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CHARLES SEIFE
Professor of Journalism, New York University; formerly journalist, Science magazine; Author, Proofiness

Randomness

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


CLIFFORD PICKOVER
Author, The Math Book: From Pythagoras to the 57th Dimension; Jews in Hyperspace

Kaleidoscopic Discovery Engine

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

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

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

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

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

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

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


REBECCA NEWBERGER GOLDSTEIN
Philosopher; Novelist; Author, Betraying Spinoza; 36 Arguments for the Existence of God: A Work of Fiction

Inference To The Best Explanation

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.


NASSIM N. TALEB
Distinguished Professor of Risk Engineering, New York University; Author, The Black Swan; The Bed of Procrustes

Antifragility — or— The Property Of Disorder-Loving Systems

I

Antifragility

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

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

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

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

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

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

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

The Triad

 

 

FRAGILE

ROBUST

ANTI-
FRAGILE

Mythology — Greek

Sword of
Damocles, Rock of Tantalus

Phoenix

Hydra

Biological & Economic Systems

Efficiency

Redundancy

Degeneracy (functional redundancy, in the Edelman-Galy sense)

Science/Technology

Directed Research

Opportunistic research

Stochastic Tinkering (convex bricolage)

Human Body

Mollification, atrophy, "aging", sarcopenia

Recovery

Hypertrophy,
Hormesis, Mithridatism

Political Systems

Nation-State;
Centralized

Statelings, vassals under a large empire

City-State; Decentralized

Income

Companies

 

Income of Executives (bonuses)

Civilization

Post-agricultural
Modern urban

Ancient settlements

Nomadic and hunter-gatherer tribes

Decision Making

Model-based  probabilistic
decision making

Heuristic-based decision making

Convex heuristics

Knowledge

Explicit

Tacit

Tacit with convexity

Epistemology

True-False

 

Sucker-Nonsucker

Ways of Thinking

Modernity

Medieval Europe

Ancient Mediterranean

Errors

Hates mistakes

Mistakes are just information

Loves mistakes

Learning

Classroom

Real life, pathemata mathemata

Real life and library

Medicine

Additive treatment (give medication)

 

Subtractive treatment (remove items from consumption, say carbs, etc.)

Finance

Short Optionality

 

Long Optionality

Decision Making

Acts of commission

 

Acts of omission ("missed opportunity")

Literature

E-Reader

Book

Oral Tradition

Business

Industry

Small Business

Artisan

Finance

Debt

Equity

Venture Capital

Finance

Public Debt

Private debt with no bailout

 

General

Large

Small but specialized

Small but not specialized

General

Monomodal payoff

 

Barbell  polarized payoff

Finance

Banks, Hedge funds managed by economists

Hedge Funds (some)

Hedge Funds
(some)

Business

Agency Problem

 

Principal Operated

Reputation (profession)

Academic, Corporate executive, Pope, Bishop, Politician

Postal employee, Truck driver, train conductor

Artist, Writer

Reputation (class)

Middle Class

Minimum wage persons

Bohemian,
aristocracy, old money

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

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

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

II

Jensen's Inequality as the Hidden Engine of History

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

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

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

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


AUBREY DE GREY
Gerontologist; Chief Science Officer. SENS Foundation; Author, Ending Aging

A Sense Of Proportion About Fear Of The Unknown

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.


EMANUEL DERMAN
Professor, Financial Engineering, Columbia University; Principal, Prisma Capital Partners; Former Head, Quantitative Strategies Group, Equities Division, Goldman Sachs & Co.; Author, My Life as a Quant

Pragmamorphism

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

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

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

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

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

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

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

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


NICHOLAS CARR
Author, The Shallows: What the Internet Is Doing to Our Brains

Cognitive Load

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.


HANS ULRICH OBRIST
Curator, Serpentine Gallery, London 

To Curate

Lately, the word "curate" seems to be used in an greater variety of contexts than ever before, in reference to everything from a exhibitions of prints by Old Masters to the contents of a concept store. The risk, of course, is that the definition may expand beyond functional usability. But I believe "curate" finds ever-wider application because of a feature of modern life that is impossible to ignore: the incredible proliferation of ideas, information, images, disciplinary knowledge, and material products that we all witnessing today. Such proliferation makes the activities of filtering, enabling, synthesizing, framing, and remembering more and more important as basic navigational tools for 21st centurylife. These are the tasks of the curator, who is no longer understood as simply the person who fills a space with objects but as the person who brings different cultural spheres into contact, invents new display features, and makes junctions that allow unexpected encounters and results.

Michel Foucault once wrote that he hoped his writings would be used by others as a theoretical toolbox, a source of concepts and models for understanding the world. For me, the author, poet, and theoretician Eduard Glissant has become this kind of toolbox. Very early he noted that in our phase of globalization — which is not the first one — there is a danger of a homogenization, but at the same time there is a counter movement to globalization, the retreat into one's own culture. And against both dangers he proposes the idea of mondialité — a global dialogue that augments difference. This inspired me to handle exhibitions in a new way. There is a lot of pressure on curators to do shows not only in one place, but to send them around the world by simply packing them into boxes in one city and unpacking them in the next ‚ this is a homogenizing sort of globalization. Using Glissant's idea as a tool means to develop exhibitions that always build a relation to their place, that change permanently with their different local conditions, that create a changing dynamic, complex system with feedback loops.

To curate, in this sense, is to refuse static arrangements and permanent alignments and instead to enable conversations and relations. Generating these kinds of links is an essential part of what it means to curate, as is disseminating new knowledge, new thinking, and new artworks in a way that can seed future cross-disciplinary inspirations. But there is another case for curating as a vanguard activity for the 21st century.

As the artist Tino Sehgal has pointed out, modern human societies find themselves today in an unprecedented situation: the problem of lack, or scarcity, which has been the primary factor motivating scientific and technological innovation, is now being joined and even superseded by the problem of the global effects of overproduction and resource use. Thus moving beyond the object as the locus of meaning has a further relevance. Selection, presentation, and conversation are ways for human beings to create and exchange real value, without dependence on older, unsustainable processes. Curating can take the lead in pointing us towards this crucial importance of choosing.


SAMUEL BARONDES
Director of the Center for Neurobiology and Psychiatry at the University of California, San Francisco; Author, Better than Prozac

Each Of Us Is Ordinary, And Yet One Of A Kind

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

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

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

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

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


RICHARD NISBETT
Social Psychologist, Co-Director, Culture and Cognition Program, University of Michigan; Author, Intelligence and How to Get It

"Graceful" SHA's

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."

U
K
3
8

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.


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