social psychology (e.g, Haidt's moral foundations theory) which ignore both how conceptual metaphor constitutes moral concepts and why those metaphors arise naturally in cultures around the world.

Even mathematical concepts are understood via metaphor, depending on the branch of mathematics. Emotions are conceptualized via metaphors that are tied to the physiology of emotion. In set theory, numbers are sets of a certain structure.

On the number line, numbers are points on a line. "Real" numbers are defined via the metaphor that infinity is a thing; an infinite decimal like pi goes on forever, yet it is a single entity — an infinite thing.

Though conceptual metaphors have been researched extensively in the fields of cognitive linguistics and neural computation for decades, experimental psychologists have been experimentally confirming their existence by showing that, as circuitry physically in the brain they can influence behavior in the laboratory. The metaphors guide the experimenters, showing them what to look for. Confirming the conceptual metaphor that The Future Is Ahead; The Past is Behind, experimenters found that subjects thinking about the future lean slightly forward, while those thinking about the past lean slightly backwards. Subjects asked to do immoral acts in experiments tended to wash or wipe their hands afterwards, confirming the conceptual metaphor Morality Is Purity. Subjects moving marbles upward tended to tell happy stories, while those moving marbles downward tended to tell sad stories, confirming Happy Is Up; Sad is Down. Similar results are coming in by the dozens. The new experimental results on embodied cognition are mostly in the realm of conceptual metaphor.

Perhaps most remarkable, there appear to be brain structures that we are born with that provide pathways ready for metaphor circuitry. Edward Hubbard has observed that critical brain regions coordinating space and time measurement are adjacent in the brain, making it easy for the universal metaphors for understanding space in terms of time to develop (as in "Christmas is coming" or "We're coming up on Christmas.") Mirror neuron pathways linking brain regions coordinating vision and hand actions provide a natural pathway for the conceptual metaphor that Seeing Is Touching (as in "Their eyes met").

Conceptual metaphors are natural and inevitable. They begin to arise in childhood just by living in the everyday world. For example, a common conceptual metaphor is Events with Causal Effects Are Actions by a Person. That is why the wind blows, why storms can be vicious, and why there is religion, in which the person causing those effects is called a god, or God if there is only one. The most common metaphors for God in the Western traditions is that God is a father, or a person with father-like properties — a creator, lawgiver, judge, punisher, nurturer, shepherd, and so on — and that God is The Infinite: the all-knowing, all-powerful, all-good, and first cause. These metaphors are not going to go away. The question is whether they will continue to be taken literally.

Those who believe, and promote the idea, that reason is not metaphorical —that mathematics is literal and structures the world independently of human minds —are ignoring conceptual metaphor and encouraging false literalness, which can be harmful.

The science is clear. Metaphorical thought is normal. That should be widely recognized.

Every time you think of paying moral debts, or getting bogged down on a project, or losing time, or being at a crossroads in a relationship, you are unconsciously activating a conceptual metaphor circuit in your brain, reasoning using it, and quite possibly making decisions and living your life on the basis of your metaphors. And that's just normal. There's no way around it!

Metaphorical reason serves us well in everyday life. But it can do harm if you are unaware of it.

1. A university needs to replace its aging hospital. Cost estimates indicate that it would be equally expensive to remodel the old hospital vs. to demolish it and build a new one from scratch. The main argument offered by the proponents of the former is that the original hospital had been very expensive to build and it would be wasteful to simply demolish it. The main argument by the proponents of a new hospital is that a new hospital would inevitably be more modern than a remodeled one. Which seems wiser to you — remodel or build a new hospital?

2. David L., a high school senior, is choosing between two colleges, equal in prestige, cost and distance from home. David has friends at both colleges. Those at College A like it from both intellectual and personal standpoints. Those at College B are generally disappointed on both grounds. But David visits each college for a day and his impressions are quite different from those of his friends. He meets several students at College A who seem neither particularly interesting nor particularly pleasant, and a couple of professors give him the brushoff. He meets several bright and pleasant students at College B and two different professors take a personal interest in him. Which college do you think David should go to?

3. Which of the cards below should you turn over to answer to determine whether the following rule has been violated or not? "If there is a vowel on the front of the card then there is an odd number on the back."

