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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...
Under the Bayesian without Recall (BWR) model of inference, the agents behave rationally with respect to their most recent observations and yet they do not recall their history of past observations and cannot reason about how other agents are making their decisions. This model avoids the complexities of fully rational inference and also provides a behavioral foundation for non-Bayesian updating. We...
We analyze a model of learning and belief formation in networks where agents attempt to maximize their state-dependent utilities by their choice of actions, while being unaware of the true state. 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 decisions of their neighboring agents at...
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