Bayesian Persuasion

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8 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Learning to Price with Persuasion
Aug 17, 2026Maria-Florina Balcan, Tejas Pagare, Karan SinghBayesian Persuasion Mechanism Design
Carnegie Mellon University, Pittsburgh, USA
Sweet Little Lies: Strategic Deception in AI Emotional Support Chatbots
Aug 2, 2026Aseem Pahuja, Zhiling Guo, Tahir Abbas SyedEmotional Support Conversation Deception in Language Models
Alliance Manchester Business School, The University of Manchester, UK · G. Brint Ryan College of Business, University of North Texas, USA
Learning to Persuade Privately Informed Receivers
Jul 30, 2026I. Arda Vurankaya, Ufuk TopcuBayesian Persuasion
The University of Texas at Austin
Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving
Jul 15, 2026Merlin Paul, Anup ApremReinforcement Learning RL for Autonomous Driving
Quantifying Theoretical AI Alignment Guarantees: Receiver-Utility Bounds in Bayesian Persuasion
Jun 20, 2026Eric Yachbes, Eva TardosAI Alignment Information Design
Cornell University, United States
Pride and Prejudice: Toward an Information-Theoretic Framework for Mutually Communicative Driver Behavior Modeling
Jun 15, 2026Tingjun Li, Nan Xu, Shuo Feng +3Autonomous Driving Bayesian Persuasion
State Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, 5988 Renmin Street, Changchun, 130025, Jilin, China · Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China · Department of Engineering, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, L2S3A1, Ontario, Canada +1
Meta-Learning for Repeated Bayesian Persuasion
Mar 20, 2026Ata Poyraz Turna, Asrin Efe Yorulmaz, Tamer BaşarBayesian Persuasion Meta-Learning
Bogazici University · University of Illinois Urbana–Champaign
Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment
Oct 15, 2025Buwei He, Yang Liu, Zhaowei Zhang +6Game Theory LLM Decision-Making
Beijing University of Posts and Telecommunications · Beijing Institute for General Artificial Intelligence · Peking University +1

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Learning to Price with Persuasion

Maria-Florina Balcan, Tejas Pagare, Karan Singh

Carnegie Mellon University, Pittsburgh, USA

Bayesian Persuasion Mechanism Design Sample Complexity Learning to Optimize

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Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer's taste via a signaling scheme. We relax the assumption that the seller knows the buyers' belief about the distribution of tastes and study the sample requirements of designing a revenue maximizing scheme. We consider both the batch setting where we have access to data from a set of i.i.d. buyers and an online demand query model where we observe the buyers' behaviors to seller's schemes. Despite the apparent non-convexity of the problem, we also give the first FPTAS to compute a scheme that maximizes the revenue within an arbitrarily small additive loss, which was left open by Bergemann et al. (2022). Overall, this brings a new learning perspective in asymmetric economic settings where buyers and sellers know different types of information.