Modeling Machine Learning: A Cognitive Economic Approach

Andrew Caplin, Daniel Martin and Philip Marx

No 30600, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We apply methodological innovations from cognitive economics that were designed to study human cognition to instead better understand machine learning. We first show that the folk theory of machine learning – that an algorithms learns optimally to minimize the loss function used in training – rests on a shaky foundation. We then identify a path forward by translating ideas from the costly learning branch of cognitive economics. We find that changes in the loss function impact learning just as they might if the algorithm was a rational human being who found learning costly according to a revealed pseudo-cost function that may or may not correspond to actual resource costs. Our approach can be leveraged to determine more effective loss functions given a third party’s objective, be it a firm or a policy maker.

JEL-codes: C0 D80 (search for similar items in EconPapers)
Date: 2022-10
New Economics Papers: this item is included in nep-big and nep-cmp
Note: TWP
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