可移除缺陷：故意不足的经济学与局限性
Removable Defects: The Economics and Limits of Deliberate Deficiency
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中文总结 AI 辅助
研究故意不足的经济学与局限性，将其视为设计变量，给出保留不足的优势条件及可移除性刻画，还分析观察与容量缺陷差异，探测器可从致命类别学习，综合多种理论成果。
中文摘要 AI 辅助
专家会容忍通才所不容的盲点。通常这被视为要最小化的成本。我们将其视为一个设计变量：一种不足可以保留，因为它有回报，并且在极少数致命情况下可按需移除，通过路由到补偿渠道。我们给出三个结果。首先，给出一个优势条件，在此条件下保留不足是一种可计算的经济状况；从结构上看，它是应用于能力差距的埃利希 - 贝克尔市场与自我保险边际，探测器作为汤森德成本状态验证技术。其次，对可移除性进行了双边刻画。一个耦合引理表明，当不足是感知的粗化时，没有切换能区分利弊，由此得出一个逆命题（一个混淆的探测器赚取零溢价，并且在乘法动态下，任何坚持正溢价的缺陷内策略会导致长期负增长）和一个可实现性结果（缺陷外的探测器赚取正溢价）。一起考虑具有严重性上限或误报率为\(O(1/L)\)的结构化不确定性类别：一个缺陷若探测器相关区分在限制下幸存且优势条件成立，则可盈利移除；溢价是该类ROC集在经济价格向量处支持函数。第三，观察缺陷和容量缺陷在是否能通过获取部署分布来挽救它们方面存在差异；差距分解为交叉泄漏加上一个闭合缺陷，并且每任务随机化可买回后者，而非前者。探测器可从已声明的致命类别中以损失严重性的线性训练成本（至多一个对数因子）学习得到。这些结果综合了周的拒绝选项、破产下的凯利增长和选择性预测。
英文摘要
A specialist tolerates blind spots that a generalist does not. Usually this is treated as a cost to be minimized. We treat it as a design variable: a deficiency can be kept because it pays and removed on demand in the rare situation where it would be fatal, by routing to a compensation channel. We give three results. First, an advantage condition under which keeping the deficiency is a computable economic position; structurally it is the Ehrlich-Becker market-vs-self-insurance margin applied to a competence gap, with the detector as a Townsend costly-state-verification technology. Second, a two-sided characterization of removability. A coupling lemma shows that when the deficiency is a coarsening of perception, no switch can separate benefit from harm, yielding a converse (a confounded detector earns zero premium, and any within-defect policy insisting on positive premium is driven, under multiplicative dynamics, to negative long-run growth) and an achievability result (a detector outside the deficiency earns a positive premium). Together, over structured uncertainty classes with severity capped or miss rate O(1/L): a defect is profitably removable iff the detector-relevant distinction survives the restriction and the advantage condition holds; the premium is the support function of the class's ROC set at an economic price vector. Third, observation defects and capacity defects differ exactly on whether access to the deployment distribution rescues them; the gap decomposes as cross-leak plus a closure deficit, and per-task randomization buys back the latter, never the former. The detector can be learned from declared fatal categories at a training bill linear in loss severity (up to a log factor). The results synthesize Chow's reject option, Kelly growth under ruin, and selective prediction.
