Payoff Control in the Iterated Prisoner's Dilemma

Abstract

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.

Cite

Text

Hao et al. "Payoff Control in the Iterated Prisoner's Dilemma." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/41

Markdown

[Hao et al. "Payoff Control in the Iterated Prisoner's Dilemma." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/hao2018ijcai-payoff/) doi:10.24963/IJCAI.2018/41

BibTeX

@inproceedings{hao2018ijcai-payoff,
  title     = {{Payoff Control in the Iterated Prisoner's Dilemma}},
  author    = {Hao, Dong and Li, Kai and Zhou, Tao},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {296-302},
  doi       = {10.24963/IJCAI.2018/41},
  url       = {https://mlanthology.org/ijcai/2018/hao2018ijcai-payoff/}
}