Year: 2025
Type: preprint
Abstract: Originating in evolutionary game theory, the class of "zero-determinant" strategies enables a player to unilaterally enforce linear payoff relationships in simple repeated games. An upshot of this kind of payoff constraint is that it can shape the incentives for the opponent in a predetermined way. An example is when a player ensures that the agents get equal payoffs. While extensively studied in infinite-horizon games, extensions to discounted games, non-affine constraints, richer strategic environments, and behaviors with long memory remain incompletely understood. In this paper, we provide necessary and sufficient conditions for a player to enforce arbitrary constraints (affine or not), in expectation, in discounted games. These conditions characterize precisely which payoff relationships are enforceable using strategies of arbitrary complexity. Our main result establishes that any such enforceable relationship can actually be implemented using a simple two-point reactive learning strategy, which conditions on the opponent's most recent action and the player's own previous mixed action, using information from only one round into the past. For additive payoff constraints, we show that enforcement is possible using even simpler (reactive) strategies that depend solely on the opponent's last move. In other words, this tractable class is universal within expectation-enforcing strategies. As examples, we apply these results to characterize extortionate, generous, and fair strategies in the repeated prisoner's dilemma, nonlinear donation game, asymmetric donation game, and the hawk-dove game, identifying precisely when each class of strategy is enforceable and with what minimum discount factor. We conclude with an example of enforcing a non-affine constraint that induces inequalities on expected payoffs rather than knife-edge conditions.
Source: arXiv (Cornell University)
Authors: Dimou, Nikos, McAvoy, Alex
Institution: University of North Carolina at Chapel Hill
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4416766390