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Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift — arXiv:2608.29465

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29465
License (as archived): CC0-1.0
Archived: 2026-10-17T08:39:50.372Z
SHA-256 of the archived text: f57a257ba00dc89cb041b93e747fe961ef98261e8373dd42255147fe0463abad
Stable URL of this page: https://metraipolis.com/documento/dc_047a0d759d97

How to cite

Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift — arXiv:2608.29465 — arXiv (en), https://arxiv.org/abs/2608.29465 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_047a0d759d97 (Metraipolis archive dc_047a0d759d97, sha256 f57a257ba00dc89cb041b93e747fe961ef98261e8373dd42255147fe0463abad)

BibTeX

@misc{dc047a0d759d97,
  title = {Deciding When to Decide: Testing Operational Suboptimality Under Distributional Shift — arXiv:2608.29465},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29465},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_047a0d759d97; sha256 f57a257ba00dc89cb041b93e747fe961ef98261e8373dd42255147fe0463abad},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29465. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 framework that uses inverse optimization to infer latent preferences and tests the deployed decision's optimality gap under the current distribution. By targeting regret, \texttt{RADAR} ignores decision-irrelevant shifts while detecting changes that warrant re-optimization. We develop two-sample and sequential changepoint procedures and establish asymptotic guarantees for Type-I error and power. Across synthetic optimization problems, a semi-synthetic capacity allocation task, and police-zone planning, \texttt{RADAR} more reliably distinguishes harmful from harmless shifts than decision-agnostic alternatives.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2608.29465 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:13:26.911Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).