AF: Small: Frontiers in Algorithmic Game Theory

算法博弈论是一个使用和扩展经济学和博弈论的工具来推理基本的计算机科学问题和应用的领域。PI将在算法博弈论的三个中心子领域推行广泛的研究议程。第一组目标是开发一般分析框架，以确定对均衡在最坏情况下无效的可靠保证，并考虑除最坏情况纳什均衡的质量外的其他低效率衡量标准。第二组目标涉及开发新颖的最坏情况分析框架，为设计与激励兼容的拍卖提供信息，以解决收入最大化问题。最终目标是应用计算复杂性理论来严格解释经济学和博弈论中的某些障碍，特别是在最优机制设计和均衡计算中。

Algorithmic game theory is a field that uses and extends tools from economics and game theory to reason about fundamental computer science problems and applications. The PI will pursue a broad research agenda across three central subfields of algorithmic game theory. The first set of goals is to develop general analysis frameworks that can identify robust guarantees on the worst-case inefficiency of equilibria, and to consider measures of inefficiency other than the quality of a worst-case Nash equilibrium. The second set of goals concern developing novel worst-case analysis frameworks to inform the design of incentive-compatible auctions for revenue-maximization problems. The final goal is to apply computational complexity theory to explain rigorously certain barriers in economics and game theory, in particular in optimal mechanism design and in equilibrium computation.