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STEPHEN M. KOSSLYN
Director, Center for Advanced Study in the Behavioral Sciences, Stanford University; Author, Image and Mind

Constraint Satisfaction

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

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

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

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

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

Constraint satisfaction is pervasive. For example:

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

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

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

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

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

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


DANIEL C. DENNETT
Philosopher; University Professor, Co-Director, Center for Cognitive Studies, Tufts University; Breaking the Spell

Cycles

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

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

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

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

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

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

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

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

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

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


JENNIFER JACQUET
Postdoctoral Fellow, University of British Columbia

Keystone Consumer

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.


JARON LANIER
Musician, Computer Scientist; Pioneer of Virtural Reality; Author, You Are Not A Gadget: A Manifesto

Cumulative Error

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


FRANK WILCZEK
Physicist, MIT; Recipient, 2004 Nobel Prize in Physics; Author, The Lightness of Being

Hidden Layers

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?


LISA RANDALL
Physicist, Harvard University; Author, Warped Passages

"Science"

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

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

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

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


DOUGLAS RUSHKOFF
Media theorist, Author of Life Inc and Program or Be Programmed

Technologies Have Biases

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.


MARCEL KINSBOURNE
Neurologist & Cognitive Neuroscientist, The New School; Coauthor, Children's Learning and Attention Problems

The Expanding In-Group

The ever-cumulating dispersion, not only of information, but also of population, across the globe, is the great social phenomenon of this age. Regrettably, cultures are being homogenized, but cultural differences are also being demystified, and intermarriage is escalating, across ethnic groups within states and between ethnicities across the world. The effects are potentially beneficial for the improvement of cognitive skills, from two perspectives. We can call these "the expanding in-group" and the "hybrid vigor" effects.

The in-group versus out-group double standard, which had and has such catastrophic consequences, could in theory be eliminated if everyone alive were to be considered to be in everyone else's in-group. This Utopian prospect is remote, but an expansion of the conceptual in-group would expand the range of friendly, supportive and altruistic behavior. This effect may already be in evidence in the increase in charitable activities in support of foreign populations that are confronted by natural disasters. Donors identifying to a greater extent with recipients make this possible. The rise in frequency of international adoptions also indicates that the barriers set up by discriminatory and nationalistic prejudice are becoming porous.

The other potential benefit is genetic. The phenomenon of hybrid vigor in offspring, which is also called heterozygote advantage, derives from a cross between dissimilar parents. It is well established experimentally, and the benefits of mingling disparate gene pools are seen not only in improved physical but also in improved mental development. Intermarriage therefore promises cognitive benefits. Indeed, it may already have contributed to the Flynn effect, the well known worldwide rise in average measured intelligence, by as much as three I.Q. points per decade, over successive decades since the early twentieth century.

Every major change is liable to unintended consequences. These can be beneficial, detrimental or both. The social and cognitive benefits of the intermingling of people and populations are no exception, and there is no knowing whether the benefits are counterweighed or even outweighed by as yet unknown drawbacks. Nonetheless, unintended though they might be, the social benefits of the overall greater probability of in-group status, and the cognitive benefits of increasing frequency of intermarriage entailed by globalization may already be making themselves felt.


JONATHAN HAIDT
Psychologist, University of Virginia; Author, The Happiness Hypothesis

Contingent Superorganism

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

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

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

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

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


NEIL GERSHENFELD
Director, MIT Center for Bits and Atoms; Author, FAB

Truth is a Model

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.


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