"If there is a vowel on the front of the card then there is an odd number on the back."

Some considerations about each of these questions

Question 1: If you said that the university should remodel on the grounds that it had been expensive to build the old hospital you have fallen into the "sunk cost trap" SHA identified by economists. The money spent on the hospital is irrelevant — it's sunk — and has no bearing on the present choice. Amos Tversky and Daniel Kahneman pointed out that people's ability to avoid such traps might be helped by a couple of thought experiments like the following:

"Imagine that you have two tickets to tonight's NBA game in your city and that the arena is 40 miles away. But it's begun to snow and you've found out that your team's star has been injured and won't be playing. Should you go or just throw away the money and skip it?" To answer that question as an economist would, ask yourself the following question: Suppose you didn't have tickets to the game and a friend were to call you up and say that he has two tickets to tonight's game which he can't use and asks if you would like to have them. If the answer is "you've got to be kidding, it's snowing and the star isn't playing," then the answer is you shouldn't go. That answer shows you that the fact that you paid good money for the tickets you have is irrelevant — their cost is sunk and can't be retrieved by doing something you don't want to do anyway. Avoidance of sunk cost traps is a religion for economists, but I find that a single college course in economics actually does little to make people aware of the sunk cost trap. It turns out that exposure to a few basketball-type anecdotes does a lot.

Question 2: If you said that "David is not his friends; he should go to the place he likes," then the SHA of "the law of large numbers" has not been sufficiently salient to you. David has one day's worth of experiences at each; his friends have hundreds. Unless David thinks his friends have kinky tastes he should ignore his own impressions and go to College A. A single college course in statistics increases the likelihood of invoking LLN. Several courses in statistics make LLN considerations almost inevitable.

Question 3: If you said anything other than "turn over the U and turn over the 8," psychologists Wason and Johnson-Laird have shown that you would be in the company of 90% of Oxford students. Unfortunately, you — and they — are wrong. The SHA of the logic of the conditional has not guided your answer. "If P then Q is satisfied by showing that the P is associated with a Q and the not-Q is not associated with a P. A course in logic actually does nothing to make people better able to answer questions such as number 3. Indeed, a Ph.D. in philosophy does nothing to make people better able to apply the logic of the conditional to simple problems like Question 3 or meatier problems of the kind one encounters in everyday life.

Some SHAs apparently are "graceful" in that they are easily inserted into the cognitive toolbox. Others appear to be clunky and don't readily fit. If educators want to improve people's ability to think, they need to know which SHAs are graceful and teachable and which are clunky and hard to teach. An assumption of educators for centuries has been that formal logic improves thinking skills — meaning that it makes people more intelligent in their everyday lives. But this belief may be mistaken. (Bertrand Russell said, almost surely correctly, that the syllogisms studied by the monks of medieval Europe were as sterile as they were.) But it seems likely that many crucially important SHAs, undoubtedly including some which have been proposed by this year's Edge contributors, are readily taught. Few questions are more important for educators to study than to find out which SHAs are teachable and how they can be taught most easily.

The media cast about for the proximate causes of life's windfalls and disasters. The public demands blocks against the bad and pipelines to the good. Legislators propose new regulations, fruitlessly dousing last year's fires, forever betting on yesterday's winning horses.

A little-known truth: Every aspect of the world is fundamentally unpredictable. Computer scientists have long since proved this.

How so? To predict an event is to know a shortcut for foreseeing the outcome in advance. A simple counting argument shows there aren't enough shortcuts to go around. Therefore most processes aren't predictable. A deeper argument plays on the fact that, if you could predict your actions, you could deliberately violate your predictions which means the predictions were wrong after all.

We often suppose that unpredictability is caused by random inputs from higher spirits or from low-down quantum foam. But chaos theory and computer science tell us that non-random systems produce surprises on their own. The unexpected tornado, the cartoon safe that lands on Uncle George, the winning pull on a slot machine odd things pop out of a computation. The world can simultaneously be deterministic and unpredictable.

In the physical world, the only way to learn tomorrow's weather in detail is to wait twenty-four hours and see even if nothing is random at all. The universe is computing tomorrow's weather as rapidly and as efficiently as possible any smaller model is inaccurate, and the smallest error is amplified into large effects.

