Feature Misspecification in Sequential Learning Problems

Dohyun Ahn (), Dongwook Shin () and Assaf Zeevi ()
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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

Management Science, 2025, vol. 71, issue 5, 4066-4086

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.

Keywords: sequential learning; ordinal optimization; model misspecification; maximum likelihood estimation (search for similar items in EconPapers)
Date: 2025
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