The Theory Is Predictive, but Is It Complete? An Application to Human Perception of Randomness

20 Pages Posted: 16 Aug 2017 Last revised: 25 Apr 2019

See all articles by Jon Kleinberg

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

When testing a theory, we should ask not just whether its predictions match what we see in the data, but also about its “completeness”: how much of the predictable variation in the data does the theory capture? Defining completeness is conceptually challenging, but we show how methods based on machine learning can provide tractable measures of completeness. We also identify a model domain—the human perception and generation of randomness — where measures of completeness can be feasibly analyzed; from these measures we discover there is significant structure in the problem that existing theories have yet to capture.

Keywords: Prediction, Randomness Perception, Theory Completeness

JEL Classification: C10, C52, C92, D9

Suggested Citation

Kleinberg, Jon and Liang, Annie and Mullainathan, Sendhil, The Theory Is Predictive, but Is It Complete? An Application to Human Perception of Randomness (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

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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