Standing on the Sun. Christopher Meyer
from an understanding of the individual parts. At the time, economic methods afforded no way to incorporate this insight; you might describe individual choices, but there were no tools for aggregating them except to look at averages. Our GDP and other national statistics, developed during the Depression, could do no better. Today, though, cheap computing power has made it practical to simulate individual decisions and the effects of their interaction. You don't have to be a well-funded economist to run such simulations; this is how games such as SimCity work. The Sims are the agents, each Sim has a “personality” made up of a set of rules to live by, and the properties of the population (its overall happiness, for example) are the net results of all their choices and interactions. Such agent-based modeling (ABM)—understanding the macro-scale picture that emerges from the interactions of many individual decision makers—has become a foundation of adaptive systems research.13
Second, Lucas introduced recursive methods to macroeconomics, meaning that what happens next year is driven in part by what happened last year—in effect, acknowledging that last year's ripples cause this year's decisions. Ecologists use these methods to explain variation over time in the numbers of species in an ecology: last year's rain kept the hunters at home, so fewer grouse were shot; this year there are more grouse. The good hunting draws out hunters, so the year after that, fewer grouse, and the population exhibits a “business cycle” for no endogenous reason. Lucas won the 1995 Nobel Prize in Economics for his work and paved the way for others. Ideas that seemed far out in the early 1990s won Nobel Prizes in 2004 and NIH Pioneer Awards in 2009.
Economists call this way of thinking—assessing how one economic action creates a force that acts on others—dynamic analysis, in contrast to comparative statics. And, consistent with Lucas's first insight, it applies not only to a single market. According to Edward Prescott, who won the 2004 Nobel Prize in Economics, “The revolution in macroeconomics was to use dynamic economic reasoning.”14 Prescott carried Lucas's work on agents forward in the late 1970s, developing models of economic cycles incorporating these individual-driven dynamics.
In 1988, a landmark event took place: Kenneth Arrow and Philip Anderson, Nobel laureates in Economics and Physics, respectively, pulled together interested academics—including biologists and computer scientists—at the first of three conferences at the Santa Fe Institute (SFI) called “The Economy as an Evolving Complex System.” Reflecting on the relative states of the social sciences and physical sciences, one of the physicists in attendance compared economics to his recent observations of cars in Cuba, shut off from the modern world for forty years. “The mathematical Packards and DeSotos were the … techniques that the Marginalists had plundered from physics textbooks a hundred years ago.”15 The impatience for progress seems to be spreading; in 2010, George Soros pledged to spend $50 million over ten years to fund the Institute for New Economic Thinking.
The SFI conferences signaled an understanding that if economies were dynamic systems, there was some new thinking to do. At its core, SFI was devoted to the idea of generalizing from biological evolution to all systems that adapt. In this framework, any system made up of groups of interacting agents—decision makers, who might be birds in a flock, traders in a market, or individuals in a bar—behaves according to a set of rules, and these rules, properly applied, govern the evolution of all systems that adapt. What calculus did for physical sciences—allow lots of confusing data about falling cannon balls, magnetic fields, or chemical reactions to make sense by applying the same powerful toolkit to all of them—complexity scientists are trying to do for the social sciences. And because the biosphere is the adaptive system that has been studied most, some of this thinking involves observing how biological evolution works, abstracting those observations into mathematics, and then seeing whether the rules apply to other systems.16
It's not hard to see the parallel: the market is an environment in which certain goods and services thrive and, having been selected, shape a new generation of an industry's participants. And coevolution—the adjustments going on between the shape of finches' beaks and the forms of the flowers they feed on—is a fact of economic life: when Intel makes faster chips, Microsoft's code expands to fill them. Social scientists are at work modeling growth, innovation, economic coevolution, mergers and extinctions, and other economic phenomena to prove the parallels at deeper levels. A new economics that conforms to the facts of life—faster adaptation, variation of practices in contact with one another in a global economy—is on the way. But business needn't wait for the previous generation of economists to be defunct to take advantage of this perspective.
The Peacock's Tale
One special case in biology turns out to be unfortunately common in economics: the runaway effect. As the term implies, this is what happens when a feedback loop gets established that reinforces a trait beyond all reason. Like a runaway horse, it's hard to rein in.
The best way to describe this effect is with reference to the classic example: the peacock's tail. How has it come to be so extravagant? Well, because peahens are partial to males with long tails. This means that if you're a peacock, size matters: more tail, more offspring.
If you live, that is. The challenge is that growing and hauling about that large ornament is as expensive as owning a Ferrari; it costs the peacock energy, meaning it needs more food. Worse, unlike a man with a Quattroporte, the peacock is slowed down by that attractive appendage, making it more difficult to elude predators. In nearly every species of bird and beast, females select males based on visible attributes, but in the vast majority of cases, those attributes are legitimate signifiers of survival strength. In the case of the peacock, sexual selection fails to align with fitness selection. So the peacock lives a shorter, if perhaps happier, life, but the net is that there are fewer peacocks in the ecology. You can see why experts on evolution also like to call this phenomenon biological suicide.
Nature has had billions of years to sort out this problem, so it's rare in ecologies. Human society is newer to the game, so the potential for runaway effects is higher. Indeed, R. A. Fisher, the British scientist who first described the phenomenon in the 1920s, was quite aware of its applicability to social matters. According to Mary Bartley, a student of his work, Fisher “often contrasted sexual selection with its well-known counterpart, natural selection, and used both of these brands of ‘selection’ to discuss fundamental problems impeding the progress of British society.” He was fond of extrapolating to worst-case scenarios; using the analogy he would warn that, if certain problems were not corrected, their runaway nature could lead to the nation's demise.
Picture the financial trader in the role of peacock, incented by short-term profits to undertake behavior that increases systemic risk for the financial species. Or the manager selected for the extravagance of his return on equity rather than for the ability to generate value on many dimensions. Or the CEO told incessantly by Wall Street analysts that quarterly profits are the essence of running a company. Runaway effects happen when a single criterion governs resource allocation—the economic equivalent of sexual selection—without balancing other criteria that determine the overall value of the choice to society.
Readers who have spent careers in management might think of this as a special class of unintended consequences—those logical but somehow unforeseen effects that often take place following bold managerial interventions. A common example is a new incentive system that aims to motivate some narrow behavior and does so at the expense of the overall health of the system. Steve Kerr, a longtime colleague of Jack Welch (Kerr led the creation of GE's management training facility in Crotonville, New York, and now serves as a dean of the online MBA program Jack Welch University), wrote about such cases in a classic management article, “On the Folly of Rewarding A While Hoping for B.”
Unintended consequences happen all the time in human rule making. Our favorite recent example is the legislation in Indonesia designed to reduce its awe-inspiring traffic jams. Perceiving that carpooling would make a difference, the government created new traffic lanes that could be used only by vehicles carrying more than one occupant (such lanes are common in many urban settings). But in a land filled with poor people with time on their hands, the main effect was to create a new industry: passengers for hire. A sort of inverse to taxi stands started to show up: lines of people