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Feasible Joint Posterior Beliefs

Arieli, Itai and Babichenko, Yakov and Sandomirskiy, Fedor and Tamuz, Omer (2020) Feasible Joint Posterior Beliefs. In: Proceedings of the 21st ACM Conference on Economics and Computation. Association for Computing Machinery , New York, NY, p. 643. ISBN 978-1-4503-7975-5. https://resolver.caltech.edu/CaltechAUTHORS:20200615-155012317

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Abstract

We study the set of possible joint posterior belief distributions of a group of agents who share a common prior regarding a binary state and who observe some information structure. Our main result is that, for the two agent case, a quantitative version of Aumann's Agreement Theorem provides a necessary and sufficient condition for feasibility. For any number of agents, a related "no trade" condition likewise provides a characterization of feasibility. We use our characterization to construct joint belief distributions in which agents are informed regarding the state, and yet receive no information regarding the other's posterior. We study a related class of Bayesian persuasion problems with a single sender and multiple receivers, and explore the extreme points of the set of feasible distributions.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3391403.3399505DOIArticle
https://arxiv.org/abs/2002.11362arXivDiscussion Paper
ORCID:
AuthorORCID
Arieli, Itai0000-0001-8663-5776
Babichenko, Yakov0000-0002-6970-1601
Sandomirskiy, Fedor0000-0001-9886-3688
Tamuz, Omer0000-0002-0111-0418
Additional Information:© 2020 Copyright held by the owner/author(s). The paper greatly benefited from multiple suggestions and comments of our colleagues. We are grateful (in alphabetic order) to Kim Border, Laura Doval, Sergiu Hart, Kevin He, Aviad Heifetz, Yuval Heller, Matthew Jackson, Benny Moldovanu, Jeffrey Mensch, Alexander Nesterov, Michael Ostrovsky, Thomas Palfrey, Luciano Pomatto, Marco Scarsini, Eilon Solan, Gabriel Ziegler, and seminar participants at Bar-Ilan Univeristy, Caltech, HSE St. Petersburg, Technion, Stanford, and UC San Diego. Itai Arieli is supported by the Ministry of Science and Technology (#2028255). Yakov Babichenko is supported by a BSF award (#2018397). Fedor Sandomirskiy is partially supported by the Lady Davis Foundation, by Grant 19-01-00762 of the Russian Foundation for Basic Research, by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (#740435), and by the Basic Research Program of the National Research University Higher School of Economics. Omer Tamuz is supported by a grant from the Simons Foundation (#419427) and by a BSF award (#2018397).
Funders:
Funding AgencyGrant Number
Ministry of Science and Technology (Israel)2028255
Binational Science Foundation (USA-Israel)2018397
Lady Davis FoundationUNSPECIFIED
Russian Foundation for Basic Research19-01-00762
European Research Council (ERC)740435
National Research University Higher School of EconomicsUNSPECIFIED
Simons Foundation419427
Binational Science Foundation (USA-Israel)2018397
Subject Keywords:Bayesian updating; Bayesian games; Bayesian persuasion; splitting lemma; no-trade theorem; extreme points; social learning; joint distribution; belief hierarchy
Record Number:CaltechAUTHORS:20200615-155012317
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200615-155012317
Official Citation:Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy, and Omer Tamuz. 2020. Feasible Joint Posterior Beliefs. In Proceedings of the 21st ACM Conference on Economics and Computation (EC ’20), July 13–17, 2020, Virtual Event, Hungary. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3391403.3399505
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103930
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:15 Jun 2020 23:00
Last Modified:03 Mar 2021 20:09

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