At a personal level, even if the world is as deterministic as a computer program, you still can't predict what you're going to do. This is because your prediction method would involve a mental simulation of you that produces its results slower than you. You can't think faster than you think. You can't stand on your own shoulders.

It's a waste to chase the pipedream of a magical tiny theory that allows us to make quick and detailed calculations about the future. We can't predict and we can't control. To accept this can be a source of liberation and inner peace. We're part of the unfolding world, surfing the chaotic waves.

Imagine you need to find the midpoint of a stick. You can measure its length, using a ruler (or making a ruler, using any available increment) and digitally compute the midpoint. Or, you can use a piece of string as an analog computer, matching the length of the stick to the string, and then finding the middle of the string by doubling it back upon itself. This will correspond, without any loss of accuracy due to rounding off to the nearest increment, to the midpoint of the stick. If you are willing to assume that mass scales linearly with length, you can use the stick itself as an analog computer, finding its midpoint by balancing it against the Earth's gravitational field.

There is no precise distinction between analog and digital computing, but, in general, digital computing deals with integers, binary sequences, and time that is idealized into discrete increments, while analog computing deals with real numbers and continuous variables, including time as it appears to exist in the real world. The past sixty years have brought such advances in digital computing that it may seem anachronistic to view analog computing as an important scientific concept, but, more than ever, it is.

Analog computing, once believed to be as extinct as the differential analyzer, has returned. Not for performing arithmetic — a task at which even a pocket calculator outperforms an analog computer — but for problems at which analog computing can do a better job not only of computing the answer, but of asking the questions and communicating the results. Who is friends with whom? For a small high school, you could construct a database to keep track of this, and update it every night to keep track of changes to the lists. If you want to answer this question, updated in real time, for 500 million people, your only hope is to build an analog computer. Sure, you may use digital components, but at a certain point the analog computing being performed by the system far exceeds the complexity of the digital code with which it is built. That's the genius that powers Facebook and its ilk. Your model of the social graph becomes the social graph, and updates itself.

In the age of all things digital, "Web 2.0" is our code word for the analog increasingly supervening upon the digital — reversing how digital logic was embedded in analog components, sixty years ago. The fastest-growing computers of 2010 — search engines and social networks — are analog computers in a big, new, and important way. Instead of meaningful information being encoded as unambiguous (and fault-intolerant) digital sequences referenced by precise numerical addressing, meaningful information is increasingly being encoded (and operated upon) as continuous (and noise-tolerant) variables such as frequencies (of connection or occurrence) and the topology of what connects where, with location being increasingly defined by fault-tolerant template rather than by unforgiving numerical address.

Complex networks — of molecules, people, or ideas — constitute their own simplest behavioral descriptions. This behavior can be more easily and accurately approximated by continuous, analog networks than it can be defined by digital, algorithmic codes. These analog networks may be composed of digital processors, but it is in the analog domain that the interesting computation is being performed.

Analog is back, and here to stay.

Some scientific concepts have been so ruined by our education system that it is necessary to explain about the ones that everyone thinks they know about when they really don't.

We learn about experimentation in school. What we learn is that scientists conduct experiments and if we copy exactly what they did in our high school labs we will get the results they got. We learn about the experiments that scientists do, usually about about the physical and chemical properties of things and we learn that they report their results in scientific journals. So, in effect we learn that experimentation is boring, is something done by scientists and has nothing to do with our daily lives.

And, this is a problem. Experimentation is something done by everyone all the time. Babies experiment with what might be good to put in their mouths. Toddlers experiment with various behaviors to see what they can get away with. Teenagers experiment with sex, drugs, and rock and roll. But because people don't really see these things as experiments nor as ways of collecting evidence in support or refutation of hypotheses, they don't learn to think about experimentation as something they constantly do and thus will need to learn to do better.

Every time we take a prescription drug we are conducting an experiment. But, we don't carefully record the results after each dose, and we don't run controls, and we mix up the variables by not changing only one behavior at a time, so that when we suffer from side effects we can't figure out what might have been the true cause. We do the same thing with personal relationships. When they go wrong, we can't figure out why because the conditions are different in each one.

