Using genetic algorithms for simulation of social dilemmas

Abstract

Studying social dilemmas and their underlying behavioral, cognitive, and evolutionary constructs is a more complicated challenge than most laboratory experiments or empirical data collection methods can meet. In contrast to those behaviors observed in a well defined laboratory setting, naturally occurring social dilemmas have a high level of complexity, interdependencies, and many non-linear links. Over the last three decades, several attempts have been made to study intricate social interactions by using computer simulations. A well-known study conducted by Robert Axelrod (1980a, b, 1981, 1984) examined the evolution of cooperation among agents who played a repeated prisoner's dilemma game in a heterogeneous population. This seminal work inspired many more studies in diverse social science domains (see, for example, Latane & Novak's (1997) study of attitude change, Fischer & Suleiman's (1997) study of the evolution of intergroup cooperation, or Axelrod's (1986) and Saam & Harrer's (1999) studies on the influence of social norms). © 2008 Springer US.