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.
Contingent Superorganisms
Jonathan Haidt
Social psychologist, University of Virginia; author, The Happiness Hypothesis
Humans are the giraffes of altruism. We’re freaks of nature, able (at our best) to achieve antlike 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’s 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 idea 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 forms a functional unit in which each member 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 hivelike 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 forty 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.
The Pareto Principle
Clay Shirky
Social and technology network topology researcher; adjunct professor, NYU Graduate School of Interactive Telecommunications Program; author, Cognitive Surplus: Creativity and Generosity in a Connected Age
You see the pattern everywhere: The top 1 percent of the population controls 35 percent of the wealth. On Twitter, the top 2 percent of users sends 60 percent of the messages. In the health-care system, the treatment of the most expensive fifth of patients creates four-fifths of the overall cost. These figures are always reported as shocking, as if the normal order of things had been disrupted, as if the appearance of anything other than a completely linear distribution of money or messages or effort were 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—the 80/20 rule, Zipf’s 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 percent of a system that exhibits a Pareto distribution, the top 20 percent 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 as the second most common, “of.”)
This pattern was so common that 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 fiftieth-ranked woman 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 percent of all words used in English. The most volatile day in the history of a stock market will typically be twice as volatile as 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 showing 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 percent of a population, just as there are ways to constrain the overall volatility of markets, or 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.
Fin
d That Frame
William Calvin
Theoretical neurobiologist; emeritus professor, University of Washington School of Medicine; author, Global Fever: How to Treat Climate Change
An automatic stage of “compare and contrast” would improve most cognitive functions, not just the grade on an essay. You set up a comparison—say, that the interwoven melodies of rock and roll are like how you must twist when dancing on a boat when the bow is rocking up and down in a different rhythm than the deck is rolling from side to side.
Comparison is an important part of trying ideas on for size, for finding related memories and exercising constructive skepticism. Without it, you can become trapped in someone else’s framing of a problem. You often need to know where someone is coming from—and while Compare ’n’ Contrast is your best friend, you may also need to search for the cognitive framing. What has been cropped out of the frame can lead the unwary to an incorrect inference, as when they assume that what is left out is unimportant. For example, “We should reach a 2˚C (3.6˚F) fever in the year 2049” always makes me want to interject “Unless another abrupt climate shift gets us there next year.”
Global warming’s ramp-up in temperature is the aspect of climate change that climate scientists can currently calculate—that’s where they are coming from. And while this can produce really important insights—even big emission reductions only delay the 2˚C fever for nineteen years—it leaves out all of those abrupt climate shifts observed since 1976, as when the world’s drought acreage doubled in 1982 and jumped from double to triple in 1997, then back to double in 2005. That’s like stairs, not a ramp.
Even if we thoroughly understood the mechanism for an abrupt climate shift—likely a rearrangement of the winds that produce Deluge ’n’ Drought by delivering ocean moisture elsewhere, though burning down the Amazon rain forest should also trigger a big one—chaos theory’s butterfly effect says we still could not predict when a big shift will occur or what size it will be. That makes a climate surprise like a heart attack. You can’t predict when. You can’t say whether it will be minor or catastrophic. But you can often prevent it—in the case of climate, by cleaning up the excess CO2.
Drawing down the CO2 is also typically excluded from the current climate framing. Mere emissions reduction now resembles locking the barn door after the horse is gone—worthwhile, but not exactly recovery. Politicians usually love locking barn doors, as it gives the appearance of taking action cheaply. Emissions reduction only slows the rate at which things get worse, as the CO2 accumulation keeps growing. (People confuse annual emissions with the accumulation that causes the trouble.) On the other hand, cleaning up the CO2 actually cools things, reverses ocean acidification, and even reverses the thermal-expansion portion of rising sea level.
Recently I heard a biologist complaining about models for insect social behavior: “All of the difficult stuff is not mentioned. Only the easy stuff is calculated.” Scientists first do what they already know how to do. But their quantitative results are no substitute for a full qualitative account. When something is left out because it is computationally intractable (sudden shifts) or would just be a guess (cleanup), they often don’t bother to mention it at all. “Everybody [in our field] knows that” just won’t do when people outside the field are hanging on your every word.
So find that frame and ask about what was left out. Like abrupt climate shifts or a CO2 cleanup, it may be the most important consideration of all.
Wicked Problems
Jay Rosen
Professor of journalism, New York University; author, What Are Journalists For?
There’s a problem that anyone who has lived in New York City must wonder about: You can’t get a cab from 4:00 to 5:00 P.M. The reason for this is not a mystery: At a moment of peak demand, taxi drivers tend to change shifts. Too many cabs are headed to garages in Queens, because when a taxi is operated by two drivers twenty-four hours a day, a fair division of shifts is to switch over at 5:00 P.M. Now, this is a problem for the city’s Taxi and Limousine Commission, it may even be a hard one to solve, but it is not a wicked problem. For one thing, it’s easy to describe, as I just showed you. That, right there, boots it from the wicked category.
Among some social scientists, there is this term of art: wicked problems. We would be vastly better off if we understood what wicked problems are and learned to distinguish between them and regular (or “tame”) problems.
