Learning to Cooperate: Stochastic and Tacit Collusion in Social Exchange

The Prisoner's Dilemma formalizes the social trap that arises when individually rational choices aggregate with mutually undesirable consequences. The game-theoretic solution centers on the opportunity for tacit collusion in repeated play. However, not all actors grasp the strategic implications of future interaction. Accordingly, this study reformulates the game as a stochastic learning model in which the behavior of interdependent actors is constinually shaped by sanctions and cues generated by their interaction. Computer simulations of a two-person game show that adaptive actors are led into a social trap more readily than are fully rational actors, but they are also better at finding their way out. Prosocial norms appear to be a consequence rather than cause of cooperation but useful in promoting forgiveness of random deviance. The model is then elaborated as an N-way Prisoner's Dilemma. Simulations show how the effects of network size, density, mobility, and anonymity derive from a fundamental principle of collective action, that is, the need to reduce the number of choices that must be fortuitously coordinated in order to escape noncooperative equilibrium. The results also suggest how network structure might evolve in tandem with the cooperation it facilitates.