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The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness

Jon Kleinberg (), Annie Liang () and Sendhil Mullainathan ()
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Jon Kleinberg: Department of Computer Science, Cornell University
Annie Liang: Department of Economics, University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

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? Deï¬ ning 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 signiï¬ cant structure in the problem that existing theories have yet to capture.

Pages: 46 pages
Date: 2017-08-09, Revised 2017-08-09
New Economics Papers: this item is included in nep-big
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