# Payoff Control in the Iterated Prisoner's Dilemma

arXiv: https://arxiv.org/abs/1807.06666 · HTML: https://mlsanity.com/paper/1807.06666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing](https://mlsanity.com/paper/2110.08671/) (2021)
- [Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning](https://mlsanity.com/paper/2110.08330/) (2021)

## Abstract (arXiv)

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.