Accounting for the integration of descriptive and experiential information in a repeated prisoner's dilemma using an instance-based learning model

The way information is presented, using description or experience, can influence the decision making process. However, little is currently known on how descriptive information is accounted for in subsequent experiential learning. In this paper, we use a computational model based upon Instance-based Learning Theory (IBLT) and use it to study hypotheses on how participants may integrate descriptive and experiential information presented in a repeated prisoner's dilemma. Two players, each simulated by an Instanced-based Learning (IBL) model, play against each other in a repeated prisoner's dilemma. They are provided with a descriptive payoff matrix as well as the experiential information in the form of each other's payoffs after making repeated decisions. Our results demonstrate that the descriptive payoff matrix information is incorporated into decisions using two mechanisms. The first is expectations derived from the description. A second mechanism, highlights the immediate and worst possible outcomes in the description, giving them more attention compared to the other outcomes. We highlight the significance of our results for decision making in social dilemmas.