Backward vs. Forward-Oriented Decision Making in the Iterated Prisoner’s Dilemma: A Comparison Between Two Connectionist Models

Abstract

We compare the performance of two connectionist models developed to account for some specific aspects of the decision making process in the Iterated Prisoner’s Dilemma Game. Both models are based on common recurrent network architecture. The first of them uses a backward-oriented reinforcement learning algorithm for learning to play the game while the second one makes its move decisions based on generated predictions about future games, moves and payoffs. Both models involve prediction of the opponent move and of the expected payoff and have an in-built autoassociator in their architecture aimed at more efficient payoff matrix representation. The results of the simulations show that the model with explicit anticipation about game outcomes could reproduce the experimentally observed dependency of the cooperation rate on the so-called cooperation index thus showing the importance of anticipation in modeling the actual decision making process in human participants. The role of the models’ building blocks and mechanisms is investigated and discussed. Comparisons with experiments with human participants are presented.