Learning under Diverse World Views: Model-Based Inference

George Mailath and Larry Samuelson

American Economic Review, 2020, vol. 110, issue 5, 1464-1501

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-codes: D82 D83 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (22)

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Working Paper: Learning under Diverse World Views: Model-Based Inference (2019)
Working Paper: Learning under Diverse World Views: Model-Based Inference (2019)
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DOI: 10.1257/aer.20190080

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