Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift
Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or switching costs. As operating conditions change, when should such decisions be re-optimized? We study this question for stochastic optimization when the objective's functional form is known but the decision maker's trade-offs are encoded by an unknown preference parameter. Standard distribution-shift tests are poorly aligned with this goal: they can flag detectable yet decision-irrelevant changes without determining whether the incumbent decision has become materially suboptimal. We propose \texttt{RADAR} (Regret-based Assessment of Decision Adequacy and Risk), a decision-focused fr...
| Authors | Minxing Zheng, Holly Wiberg, Shixiang Zhu |
|---|---|
| Primary category | stat.ML |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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