Measuring the Completeness of Theories

38 Pages Posted: 16 Aug 2017 Last revised: 27 Jan 2020

See all articles by Drew Fudenberg

Drew Fudenberg

Massachusetts Institute of Technology (MIT)

Jon Kleinberg

Cornell University - Department of Computer Science

Annie Liang

University of Pennsylvania - Department of Economics

Sendhil Mullainathan

Harvard University - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: April 23, 2019

Abstract

To evaluate how well economic models predict behavior it is important to have a measure of how well any theory could be expected to perform. We provide a measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We evaluate the completeness of leading theories in three applications---assigning certainty equivalents to lotteries, initial play in games, and human generation of random sequences---and show that this approach reveals new insights. We also illustrate how and why our completeness measure varies with the experiments considered, for example with the choice of lotteries used to evaluate risk preferences, and explain how our completeness measure can help guide the development of new theories.

Keywords: Prediction, Randomness Perception, Theory Completeness

JEL Classification: C10, C52, C92, D9

Suggested Citation

Fudenberg, Drew and Kleinberg, Jon and Liang, Annie and Mullainathan, Sendhil, Measuring the Completeness of Theories (April 23, 2019). PIER Working Paper No. 18-010. Available at SSRN: https://ssrn.com/abstract=3018785 or http://dx.doi.org/10.2139/ssrn.3018785

Drew Fudenberg

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Jon Kleinberg

Cornell University - Department of Computer Science ( email )

4130 Upson Hall
Ithaca, NY 14853-7501
United States

Annie Liang (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States

Sendhil Mullainathan

Harvard University - Department of Economics ( email )

Littauer Center
Cambridge, MA 02138
United States
617-496-2720 (Phone)
617-495-7730 (Fax)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States
617-588-1473 (Phone)
617-876-2742 (Fax)

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