Adaptive Incentive Design
Adaptive Incentive Design
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DOI：
10.1109/tac.2020.3027503
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发表时间：
2018-06
影响因子：
6.8
通讯作者：
L. Ratliff;Tanner Fiez
中科院分区：
文献类型：
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作者：
L. Ratliff;Tanner Fiez
本文应用控制理论和优化技术，对委托人与多个代理人交互时面临逆向选择的委托代理问题进行了自适应激励设计。特别是，委托人的目标依赖于决策过程先验未知的战略决策者（代理人）的数据。我们考虑了两种情况，agent对彼此采取最佳对策（纳什），以及他们采用短视更新规则。通过参数化代理的效用函数和提供的激励，我们开发了一种算法，委托人可以使用该算法来学习代理的决策过程，同时设计激励来改变他们的反应，使其更可取。给出了该算法在无噪声和有噪声情况下的收敛结果，并给出了实例。
We apply control theoretic and optimization techniques to adaptively design incentives for principal-agent problems in which the principal faces adverse selection in its interaction with multiple agents. In particular, the principal's objective depends on data from strategic decision makers (agents) whose decision-making process is unknown a priori. We consider both the cases where agents play best response to one another (Nash) and where they employ myopic update rules. By parametrizing the agents’ utility functions and the incentives offered, we develop an algorithm that the principal can employ to learn the agents’ decision-making processes while simultaneously designing incentives to change their response to one that is more desirable. We provide convergence results for this algorithm both in the noise-free and noisy cases and present illustrative examples.