Published July 11, 2022
| Published + Submitted
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Learning in Repeated Interactions on Networks
- Creators
- Huang, Wanying
- Strack, Philipp
- Tamuz, Omer
Abstract
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.
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).Attached Files
Published - 3490486.3538307.pdf
Submitted - 2112.14265.pdf
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Additional details
- Eprint ID
- 115371
- Resolver ID
- CaltechAUTHORS:20220707-170550926
- Alfred P. Sloan Foundation
- 419427
- Simons Foundation
- 2018397
- Binational Science Foundation (USA-Israel)
- DMS-1944153
- NSF
- Created
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2022-07-07Created from EPrint's datestamp field
- Updated
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2022-07-27Created from EPrint's last_modified field