Learning under Diverse World Views: Model-Based Inference

58 Pages Posted: 3 Oct 2019

See all articles by George J. Mailath

George J. Mailath

University of Pennsylvania - Department of Economics; Research School of Economics, ANU

Larry Samuelson

Yale University - Department of Economics; Yale University - Cowles Foundation

Multiple version iconThere are 2 versions of this paper

Date Written: September 30, 2019

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 find 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: information aggregation, model-based reasoning

JEL Classification: D8

Suggested Citation

Mailath, George J. and Samuelson, Larry, Learning under Diverse World Views: Model-Based Inference (September 30, 2019). PIER Working Paper No. 19-018, Available at SSRN: https://ssrn.com/abstract=3463205 or http://dx.doi.org/10.2139/ssrn.3463205

George J. Mailath (Contact Author)

University of Pennsylvania - Department of Economics ( email )

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HOME PAGE: http://web.sas.upenn.edu/gmailath/

Research School of Economics, ANU ( email )

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Larry Samuelson

Yale University - Department of Economics ( email )

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Yale University - Cowles Foundation

Box 208281
New Haven, CT 06520-8281
United States

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