Machine Learning for Strategic Inference

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Date

23 janvier 2021

Type de document
Périmètre
Identifiant
  • 2101.09613
Collection

arXiv

Organisation

Cornell University




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In-Koo Cho et al., « Machine Learning for Strategic Inference », arXiv - économie


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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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