  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.
