发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布偏移下决策是否需重新优化的问题，提出以决策为中心的框架\texttt{RADAR}，可区分有害与无害偏移，在多任务中表现优于决策无关替代方法。
AI 中文摘要
部署后的决策通常经过一次优化后便被保留，因为更新会产生操作、监管或切换成本。随着运营条件变化，这类决策应何时重新优化？我们针对随机优化研究该问题，此时目标函数形式已知，但决策者的权衡由未知偏好参数编码。标准分布偏移检验与该目标契合度低：它们会标记可检测但与决策无关的变化，却未确定现有决策是否已实质性次优。我们提出\texttt{RADAR}（决策充分性与风险的基于遗憾评估），这是一个以决策为中心的框架，利用逆优化推断潜在偏好，并检验部署决策在当前分布下的最优性差距。通过聚焦遗憾，\texttt{RADAR}会忽略与决策无关的偏移，同时检测出值得重新优化的变化。我们开发了两样本和序列变点程序，并建立了第一类错误和功效的渐近保证。在合成优化问题、半合成容量分配任务及警区规划中，\texttt{RADAR}相比与决策无关的替代方法，能更可靠地区分有害偏移与无害偏移。
英文摘要
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.
