Axiomatic foundations of Bayesian persuasion
Axiomatic foundations of Bayesian persuasion
In this paper, we study axiomatic foundations of Bayesian persuasion, where a principal (i.e., sender) delegates the task of choice making after informing a biased agent (i.e., receiver) about the payoff relevant uncertain state (see, e.g., Kamenica and Gentzkow (2011)). Our characterizations involve novel models of Bayesian persuasion, where the principal can steer the agent's bias after acquiring costly information. Importantly, we provide an elicitation method using only observable menu-choice data of the principal, which shows how to construct the principal's subjective costs of acquiring information even when he anticipates managing the agent's bias.
Higashi, Youichiro
e54a7686-a25d-4bcc-9c46-78ff368c2779
Ozbek, Kemal
e7edfcf5-cb17-4e64-bfa4-30fb527d2e46
Takeoka, Norio
2dacf626-57eb-4956-81bc-c0197539bc01
30 December 2025
Higashi, Youichiro
e54a7686-a25d-4bcc-9c46-78ff368c2779
Ozbek, Kemal
e7edfcf5-cb17-4e64-bfa4-30fb527d2e46
Takeoka, Norio
2dacf626-57eb-4956-81bc-c0197539bc01
Higashi, Youichiro, Ozbek, Kemal and Takeoka, Norio
(2025)
Axiomatic foundations of Bayesian persuasion
32pp.
Record type:
Monograph
(Working Paper)
Abstract
In this paper, we study axiomatic foundations of Bayesian persuasion, where a principal (i.e., sender) delegates the task of choice making after informing a biased agent (i.e., receiver) about the payoff relevant uncertain state (see, e.g., Kamenica and Gentzkow (2011)). Our characterizations involve novel models of Bayesian persuasion, where the principal can steer the agent's bias after acquiring costly information. Importantly, we provide an elicitation method using only observable menu-choice data of the principal, which shows how to construct the principal's subjective costs of acquiring information even when he anticipates managing the agent's bias.
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Published date: 30 December 2025
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Local EPrints ID: 510698
URI: http://eprints.soton.ac.uk/id/eprint/510698
PURE UUID: 05468d8f-d71f-40f5-8d49-677f577a4ac5
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Date deposited: 16 Apr 2026 17:09
Last modified: 17 Apr 2026 02:01
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Contributors
Author:
Youichiro Higashi
Author:
Norio Takeoka
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