Learning under Diverse World Views: Model-Based Inference

George Mailath and Larry Samuelson ()
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Larry Samuelson: Yale University

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: People reason with incomplete models. How do people hampered by different, incomplete views 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 common beliefs, and the correct-model belief will typically lie outside the convex hull of the agents' beliefs. However, if the agents' models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit bizarre idiosyncrasies and their information is widely dispersed.

Keywords: Information aggregation; model-based reasoning (search for similar items in EconPapers)
JEL-codes: D8 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2019-09-30
New Economics Papers: this item is included in nep-dcm, nep-gth and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Learning under Diverse World Views: Model-Based Inference (2020)
Working Paper: Learning under Diverse World Views: Model-Based Inference (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:19-018

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