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.

翻译：在本研究中，我们开发了一个奖励设计框架，用于将期望行为作为严格均衡安装到标准解概念中：占优策略均衡、纳什均衡、相关均衡与粗相关均衡。我们还将该框架扩展至各解概念的马尔可夫完美等价形式。我们框架的核心是基于目标解概念与行为结构，对严格可安装性进行全面的数学刻画。这些刻画导出了高效的迭代算法，我们进一步将其推广至通过线性规划处理优化目标的情形。最后，我们探讨了研究结果如何推广至有限理性智能体。