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This is a Quantitative Study study.

September 27, 2025Open Access

Dynamic Inverse Optimization under Drift and Shocks: Theory, Regret Bounds, and Applications

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This study develops a dynamic inverse optimization framework for capturing hidden preferences, suggesting its utility across various sectors.

The study identifies conditions for existence and uniqueness of recovered parameters in dynamic inverse optimization.
Dynamic inverse optimization shows how organizations can adapt to gradual drift and sudden shocks in decision environments.
Methodologically, the framework includes a drift-aware estimator with finite-sample guarantees on recovery accuracy.
Computational experiments reveal variations in resilience and vulnerability across sectors like healthcare, logistics, and finance.

Jinho Cha (2025) studied this question.

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This study develops a dynamic inverse optimization framework for capturing hidden preferences, suggesting its utility across various sectors.