CORTEXA
← Browse
arxivecon.EMcs.AIcs.LGstat.ML2026-07-13

Removable Defects: The Economics and Limits of Deliberate Deficiency

Cheng Qian

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.

View free PDFSource page

Related papers

arxivstat.MLcs.AIcs.LGecon.EM2026-07-24

CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference

Jiyuan Tan, Vasilis Syrgkanis

Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation. A common approach is to close the research loop with a large language model (LLM) reviewer. However, such reviewers remain empirically unreliable…

View free PDFSource page
arxivstat.MLcs.AIcs.LG2026-07-16

Subjective Risk Decomposition: A New View for Uncertainty Quantification

Raghad Alamri, Michele Caprio, Gavin Brown

We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling decisions. We show how epistemic and aleatoric uncertainty measures can be derived via de…

View free PDFSource page
arxivcs.LGcs.AIstat.ML2026-07-22

When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers

Tong Zhang, Junhao Hu, Yun Peng, Tao Xie

When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm? We answer with four findings from controlled populations on group word problems. (1) The budget law: free training installs a linear computation frontier, a mechanism that so…

View free PDFSource page