Socially Optimal Non-discriminatory Restrictions for Continuous-Action Games

Authors

  • Michael Oesterle Institute for Enterprise Systems (InES), University of Mannheim
  • Guni Sharon Texas A&M University

DOI:

https://doi.org/10.1609/aaai.v37i10.26375

Keywords:

MAS: Mechanism Design, ML: Bias and Fairness, MAS: Adversarial Agents, MAS: Agent-Based Simulation and Emergent Behavior, MAS: Applications, MAS: Coordination and Collaboration, PEAI: Bias, Fairness & Equity

Abstract

We 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. First, we propose a formal model of a Restricted Game and the corresponding restriction optimization problem. We then present an algorithm to find optimal non-discriminatory restrictions under some assumptions. Our 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, we outline a generalization of our approach to the much wider scope of Stochastic Games.

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Published

2023-06-26

How to Cite

Oesterle, M., & Sharon, G. (2023). Socially Optimal Non-discriminatory Restrictions for Continuous-Action Games. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 11638-11646. https://doi.org/10.1609/aaai.v37i10.26375

Issue

Section

AAAI Technical Track on Multiagent Systems