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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… Click to show full 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? Dei¬ 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 signii¬ cant structure in the problem that existing theories have yet to capture.
Journal Title: Journal of Economic Dynamics and Control
Year Published: 2019
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