A Deficiency of the Predict-Then-Optimize Framework: Decreased Decision Quality with Increased Data Size

Shuaian Wang and Xuecheng Tian ()
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Shuaian Wang: Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, China
Xuecheng Tian: Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, China

Mathematics, 2023, vol. 11, issue 15, 1-9

Abstract: This paper presents an analysis of the decision quality of the predict-then-optimize (PO) framework, an extensively used prescriptive analytics framework in uncertain optimization problems. Our primary aim is to investigate whether an increase in data size invariably leads to better decisions within the PO framework. We focus our analysis on two contextual stochastic optimization problems—one with a non-linear objective function and the other with a linear objective function—under the PO framework. The novelty of our work lies in uncovering a previously unknown relationship: the decision quality can deteriorate with increasing data size in the non-linear case and exhibit non-monotonic behavior in the linear case. These findings highlight a potential pitfall of the PO framework and constitute our main contribution to the field, offering invaluable insights for both researchers and practitioners.

Keywords: data-driven optimization; prescriptive analytics; predict-then-optimize; limited data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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