Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response
Yiding Feng, Chien-Ju Ho, Wei Tang
Abstract
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
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 13cc6659-ad67-4acb-ae23-e2281621eabfCited by top-tier papers6
- Computational Aspects of Bayesian Persuasion under Approximate Best ResponseKunhe Yang, Hanrui ZhangNeurIPS 2024 · 10 citations
- Encoding Human Behavior in Information Design through Deep LearningGuanghui Yu, Wei Tang, Saumik Narayanan, Chien-Ju HoNeurIPS 2023 · 8 citations
- Bias Detection via SignalingYiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas et al.NeurIPS 2024 · 1 citation
- Persuasive CalibrationYiding Feng, Wei TangSODA 2026 · 1 citation
- Decision Aggregation under Quantal ResponseZhihuan Huang, Yichong Xia, Yuqing KongICLR 2026
Builds on8
- Multi-Receiver Online Bayesian PersuasionMatteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola GattiICML 2021 · 36 citations
- Online Bayesian PersuasionMatteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola GattiNeurIPS 2020 · 26 citations
- On the Tractability of Public Persuasion with No ExternalitiesHaifeng XuSODA 2020 · 22 citations
- Algorithmic Price DiscriminationRachel Cummings, Nikhil R. Devanur, Zhiyi Huang, Xiangning WangSODA 2020 · 21 citations
- Encoding Human Behavior in Information Design through Deep LearningGuanghui Yu, Wei Tang, Saumik Narayanan, Chien-Ju HoNeurIPS 2023 · 8 citations
Related papers
- Private Bayesian Persuasion with Sequential GamesAndrea Celli, Stefano Coniglio, Nicola GattiAAAI 2020 · 29 citations
- Algorithms for Persuasion with Limited CommunicationRonen Gradwohl, Niklas Hahn, Martin Hoefer, Rann SmorodinskySODA 2021 · 6 citations
- Mediated Cheap Talk DesignItai Arieli, Ivan Geffner, Moshe TennenholtzAAAI 2023 · 5 citations
- Differentially Private Bayesian PersuasionYuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran ZhengWWW 2025 · 2 citations
- Bayesian Persuasion under Ex Ante and Ex Post ConstraintsYakov Babichenko, Inbal Talgam-Cohen, Konstantin ZabarnyiAAAI 2021 · 17 citations