Now, while it is difficult if not impossible to conduct controlled experiments in most aspects of our own lives, it is possible to come to understand that we are indeed conducting an experiment when we take a new job, or try a new tactic in a game we are playing, or when we pick a school to attend, or when we try and figure out how someone is feeling, or when we wonder why we ourselves feel the way we do.

Every aspect of life is an experiment that can be better understood if it is perceived in that way. But because we don't recognize this we fail to understand that we need to reason logically from evidence we gather, and that we need to carefully consider the conditions under which our experiments have been conducted, and that we need to decide when and how we might run the experiment again with better results.

In other words, the scientific activity that surrounds experimentation is about thinking clearly in the face of evidence obtained as the result of an experiment. But people who don't see their actions as experiments, and those who don't know how to reason carefully from data, will continue to learn less well from their own experiences than those who do.

Since most of us have learned the word "experiment" in the context of a boring ninth grade science class, most people have long since learned to discount science and experimentation as being relevant to their lives.

If school taught basic cognitive concepts such as experimentation in the context of everyday experience, and taught people how to carefully conduct experiments in their own lives instead of concentrating on using algebra as a way of teaching people how to reason, then people would be much more effective at thinking about politics, child raising, personal relationships, business, and every other aspect of daily life.

Understanding the concept of haecceity would improve everybody's cognitive toolkit because it succinctly captures most people's intuitions about authenticity that are increasingly threatened by the development of new technologies. Cloning, genetic modification and even digital reproduction are some examples of new innovations that alarm many members of the public because they appear to violate a belief in the integrity of objects

Haecceity is originally a metaphysical concept that is both totally obscure and yet very familiar to all of us. It is the psychological attribution of an unobservable property to an object that makes it unique among identical copies. All objects may be categorized into groups on the basis of some shared property but an object within a category is unique by virtual of its haecceity. It is haecceity that makes your wedding ring authentic and your spouse irreplaceable, even though such things could be copied exactly in a futuristic science fiction world where matter duplication had been solved.

Haecceity also explains why you can gradually replace every atom in an object so that it not longer contains any of the original material and yet psychologically, we consider it to be the same object. That transformation can be total but so long as it has been gradual, we consider it to be the same thing. It is haecceity that enables us to accept restoration of valuable works of art and antiquities as a continuous process of rejuvenation. Even when we discover that we replace most of the cellular structures of our bodies every couple of decades, haecceity enables us to consider the continuity of our own unique self.

Haecceity is an intellectually challenging concept attributable to the medieval Scottish philosopher, John Duns Scotus, who ironically is also the origin of the term for the intellectually challenged, "dunces." Duns Scotus coined haecceity to address the confusion in Greek metaphysics between the invisible property that defines the individual, as opposed to "quiddity" which is the unique property that defines the group.

Today, both haecceity and quiddity have been subsumed under the more recognizable term, "essentialism." Richard Dawkins has recently called essentialism, "the dead hand of Plato," because, as he points out, a intuitive belief in distinct identities is a major impediment to accepting the reality that all diverse life forms have a common biological ancestry. However drawing the distinction within essentialism is important. For example, it is probably intuitive quiddity that makes some people unhappy about genetic modification because they see this as a violation of integrity of the species as a group. On the other hand it is intuitive haecceity that forms our barrier to cloning, where the authenticity of the individual is compromised.

By reintroducing haecceity as a scientific concept, albeit one that captures a psychological construct, we can avoid the confusion over using the less constrained term of essentialism that is applied to hidden properties that define both the group and the individual identity. It also provides a term for that gut feeling that many of us have when the identity and integrity of objects we value are threatened and we can't find the word for describing our concerns.

I'm alone in my home, working in my study, when I hear the click of the front door, the sound of footsteps making their way toward me. Do I panic? That depends on what I — my attention instantaneously appropriated to the task and cogitating at high speed—infer as the best explanation for those sounds. My husband returning home, the house cleaners, a miscreant breaking and entering, the noises of our old building settling,  a supernatural manifestation? Additional details could make any one of these explanations, excepting the last, the best explanation for the circumstances. Why not the last? As Charles Sanders Peirce, who first drew attention to this type of reasoning, pointed out: "Facts cannot be explained by a hypothesis more extraordinary than these facts themselves; and of various hypotheses the least extraordinary must be adopted."

