Game Redesign in No-regret Game Playing
Abstract
We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize all players to take a specific target action profile frequently; and (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Game redesign describes both positive and negative applications: a benevolent designer who incentivizes players to take a target action profile with better social welfare compared to the solution of the original game, or a malicious attacker whose target action profile benefits themselves but not the players. Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms.
Keywords
Cite
@article{arxiv.2110.11763,
title = {Game Redesign in No-regret Game Playing},
author = {Yuzhe Ma and Young Wu and Xiaojin Zhu},
journal= {arXiv preprint arXiv:2110.11763},
year = {2021}
}