  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.