"Inference to the best explanation" is ubiquitously pursued, which doesn't mean that it is ubiquitously pursued well. The phrase, coined by the Princeton philosopher Gilbert Harmon as a substitute for Peirce's term "abduction," should be in everybody's toolkit, if only because it forces one to think about what makes for a good explanation. There is that judgmental phrase, the best, sitting out in the open, shamelessly invoking standards. Not all explanations are created equal; some are objectively better than others. And the phrasealso highlights another important fact. The best means the one that wins out over the alternatives, of which there are always many. Evidence calling for an explanation summons a great plurality (in fact an infinity) of possible explanations, the great mass of which can be eliminated on the grounds of violating Peirce's maxim. We decide among the remainder using such criteria as: which is the simpler, which does less violence to established beliefs, which is less ad hoc, which explains the most, which is the loveliest. There are times when these criteria clash with one another. Inference to the best explanation is certainly not as rule-bound as logical deduction, nor even as enumerative induction, which takes us from observed cases of all a's being b's to the probability that unobserved cases of a's are also b's. But inference to the best explanation also gets us a great deal more than either deduction or enumerative induction does.

It's inference to the best explanation that gives science the power to expand our ontology, giving us reasons to believe in things that we can't directly observe, from sub-atomic particles — or maybe strings — to the dark matter and dark energy of cosmology. It's inference to the best explanation that allows us to know something of what it's like to be other people on the basis of their behavior. I see the hand drawing too near the fire and then quickly pull away, tears starting in the eyes while an impolite word is uttered, and I know something of what that person is feeling. It's on the basis of inference to the best explanation that I can learn things from what authorities say and write, my inferring that the best explanation for their doing so is that they believe what they say or write. (Sometimes that's not the best explanation.) In fact, I'd argue that my right to believe in a world outside of my own solipsistic universe, confined to the awful narrowness of my own immediate experience, is based on inference to the best explanation. What best explains the vivacity and predictability of some of my representations of material bodies, and not others, if not the hypothesis of actual material bodies? Inference to the best explanation defeats mental-sapping skepticism.

Many of our most rancorous scientific debates — say, over string theory or  foundations of quantum mechanics — have been over which competing criteria for judging explanations the best ought to prevail. So, too, have debates that many of us have been having over scientific versus religious explanations. These debates could be sharpened by bringing to bear on them the rationality-steeped notion of inference to the best explanation, its invocation of the sorts of standards that make some explanations objectively better than others, beginning with Peirce's enjoiner that extraordinary hypotheses be ranked far away from the best.

In politics and society at large, important decision are all too often based on deeply held presuppositions,ideology or dogma — or, on the other hand, on headlong pragmatism without study of long-range consequences.

Therefore I suggest the adoption of Skeptical Empiricism, the kind that is exemplified by the carefully thought-out and tested research in science at its best. It differs from plain empiricism on the sort that characterized the writings of the scientist/philosopher Ernst Mach, who refused to believe in the existence of atoms because one could not "see" them.

To be sure, in politics and daily life, on some topics decisions have to be made very rapidly, on few or conflicting data. Yet, precisely for that reason it will be wise also to launch a more considerate program of skeptical empiricism on the same topic, if only to be better prepared for the consequences, intended or not, that followed from the quick decision.

One of the most widely-useful (but not widely-understood) scientific concepts is that of a possibility space. This is a way of thinking precisely about complex situations. Possibility spaces can be difficult to get your head around, but once you learn how to use them, they are a very powerful way to reason, because they allow you to sidestep thinking about causes and effects.

As an example of how a possibility space can help answer questions, I will use "the Monty Hall problem," which many people find confusing using our normal tools of thought. Here is the setup: A game-show host presents a guest with a choice of items hidden behind three curtains. Behind one is a valuable prize; behind the other two are disappointing duds. After the guest has made an initial choice, the host reveals what is behind one of the un-chosen curtains, showing that it would have been a dud. The guest is then offered the opportunity to change their mind. Should they change or stick with their original decision?

Plausible-sounding arguments can be made for different answers. For instance, one might argue that it does not matter whether the guest switches or not, since nothing has changed the probability that the original choice is correct. Such arguments can be very convincing, even when they are wrong. The possibility space approach, on the other hand, allows us skip reasoning about complex ideas like probabilities and what causes change. Instead, we use a kind of systematic bookkeeping that leads us directly to the answer. The trick is just to be careful to keep track of all of the possibilities. 

