The social sciences have had a good run with linear models, in which effects are proportionate to their causes. Nearly all of our theoretical models, in fields as diverse as microeconomics and evolutionary game theory, are equilibrium theories, which examine the properties of various fixed points and analyze the conditions under which they are selected. In contrast, “complexity” uses different mathematical tools to investigate nonlinear processes. But linear models have the advantages of simplicity and power. Is there a real need to import the theoretical apparatus of “complexity” into the social sciences? Or might it be merely the latest example of Fashionable Nonsense?

As it turns out, one need not seek very far to discover nonlinear dynamics in the social world. And if more than one attractor exists, the resulting variation in dynamical behavior will be mistaken for noise if one assumes linearity. In the past two or three decades, interest in complexity has burgeoned across the social sciences. Under the banner of complexity, researchers have investigated questions as dissimilar as the causes of the Maya collapse, the spread of disease, the origins of syntax, the structural properties of cities, and the evolution of culture. Presently, the mathematical tools needed to analyze complexity present an entry barrier for many social scientists. But generation time in graduate schools is short, and the physicists who have taken the lead in the application of complexity to social science are beginning to have their elbows jostled by social scientists with a new set of skills. As for the likely impact, it is still early days. But one change that is already visible on the horizon has to do with history, which plays no role in equilibrium analysis, but is intrinsic to many of the questions and methods developed to study complex systems.