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Learning under Diverse World Views: Model-Based Inference
George J. Mailath and Larry Samuelson
American Economic Review. May 2020, Vol. 110, No. 5: Pages 1464-1501

Learning under Diverse World Views: Model-Based Inference

George J. Mailath1 and Larry Samuelson2

1Department of Economics, University of Pennsylvania, and Research School of Economics, Australian National University (email: )

2Department of Economics, Yale University (email: )

Abstract

People reason about uncertainty with deliberately incomplete models. How do people hampered by different, incomplete views of the world learn from each other? We introduce a model of “ model-based inference.” Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. Unless the differences in agents’ models are trivial, interactions will often not lead agents to have common beliefs or beliefs near the correct-model belief. If the agents’ models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed. (JEL D82, D83)