One of the best ways to generate all the possibilities is to find a set of independent pieces of information that tell you everything you could possibly need to know about what could happen. For example, in the case of the Monty Hall problem, it would be sufficient to know what choice the guests is going to make, whether the host will reveal the leftmost or rightmost dud, and where the prize is located. Knowing these three pieces of information would allow you to predict exactly what is going to happen. It is also important that these three pieces of information are completely independent, in the sense that knowing one of them tells you nothing about any of the others. The possibility space is constructed by creating every possible combination of these three unknowns.

In this case, the possibility space is three-dimensional, because there are three unknowns. Since there are three possible initial choices for the guest, two dud options for the host, and three possible locations for the prize, there are initially 3x2x3=18 possibilities in the space. (One might reasonably ask why we don't just call this a possibility table. In this simple case, we could. But, scientists generally work with possibility spaces that contain an infinity of possibilities in a multidimensional continuum, more like a kind of physical space space.) This particular possibility space starts out as three-dimensional, but once the guest makes their initial choice, twelve of the possibilities become impossible and it collapses to two dimensions.

Let's assume that the guest already knows what initial choice they are going to make. In that case they could model the situation as a two-dimensional possibility space, one representing the location of the prize, the other representing whether the host will reveal the rightmost or leftmost dud. In this case, the first dimension indicates which curtain hides the prize (1, 2 or 3), and the second represents the arbitrary choice of the host (left dud or right dud), so there are six points in the space, representing the six possibilities of reality. Another way to say this is that the guest can deduce that they may be living in one of six equally-possible worlds. By listing them all, they will see that in four of these six, it is to their advantage to switch from their initial choice. 

Example of a two-dimensional possibility space, when guest's initial Choice is 1

After the host makes his revelation, half of these possibilities become impossible, and the space collapses to three possibilities. It will still be true that in two out of three of these possible worlds it is to the guest's advantage to switch. (In fact, this was even true of the original three-dimensional possibility space, before the guest made their initial choice.)

This is a particularly simple example of a possibility space where it is practical to list all the possibilities in a table, but the concept is far more general. In fact one way of looking at quantum mechanics is that reality actually consists of a possibility space, with Schrödinger's equation assigning a probability to each possibility. This allows quantum mechanics to explain phenomena that are impossible to account for in terms of causes and effects. Even in normal life, possibility spaces give us a reliable way the solve problems when our normal methods of reasoning seem to give contradictory or paradoxical answers. As Sherlock Holmes would say,  "Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth." 

Einstein ranks extremely high not only among the all-time practitioners of science but also among the producers of aphorisms that place science in its real-world context. One of my favourites is "If we knew what we were doing, it wouldn't be called research." This disarming comment, like so many of the best quotes by experts in any field, embodies a subtle mix of sympathy and disdain for the difficulties that the great unwashed find in appreciating what those experts do.

One of the foremost challenges that face scientists today is to communicate the management of uncertainty. The public know that experts are, well, expert - that they know more than anyone else about the issue at hand. What is evidently far harder for most people to grasp is that "more than anyone else" does not mean "everything" - and especially that, given the possession of only partial knowledge, experts must also be expert at figuring out what is the best course of action. Moreover, those actions must be well judged whether in the lab, the newsroom or the policy-maker's office.

It must not, of course, be neglected that many experts are decidedly inexpert at communicating their work in lay terms. This remains a major issue largely because virtually all experts are called upon to engage in general-audience communication only very rarely, hence do not see it as a priority to gain such skills. Training and advice are available, often from university press offices, but even when experts take advantage of such opportunities it is generally too little and too late.

However, in my view that is a secondary issue. As a scientist with the luxury of communicating with the general public very frequently, I can report with confidence that experience only helps up to a point. A fundamental obstacle remains: that non-scientists harbour deep-seated instincts concerning the management of uncertainty in their everyday lives, which exist because they generally work, but which profoundly differ from the optimal strategy in science and technology. And of course it is technology that matters here, because technology is where the rubber hits the road - where science and the real world meet and must communicate effectively.

