Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response
Yiding Feng, Chien-Ju Ho, Wei Tang
摘要
Classic mechanism/information design imposes the assumption that agents are fully rational, meaning each of them always selects the action that maximizes her expected utility. Yet many empirical evidence suggests that human decisions may deviate from this full rationality assumption. In this work, we attempt to relax the full rationality assumption with bounded rationality. Specifically, we formulate the bounded rationality of an agent by adopting the quantal response model (McKelvey and Palfrey, 1995).
We develop a theory of rationality-robust information design in the canonical setting of Bayesian persuasion (Kamenica and Gentzkow, 2011) with binary receiver action. We first identify conditions under which the optimal signaling scheme structure for a fully rational receiver remains optimal or approximately optimal for a boundedly rational receiver. In practice, it might be costly for the designer to estimate the degree of the receiver's bounded rationality level. Motivated by this practical consideration, we then study the existence and construction of robust signaling schemes when there is uncertainty about the receiver's bounded rationality level. 1
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引用它的顶会 Paper6
- Computational Aspects of Bayesian Persuasion under Approximate Best ResponseKunhe Yang, Hanrui ZhangNeurIPS 2024 · 被引用 10 次
- Encoding Human Behavior in Information Design through Deep LearningGuanghui Yu, Wei Tang, Saumik Narayanan, Chien-Ju HoNeurIPS 2023 · 被引用 8 次
- Bias Detection via SignalingYiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas 等NeurIPS 2024 · 被引用 1 次
- Persuasive CalibrationYiding Feng, Wei TangSODA 2026 · 被引用 1 次
- Decision Aggregation under Quantal ResponseZhihuan Huang, Yichong Xia, Yuqing KongICLR 2026
它引用的顶会 Paper8
- Multi-Receiver Online Bayesian PersuasionMatteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola GattiICML 2021 · 被引用 36 次
- Online Bayesian PersuasionMatteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola GattiNeurIPS 2020 · 被引用 26 次
- On the Tractability of Public Persuasion with No ExternalitiesHaifeng XuSODA 2020 · 被引用 22 次
- Algorithmic Price DiscriminationRachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning WangSODA 2020 · 被引用 21 次
- Encoding Human Behavior in Information Design through Deep LearningGuanghui Yu, Wei Tang, Saumik Narayanan, Chien-Ju HoNeurIPS 2023 · 被引用 8 次
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