Machine Learning for Strategic Inference

• Tác giả: In-Koo Cho, Jonathan Libgober
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.31 Machine learning
• Thông tin xuất bản: 2021
• Bộ sưu tập: Metadata
• ID: 166053

Tóm tắt

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.