Examples of failure in this regard abound - so much so that they are hardly worthy of enumeration. Whether it be swine flu, bird flu, GM crops, stem cells: the public debate departs so starkly from the scientist's comfort zone that it is hard not to sympathise with the errors that scientists make, such as letting nuclear transfer be called "cloning", which end up holding critical research fields back for years.

One particular aspect of this problem stands out in its potential for public self-harm, however: risk-aversion. When uncertainty revolves around such areas as ethics (as with nuclear transfer) or economic policy (as with flu vaccination), the issues are potentially avoidable by appropriate forward planning. This is not the case when it comes to the public attitude to risk. The immense fall in uptake of vaccinations for major childhood diseases following a single, contentious study lining them to autism is a prime example. Another is the suspension of essentially all clinical trials of gene therapy for at least a year in response to the death of one person in a trial: a decision taken by regulatory bodies, yes, but one that was in line with public opinion.

These responses to the risk benefit ratio of cutting-edge technologies are examples of fear of the unknown - of an irrationally conservative prioritisation of the risks of change over the benefits, with unequivocally deleterious consequences in terms of quality and quantity of life in the future. Fear of the unknown is not remotely irrational in principle, when "fear of" is understood as a synonym for "caution about" - but it can be, and generally is, overdone. If the public could be brought to a greater understanding of how to evaluate the risks inherent in exploring future technology, and the merits of accepting some short-term risk in the interests of overwhelmingly greater expected long-term benefit, progress in all areas of technology — especially biomedial technology - would be greatly accelerated.

A zero-sum game is an interaction in which one party's gain equals the other party's loss — the sum of their gains and losses is zero. (More accurately, it is constant across all combinations of their courses of action.) Sports matches are quintessential examples of zero-sum games: winning isn't everything, it's the only thing, and nice guys finish last. A nonzero-sum game is an interaction in which some combinations of actions provide a net gain (positive-sum) or loss (negative sum) to the two of them. The trading of surpluses, as when herders and farmers exchange wool and milk for grain and fruit, is a quintessential example, as is the trading of favors, as when people take turns baby-sitting each others' children.

In a zero-sum game, a rational actor seeking the greatest gain for himself or herself will necessarily be seeking the maximum loss for the other actor. In a positive-sum game, a rational, self-interested actor may benefit the other guy with the same choice that benefits himself or herself. More colloquially, positive-sum games are called win-win situations, and are capture in the cliché "Everybody wins."

This family of concepts (zero-sum, nonzero-sum, positive-sum, negative-sum, constant-sum, and variable-sum games) was introduced by John von Neumann and Oskar Morgenstern when they invented the mathematical theory of games in 1944. The Google Books Ngram tool shows that the terms saw a steady increase in popularity beginning in the 1950s, and its colloquial relative "win-win" began a similar ascent in the 1970s.

[click to enlarge]

Once people are thrown together in an interaction, their choices don't determine whether they are in a zero- or nonzero-sum game; the game is a part of the world they live in. But people, by neglecting some of the options on the table, may perceive that they are in a zero-sum game when in fact they are in a nonzero-sum game. Moreover, they can change the world to make their interaction nonzero-sum. For these reasons, when people become consciously aware of the game-theoretic structure of their interaction (that is, whether it is positive-, negative-, or zero-sum), they can make choices that bring them valuable outcomes — like safety, harmony, and prosperity — without their having to become more virtuous, noble, or pure.

Some examples. Squabbling colleagues or relatives agree to swallow their pride, take their losses, or lump it to enjoy the resulting comity rather than absorbing the costs of continuous bickering in hopes of prevailing in a battle of wills. Two parties in a negotiation split the difference in their initial bargaining positions to "get to yes." A divorcing couple realizes that they can reframe their negotiations from each trying to get the better of the other while enriching their lawyers to trying to keep as much money for the two of them and out of the billable hours of Dewey, Cheatham, and Howe as possible. Populaces recognize that economic middlemen (particularly ethnic minorities who specialize in that niche such as Jews, Armenians, overseas Chinese, and expatriate Indians) are not social parasites whose prosperity comes at the expense of their hosts but positive-sum-game-creators who enrich everyone at once. Countries recognize that international trade doesn't benefit their trading partner to their own detriment but benefits them both, and turn away from beggar-thy-neighbor protectionism to open economies which (as classical economists noted) make everyone richer and which (as political scientists have recently shown) discourage war and genocide. Warring countries lay down their arms and split the peace dividend rather than pursuing Pyrrhic victories.

