Determinism versus uncertainty : Examining the worst-case expected performance of data-driven policies
Journal article
Tian, Xuecheng, Wang, Shuaian, Laporte, Gilbert and Yang, Ying. (2024). Determinism versus uncertainty : Examining the worst-case expected performance of data-driven policies. European Journal of Operational Research. 318(1), pp. 242-252. https://doi.org/10.1016/j.ejor.2024.04.031
Authors | Tian, Xuecheng, Wang, Shuaian, Laporte, Gilbert and Yang, Ying |
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Abstract | This paper explores binary decision making, a critical domain in areas such as finance and supply chain management, where decision makers must often choose between a deterministic-cost option and an uncertain-cost option. Given the limited historical data on the uncertain cost and its unknown probability distribution, this research aims to ascertain how decision makers can optimize their decisions. To this end, we evaluate the worst-case expected performance of all possible data-driven policies, including the sample average approximation policy, across four scenarios differentiated by the extent of knowledge regarding the lower and upper bounds of the first moment of the uncertain cost distribution. Our analysis, using worst-case expected absolute regret and worst-case expected relative regret metrics, consistently shows that no data-driven policy outperforms the straightforward strategy of choosing either a deterministic-cost or uncertain-cost option in these scenarios. Notably, the optimal choice between these two options depends on the specific lower and upper bounds of the first moment. Our research contributes to the literature by revealing the minimal worst-case expected performance of all possible data-driven policies for binary decision-making problems. |
Keywords | Decision analysis ; Data-driven optimization ; Sample average approximation ; Worst-case expected performance |
Year | 01 Jan 2024 |
Journal | European Journal of Operational Research |
Journal citation | 318 (1), pp. 242-252 |
Publisher | Elsevier Science BV |
ISSN | 0377-2217 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ejor.2024.04.031 |
Open access | Published as non-open access |
Research or scholarly | Research |
Page range | 242-252 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 27 Apr 2024 |
Publication process dates | |
Accepted | 26 Apr 2024 |
Deposited | 04 Jul 2024 |
Additional information | © 2024 Elsevier B.V. All rights reserved. |
Place of publication | Netherlands |
https://acuresearchbank.acu.edu.au/item/90qvv/determinism-versus-uncertainty-examining-the-worst-case-expected-performance-of-data-driven-policies
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