Dynamic interventions with limited knowledge in network games

TL;DR

This paper explores intervention strategies in network games to optimize social welfare when the regulator has limited knowledge of players and network parameters, proposing protocols with proven convergence.

Contribution

It introduces static, dynamic, and adaptive intervention protocols tailored for scenarios with limited information, ensuring convergence to social optimality in network games.

Findings

Proposed intervention protocols guarantee convergence to social optimum.

Protocols are effective under various knowledge limitations.

Validated through a Cournot competition case study.

Abstract

This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their actions with the aim of maximizing their individual payoff functions, while a central regulator uses interventions to modify their marginal returns and maximize the social welfare function. This work builds on the key observation that the solution to the steering problem depends on the knowledge of the regulator on the players' parameters and the underlying network. We, therefore, consider different scenarios based on limited knowledge and propose suitable static, dynamic and adaptive intervention protocols. We formally prove convergence to the social optimum under the proposed mechanisms. We demonstrate our theoretical findings on a case study of Cournot competition with differentiated goods.