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ALTERED PAYOFF VALUES AND THE EFFECT ON A POPULATION OF ITERATED PRISONER'S DILEMMA PLAYERS

Game theory and evolutionary programming are used to model social interactions, and simulate aspects of nature. Scientists often use the prisoner's dilemma game with Genetic Algorithms for this purpose. The prisoner's dilemma gives each player a choice between the move best for both players, if they can trust each other (cooperation), or the selfish but safer choice (defection). The combined choices result in a payoff of utilities to each player. This thesis examines the effect of varying payoff values on populations of prisoner's dilemma players. For example, will a selfish player thrive under certain payoffs? As the saying goes, "every man has his price." This thesis asks, "if we alter the risks for selfishness, would the population become more or less selfish?" The prisoner's dilemma game is significant because it readily models conflict in cooperative situations. Scientists also use it to simulate evolutionary biology. Altering payoff values for the supergame influences the success of the individual players. The author found that the payoff values do affect how the population evolves, if cooperation evolves at all. How the players interact also changes the evolution of cooperation. Random interaction provides a more realistic simulation of several kinds of natural behavior. Finally, counting the number of cooperative outputs of the finite state machine proves to be another way to measure cooperation.