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
When testing a theory, we should ask not just whether its predictions match what we see in the data, but also about its â€œcompletenessâ€ : 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 domainâ€”the human perception and generation of randomnessâ€”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
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Working Paper: The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness (2017)
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