Brian Skyrms, author of the successful Evolution of the Social Contract has written a sequel. The book is a study of ideas of cooperation and collective action. The point of departure is a prototypical story found in Rousseau's A Discourse on Inequality. Rousseau contrasts the pay-off of hunting hare where the risk of non-cooperation is small but the reward is equally small, against the pay-off of hunting the stag where maximum cooperation is required but where the reward is so much (...) greater. Thus, rational agents are pulled in one direction by considerations of risk and in another by considerations of mutual benefit. Written with Skyrms's characteristic clarity and verve, this intriguing book will be eagerly sought out by students and professionals in philosophy, political science, economics, sociology and evolutionary biology. (shrink)

In species capable of learning, including our own, individuals can modify their behavior by some adaptive process. Important classes of behavior - mating, predation, coalitions, trade, signaling, and division of labor - involve interactions between individuals. The agents involved learn two things: with whom to interact and how to act. That is to say that adaptive dynamics operates both on structure and strategy.

We investigate a simple stochastic model of social network formation by the process of reinforcement learning with discounting of the past. In the limit, for any value of the discounting parameter, small, stable cliques are formed. However, the time it takes to reach the limiting state in which cliques have formed is very sensitive to the discounting parameter. Depending on this value, the limiting result may or may not be a good predictor for realistic observation times.

Reinforcement schemes are a class of non-Markovian stochastic processes. Their non-Markovian nature allows them to model some kind of memory of the past. One subclass of such models are those in which the past is exponentially discounted or forgotten. Often, models in this subclass have the property of becoming trapped with probability 1 in some degenerate state. While previous work has concentrated on such limit results, we concentrate here on a contrary effect, namely that the time to become trapped may (...) increase exponentially in 1/x as the discount rate, 1− x, approaches 1. As a result, the time to become trapped may easily exceed the lifetime of the simulation or of the physical data being modeled. In such a case, the quasi-stationary behavior is more germane. We apply our results to a model of social network formation based on ternary (three-person) interactions with uniform positive reinforcement. (shrink)

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on April 27, 1999.