Modeling Social Information in Conflict Situations through Instance-Based Learning Theory


Behavior in conflict situations can be influenced by the social information that individuals have about their opponents. This paper tests whether an existent Instance-based Learning (IBL) model, built using the Instance-based Learning Theory (IBLT) to explain behavior in a single-person binary-choice task (BCT), can predict behavior in a two-player iterated prisoner’s dilemma (IPD) game. The same IBL model is generalized to two conditions in the IPD: Social, where individuals have information about their opponents and their choices; and Non-social, where individuals and opponents lack this information. We expect the single-person IBL model to predict behavior in the Non-social condition better than in the Social condition. However, due to the structural differences between BCT and IPD, we also expect only moderately good model predictions in the Non-social condition. Our results confirm these expectations. These findings highlight the need for additional cognitive mechanisms to account for social information in conflict situations.

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