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Learning without recall: A case for log-linear learning

Rahimian, MA and Jadbabaie, A (2015) Learning without recall: A case for log-linear learning. In: UNSPECIFIED.

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Abstract

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the beliefs of their neighboring agents at each time. Fully rational agents would successively apply Bayes rule to the entire history of observations. This leads to forebodingly complex inferences due to lack of knowledge about the global network structure that causes those observations. To address these complexities, we consider a "Learning without, Recall" model, which in addition to providing a tractable framework for analyzing the behavior of rational agents in social networks, can also provide a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for time-varying priors of such agents and how this choice affects learning and its rate.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Rahimian, MARAHIMIAN@pitt.eduRAHIMIAN0000-0001-9384-1041
Jadbabaie, A
Date: 1 October 2015
Date Type: Publication
Journal or Publication Title: IFAC-PapersOnLine
Volume: 28
Number: 22
Page Range: 46 - 51
Event Type: Conference
DOI or Unique Handle: 10.1016/ifacol.2015.10.305
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Refereed: Yes
Date Deposited: 17 Aug 2020 16:53
Last Modified: 07 Sep 2020 16:55
URI: http://d-scholarship.pitt.edu/id/eprint/39615

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