Learning without Recall: A Case for Log-Linear Learning

30 Sep 2015Mohammad Amin RahimianAli Jadbabaie

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... (read more)

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