论文标题
在网络游戏中知识有限的动态干预措施
Dynamic interventions with limited knowledge in network games
论文作者
论文摘要
本文研究了干预设计的问题，目的是将非合作者在二次网络游戏中的行动转向社会最佳。玩家选择自己的行动是为了最大程度地提高自己的收益功能，而中央监管机构则使用干预措施来修改其边际收益并最大程度地提高社会福利功能。这项工作基于关键观察，即转向问题的解决方案取决于调节器对玩家参数和基础网络的知识。因此，我们根据有限的知识来考虑不同的方案，并提出合适的静态，动态和适应性干预方案。我们正式证明在提议的机制下融合了社会最佳。我们在与差异化商品的库诺特竞争案例研究中展示了我们的理论发现。
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.
