Disturbed bayesian learning in multiagent systems: Improving our understanding of its capabilities and limitations

Petar M. Djurić, Yunlong Wang

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

In this article, we study social networks of agents, where agents learn not only from private signals (i.e., signals only available to the agents receiving them), but from other agents too. Based on all the available information, agents modify their beliefs in events of interest and make decisions on which actions to take based on the beliefs. In doing so, they optimize functions that reflect some (cumulative) reward. This problem has been studied in various disciplines including control theory, operations research, artificial intelligence, game theory, information theory, economics, statistics, computer science, and signal processing.

Original languageEnglish (US)
Article number6153148
Pages (from-to)65-76
Number of pages12
JournalIEEE Signal Processing Magazine
Volume29
Issue number2
DOIs
StatePublished - Mar 2012

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