Learning What to Want: Data-Driven Microfoundations

75 Pages Posted: 19 Nov 2014

See all articles by Nisheeth Srivastava

Nisheeth Srivastava

University of California, San Diego (UCSD)

Paul Schrater

University of Minnesota - Twin Cities

Date Written: November 17, 2014

Abstract

We have developed a method for directly learning relative preferences from histories of comparison information without an intermediate utility computation. Our method infers preferences that are rational in a psychological sense, where agent choices result from Bayesian inference of what to do from observable inputs. We further characterize conditions wherein it is appropriate for modelers to describe relative preferences as scalar utilities, and illustrate the importance of option availability in supplying auxiliary information by explaining all major categories of context effects, as well as predicting novel context effects. Applying our theory to predicting choices under uncertainty leads to good fits with empirical data and endogenous explanations for a number of economic behaviors. By retrieving economic rationality as a special case of psychologically rational preference formation, this work clarifies theoretical connections between economic and psychological definitions of rationality.

JEL Classification: D11, D83, D00

Suggested Citation

Srivastava, Nisheeth and Schrater, Paul, Learning What to Want: Data-Driven Microfoundations (November 17, 2014). Available at SSRN: https://ssrn.com/abstract=2526540 or http://dx.doi.org/10.2139/ssrn.2526540

Nisheeth Srivastava (Contact Author)

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

Paul Schrater

University of Minnesota - Twin Cities ( email )

420 Delaware St. SE
Minneapolis, MN 55455
United States

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