Learning under Diverse World Views: Model-Based Inference

George Mailath and Larry Samuelson ()
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Larry Samuelson: Cowles Foundation, Yale University, https://economics.yale.edu/people/faculty/larry-samuelson

No 2161R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: People reason about uncertainty with deliberately incomplete models, including only the most relevant variables. 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. We nd that unless the differences in agents' models are trivial, interactions will often not lead agents to have common beliefs, and indeed 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 some bizarre idiosyncrasies and their information is widely dispersed.

Keywords: Wisdom of the Crowd; Information aggregation; Common prior; NonBayesian updating (search for similar items in EconPapers)
JEL-codes: D8 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2019-01, Revised 2019-09
New Economics Papers: this item is included in nep-hpe and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in American Economic Review, (May 2020), 110(5): 1464-1501

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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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