Granted, some human interactions really are zero-sum — competition for mates is a biologically salient example. And even in positive-sum games a party may pursue an individual advantage at the expense of joint welfare. But a full realization of the risks and costs of the game-theoretic structure of an interaction (particularly if it is repeated, so that the temptation to pursue an advantage in one round may be penalized when roles reverse in the next) can militate against various forms of short-sighted exploitation.

Has an increasing awareness of the zero- or nonzero-sumness of interactions in the decades since 1950 (whether referred to in those terms or not) actually led to increased peace and prosperity in the world? It's not implausible. International trade and membership in international organizations has soared in the decades that game-theoretic thinking has infiltrated popular discourse. And perhaps not coincidentally, the developed world has seen both spectacular economic growth and a historically unprecedented decline in several forms of institutionalized violence, such as war between great powers, war between wealthy states, genocides, and deadly ethnic riots. Since the 1990s these gifts have started to accrue to the developing world as well, in part because they have switched their foundational ideologies from ones that glorify zero-sum class and national struggle to ones that glorify positive-sum market cooperation. (All these claims can be documented from the literature in international studies.)

The enriching and pacifying effects of participation in positive-sum games long antedate the contemporary awareness of the concept. The biologists John Maynard Smith and Eörs Szathmáry have argued that an evolutionary dynamic which creates positive-sum games drove the major transitions in the history of life: the emergence of genes, chromosomes, bacteria, cells with nuclei, organisms, sexually reproducing organisms, and animal societies. In each transition, biological agents entered into larger wholes in which they specialized, exchanged benefits, and developed safeguards to prevent one from exploiting the rest to the detriment of the whole. The journalist Robert Wright sketched a similar arc in his book Nonzero and extended it to the deep history of human societies. An explicit recognition among literate people of the shorthand abstraction "positive-sum game" and its relatives may be extending a process in the world of human choices that has been operating in the natural world for billions of years.

My concept is important, useful, scientific and very appropriate for Edge, but it does not exist. It is: The Edge of the Circle.

We know that a circle has no edge, and we also know that, when you travel on a circle far enough to the right or to the left, you reach the same place. Today's world is gradually moving towards extremism in almost every area: Politics, law, religion, economics, education, ethics, you name it. This is probably due to the brevity of messages, the huge amounts of information flooding us, the time pressure to respond before you think and the electronic means (Twitter, text messages) which impose superficiality. Only extremist messages can be fully conveyed in one sentence.

In this world, it often appears that there are two corners of extremism: Atheism and religious fanaticism; Far right and far left in politics; Suffocating bureaucratic detailed regulatory rules or a complete laissez faire; No ethical restrictions in Biology research and absolute restrictions imposed by religion; one can continue with dozens of examples.

But, in reality, the extremists in the two edges always end up in the same place. Hitler and Stalin both murdered millions, and signed a friendship pact. Far left secular atheist demonstrators in the western world, including gays and feminists, support Islamic religious fanatics who treat women and gays as low animals. It has always been known that no income tax and 100% income tax yield the same result: no tax collected at all, as shown by the famous Laffer curve. This is the ultimate meeting point of the extremist supporters of tax increase and tax reduction.

Societies, preaching for absolute equality among their citizens, always end up with the largest economic gaps. Fanatic extremist proponents of developing only renewable energy sources, with no nuclear power, delay or prevent acceptable interim solutions to global energy issues, just as much as the oil producers. Misuse of animals in biology research is as damaging as the objections of fanatic animal right groups. One can go on and on with illustrations, which are more visible now than they were a decade or two ago. We live on the verge of an age of extremism.

So, the edge of the circle is the place where all of these extremists meet, live and preach. The military doctor who refuses to obey orders "because Obama was born in Africa" and the army doctor, who murdered 12 people in Texas, are both at the edge of the circle.

If you are a sensible moderate thinking person, open any newspaper and see how many times you will read news items or editorials, which will lead you to say: "Wow, these people are really at the edge of the circle" ...

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.