Opponent Modelling in Strategic Interaction

In repeated multi-agent games such as Poker or Financial Markets, agents (human or artificial) can learn about the strategies of their opponents and adapt their own strategies accordingly. Traditionally, game theory has ignored such learning or opponent modelling, focusing on equilibrium strategies for single-shot games. In this study, we will focus on opponent modelling in multi-agent games. Participants play cooperative and competitive games repeatedly with distinct computer agents that implement differentiated strategies. We aim to elicit whether participant's strategies are affected by knowledge of the opponent they believe they are facing, and to what extent is this knowledge transferred across games. A subsequent goal is to test various hypotheses on how these models are implemented by participants, such as through observation of past plays, stereotyping or perspective taking.