Wicked problems have these features: It is hard to say what the problem is, to define it clearly, or to tell where it stops and starts. There is no “right” way to view the problem, no definitive formulation. The way it’s framed will change what the solution appears to be. Someone can always say that the problem is just a symptom of another problem, and that someone will not be wrong. There are many stakeholders, all with their own frames, which they tend to see as exclusively correct. Ask what the problem is and you will get a different answer from each. The problem is interconnected to a lot of other problems; pulling them apart is almost impossible.
It gets worse. Every wicked problem is unique, so in a sense there is no prior art, and solving one won’t help you with the others. No one has “the right to be wrong”; meaning has enough legitimacy and stakeholder support to try stuff that will almost certainly fail at first. Instead, failure is savaged, and the trier is deemed unsuitable for another try. The problem keeps changing on us. It is never definitely resolved. We just run out of patience, or time, or money. It’s not possible to understand the problem first, then solve it; rather, attempts to solve it reveal further dimensions of the problem. (Which is the secret of success for people who are “good” at wicked problems.)
Know any problems like that? Sure you do. Probably the best example in our time is climate change. What could be more interconnected than it? Someone can always say that climate change is just a symptom of another problem—our entire way of life, perhaps—and he or she would not be wrong. We’ve certainly never solved anything like it before. Stakeholders: everyone on the planet, every nation, every company.
When General Motors was about to go bankrupt and throw tens of thousands of people out of work, that was a big, honking problem, which rightly landed on the president’s desk, but it was not a wicked one. Barack Obama’s advisors could present him with a limited range of options; if he decided to take the political risk and save General Motors from collapse, he could be reasonably certain that the recommended actions would work. If they didn’t, he could try more drastic measures.
But health care reform wasn’t like that at all. In the United States, rising health care costs are a classic case of a wicked problem. No “right” way to view it. Every solution comes with its own contestable frame. There are multiple stakeholders, who don’t define the problem the same way. If the number of uninsured goes down but costs go up, is that progress? We don’t even know.
Wicked!
Still, we would be better off if we knew when we were dealing with a wicked problem as opposed to the regular kind. If we could designate some problems as wicked, we might realize that “normal” approaches to problem solving don’t work. We can’t define the problem, evaluate possible solutions, pick the best one, hire the experts, and implement. No matter how much we may want to follow a routine like that, it won’t succeed. Institutions may require it, habit may favor it, the boss may order it, but wicked problems don’t care.
Presidential debates that divided wicked from tame problems would be very different debates. Better, I think. Journalists who covered wicked problems differently from the way in which they covered normal problems would be smarter journalists. Institutions that knew how to distinguish wicked problems from the other kind would eventually learn the limits of command and control.
Wicked problems demand people who are creative, pragmatic, flexible, and collaborative. They never invest too much in their ideas, because they know they will have to alter them. They know
there’s no right place to start, so they simply start somewhere and see what happens. They accept the fact that they’re more likely to understand the problem after it’s solved than before. They don’t expect to get a good solution; they keep working until they’ve found something that’s good enough. They’re never convinced they know enough to solve the problem, so they’re constantly testing their ideas on different stakeholders.
Know any people like that? Maybe we can get them interested in health care . . .
Anthropocene Thinking
Daniel Goleman
Psychologist; author, Emotional Intelligence
Do you know the PDF of your shampoo? A PDF refers to a “partially diminished fraction” of an ecosystem, and if your shampoo contains palm oil cultivated on clear-cut jungle in Borneo, say, that value will be high. How about your shampoo’s DALY? This measure comes from public health: “disability-adjusted life years,” or the amount of one’s life that will be lost to a disabling disease because of, say, a lifetime’s cumulative exposure to a given industrial chemical. So if your favorite shampoo contains two common ingredients, the carcinogen 1,4-Dioxane, or BHA, an endocrine disrupter, its DALY will be higher.
PDFs and DALYs are among myriad metrics for anthropocene thinking, which views how human systems affect the global systems that sustain life. This way of perceiving interactions between the built and the natural worlds comes from the geological sciences. If adopted more widely, this lens might usefully inform how we find solutions to the singular peril our species faces: the extinction of our ecological niche.
Beginning with cultivation and accelerating with the Industrial Revolution, our planet left the Holocene epoch and entered what geologists call the Anthropocene, in which human systems erode the natural systems that support life. Through the Anthropocene lens, the daily workings of the energy grid, transportation, industry, and commerce inexorably deteriorate global biogeochemical systems, such as the carbon, phosphorus, and water cycles. The most troubling data suggest that since the 1950s the human enterprise has led to an explosive acceleration that will reach criticality within the next few decades, as different systems reach a tipping point-of-no-return. For instance, about half the total rise in atmospheric CO2 concentration has occurred in just the last thirty years—and of all the global life-support systems, the carbon cycle is closest to no-return. While such “inconvenient truths” about the carbon cycle have been the poster child for our species’ slow-motion suicide, that’s just part of a much larger picture, with all the eight global life-support systems under attack by our daily habits.
This Will Make You Smarter Page 19