Bayesian Learning Without Recall
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1601.06103.pdf
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708.55 KB
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Author(s)
Jadbabaie, Ali
Rahimian, Mohammad Amin
Jadbabaie-Moghadam, Ali
Date Issued
November 2016
Journal
IEEE Transactions on Signal and Information Processing over Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Rahimian, M. Amin, and Ali Jadbabaie. “Bayesian Learning Without Recall.” IEEE Transactions on Signal and Information Processing over Networks 3, no. 3 (September 2017): 592–606.
Version
Original manuscript
Abstract
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 actions of their neighboring agents at each time. Successive applications of Bayes rule to the entire history of past observations lead to forebodingly complex inferences: due to lack of knowledge about the global network structure, and unavailability of private observations, as well as third party interactions preceding every decision. Such difficulties make Bayesian updating of beliefs an implausible mechanism for social learning. To address these complexities, we consider a Bayesian without Recall model of inference. On the one hand, this model provides a tractable framework for analyzing the behavior of rational agents in social networks. On the other hand, this model also provides a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for the structure of the action space and utility functions for such agents and investigate the properties of learning, convergence, and consensus in special cases.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TSIPN.2016.2631943
