  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. Fully rational agents
would successively apply Bayes rule to the entire history of observations. This
leads to forebodingly complex inferences due to lack of knowledge about the
global network structure that causes those observations. To address these
complexities, we consider a Learning without Recall model, which in addition to
providing a tractable framework for analyzing the behavior of rational agents
in social networks, can also provide a behavioral foundation for the variety of
non-Bayesian update rules in the literature. We present the implications of
various choices for time-varying priors of such agents and how this choice
affects learning and its rate.
