Paper on Socially Optimal Restrictions for Multi-Player Games Accepted for AAAI 2023

We are happy to announce that the Paper “Socially Optimal Non-Discriminatory Restrictions for Continuous-Action Games” by Michael Oesterle and Guni Sharon has been accepted for the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023).

In 2022, InES PhD candidate Michael Oesterle was awarded a grant for a Research Visit by the Fulbright Association, allowing him to spend four months at Texas A&M University and to work with Prof. Guni Sharon, a distinguished researcher in the fields of Game Theory and Multiagent Modeling and Simulation. The result of this collaboration, a paper on the game-theoretic perspective of multi-agent governance, has now been accepted for publication at one of the flagship conferences on Artificial Intelligence, which will be held in February 2023 in Washington, D.C. (USA).

In this paper, the authors address the following mechanism design problem: Given a multi-player Normal-Form Game (NFG) with a continuous action space, find a non-discriminatory (i.e., identical for all players) restriction of the action space which maximizes the resulting Nash Equilibrium with respect to a fixed social utility function. They first propose a formal model of a Restricted Game and the corresponding restriction optimization problem, and then present an algorithm to find optimal non-discriminatory restrictions under some assumptions. Experimental results with Braess’ Paradox and the Cournot Game show that this method leads to an optimized social utility of the Nash Equilibria, even when the assumptions are not guaranteed to hold. Finally, they outline a generalization of our approach to the much wider scope of Stochastic Games.

The paper, together with supplementary materials and the experimental codebase, can be found at Github.