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Machine Learning for Strategic Inference

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  • In-Koo Cho
  • Jonathan Libgober

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.

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  • In-Koo Cho & Jonathan Libgober, 2021. "Machine Learning for Strategic Inference," Papers 2101.09613, arXiv.org.
    • Handle: RePEc:arx:papers:2101.09613
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