(Non-)Bayesian learning without recall

Mohammad Amin Rahimian, Pooya Molavi, Ali Jadbabaie

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


This work is concerned with the problem of social learning. A network of agents attempt to learn some unknown state of the world which is drawn by nature from a finite set. The sources of information available to the agents are their own private observations as well as the beliefs of their neighboring agents in the social structure in which they interact. The rational approach to the problem of social learning is for each agent to refine her opinion, by using the Bayes' rule to incorporate her neighbors' beliefs and own private signals over time. However, repeated applications of the Bayes' rule in social networks can become computationally intractable, partly due to the fact that each agent needs to use her local data that is increasing over time and make inferences about the global network structure. The inherent complexity of the Bayesian approach has lead to the consideration of behavioral, non-Bayesian updates, where each agent instead of processing all new information in the optimum Bayesian way uses simple rules such as linear or convex combinations and forms updated beliefs. In this paper, it is shown that by replicating the rule that maps the agents' common prior to their Bayesian posterior at the initial time-step for all future steps, one can derive a memoryless Bayesian update that has a log-linear format and can serve as a theoretical justification for some of the non-Bayesian rules suggested in the literature. Convergence and learning under the derived rules are also investigated, and it is shown that while two communicating agents always learn the true state, the beliefs may fail to converge in the general network setting. The proposed approach has the advantage that while preserving some features of the Bayesian inference, they are made tractable.

Original languageEnglish (US)
Article number7040286
Pages (from-to)5730-5735
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Issue numberFebruary
StatePublished - Jan 1 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


Dive into the research topics of '(Non-)Bayesian learning without recall'. Together they form a unique fingerprint.

Cite this