设计可严格实现的激励机制，让期望行为成为各类博弈均衡。
Optimally Installing Strict Equilibria
- 基于数学刻画，构建可安装严格均衡的奖励设计框架。
- 提出迭代算法，支持线性规划优化目标，高效求解。
- 适用于完全理性与有限理性的智能体，拓展性强。
本文提出一种奖励设计框架，可将期望行为作为严格均衡嵌入标准解概念：占优策略均衡、纳什均衡、相关均衡和粗略相关均衡。同时将框架扩展至马尔可夫完美解概念。核心在于对严格可安装性的全面数学表征，依赖于解概念与行为结构。由此导出高效迭代算法，并通过线性规划支持优化目标。最后探讨了结果在有限理性代理情形下的推广性。
原文摘要 · Abstract (English)
In this work, we develop a reward design framework for installing a desired behavior as a strict equilibrium across standard solution concepts: dominant strategy equilibrium, Nash equilibrium, correlated equilibrium, and coarse correlated equilibrium. We also extend our framework to capture the Markov-perfect equivalents of each solution concept. Central to our framework is a comprehensive mathematical characterization of strictly installable, based on the desired solution concept and the behavior's structure. These characterizations lead to efficient iterative algorithms, which we generalize to handle optimization objectives through linear programming. Finally, we explore how our results generalize to bounded rational agents.
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