Machine Learning for Strategic Inference

Inkoo Cho and Jonathan Libgober

Papers from arXiv.org

Abstract: We study interactions between strategic players and markets whose behavior is guided by an algorithm. Algorithms use data from prior interactions and a limited set of decision rules to prescribe actions. While as-if rational play need not emerge if the algorithm is constrained, it is possible to guide behavior across a rich set of possible environments using limited details. Provided a condition known as weak learnability holds, Adaptive Boosting algorithms can be specified to induce behavior that is (approximately) as-if rational. Our analysis provides a statistical perspective on the study of endogenous model misspecification.

Date: 2021-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-gth
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