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Feature Misspecification in Sequential Learning Problems

Author

Listed:
  • Dohyun Ahn

    (Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong)

  • Dongwook Shin

    (HKUST Business School, Clear Water Bay, Kowloon, Hong Kong)

  • Assaf Zeevi

    (Graduate School of Business, Columbia University, New York, New York 10025)

Abstract

We consider a class of sequential learning problems where a decision maker must learn the unknown statistical characteristics of a finite set of alternatives (or systems) using sequential sampling to ultimately select a subset of “good” alternatives. A salient feature of our problem is that system performance is governed by a set of features . The decision maker postulates the dependence on these features to be linear, but this model may not precisely represent the true underlying system structure. We show that this misspecification, if not managed properly, can lead to suboptimal performance because of a phenomenon identified as sample-selection endogeneity . We propose a prospective sampling principle—a new approach that eliminates the adverse effects of misspecification as the number of samples grows large. The proposed principle applies across a very general class of widely used sampling policies, enjoys strong asymptotic performance guarantees, and exhibits effective finite-sample performance in numerical experiments.

Suggested Citation

  • Dohyun Ahn & Dongwook Shin & Assaf Zeevi, 2025. "Feature Misspecification in Sequential Learning Problems," Management Science, INFORMS, vol. 71(5), pages 4066-4086, May.
    • Handle: RePEc:inm:ormnsc:v:71:y:2025:i:5:p:4066-4086
    DOI: 10.1287/mnsc.2022.00328
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