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Machine Learning for Strategic Inference

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Document pages: 42 pages

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

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