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Learning in Repeated Interactions on Networks

Huang, Wanying and Strack, Philipp and Tamuz, Omer (2022) Learning in Repeated Interactions on Networks. In: Proceedings of the 23rd ACM Conference on Economics and Computation. Association for Computing Machinery , New York, NY, p. 325. ISBN 978-1-4503-9150-4.

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We study how long-lived, rational, exponentially discounting agents learn in a social network. In every period, each agent observes the past actions of his neighbors, receives a private signal, and chooses an action with the objective of matching the state. Since agents behave strategically, and since their actions depend on higher order beliefs, it is difficult to characterize equilibrium behavior. Nevertheless, we show that regardless of the size and shape of the network, and the patience of the agents, the equilibrium speed of learning is bounded from above by a constant that only depends on the private signal distribution.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Strack, Philipp0000-0002-7960-9243
Tamuz, Omer0000-0002-0111-0418
Additional Information:© 2022 Copyright held by the owner/author(s). Philipp Strack was supported by a Sloan fellowship. Omer Tamuz was supported by a grant from the Simons Foundation (#419427), a Sloan fellowship, a BSF award (#2018397) and a National Science Foundation CAREER award (DMS-1944153).
Funding AgencyGrant Number
Alfred P. Sloan FoundationUNSPECIFIED
Simons Foundation419427
Binational Science Foundation (USA-Israel)2018397
Subject Keywords:social learning
Record Number:CaltechAUTHORS:20220707-170550926
Persistent URL:
Official Citation:Wanying Huang, Philipp Strack, and Omer Tamuz. 2022. Learning in Repeated Interactions on Networks. In Proceedings of the 23rd ACM Conference on Economics and Computation (EC ’22), July 11–15, 2022, Boulder, CO, USA. ACM, New York, NY, USA, 1 page.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115371
Deposited By: George Porter
Deposited On:07 Jul 2022 19:30
Last Modified:27 Jul 2022 17:55

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