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Learning without Recall: A Case for Log-Linear Learning
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.
Learning without Recall by Random Walks on Directed Graphs
Each agent might not be able to distinguish the true state based only on her private observations.
Switching to Learn
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set.
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