Payoff Control in the Iterated Prisoner's Dilemma

Payoff Control in the Iterated Prisoner's Dilemma

Dong Hao, Kai Li, Tao Zhou

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 296-302. https://doi.org/10.24963/ijcai.2018/41

Repeated game has long been the touchstone model for agents’ long-run relationships. Previous results suggest that it is particularly difficult for a repeated game player to exert an autocratic control on the payoffs since they are jointly determined by all participants. This work discovers that the scale of a player’s capability to unilaterally influence the payoffs may have been much underestimated. Under the conventional iterated prisoner’s dilemma, we develop a general framework for controlling the feasible region where the players’ payoff pairs lie. A control strategy player is able to confine the payoff pairs in her objective region, as long as this region has feasible linear boundaries. With this framework, many well-known existing strategies can be categorized and various new strategies with nice properties can be further identified. We show that the control strategies perform well either in a tournament or against a human-like opponent.
Keywords:
Agent-based and Multi-agent Systems: Coordination and Cooperation
Agent-based and Multi-agent Systems: Noncooperative Games