SlewMatch: Joint-Envelope Diagnostics for Decision-Relevant Prediction Error

A response model can choose correctly at a few preference weights while misrepresenting the decision family between them. We study finite affine costs with a known penalty slope and frozen predicted responses. Classical lower envelopes provide the computational skeleton; their true–predicted joint cells expose which relative errors can affect a choice. The resulting offline diagnostic returns a worst-loss witness and separates common offsets, ineligible comparisons, margin slack and retained regret. Same-window experiments compare sparse, dense, random and structure-guided audits. In a release-response case, three probes miss 423 of 491 intrinsic-shift bound violations, whereas 1,025 uniform probes miss four; conventional hull vertices recover every exact maximum. A separately fitted synthetic capacity task has over thirteen active actions on average and confirms that finding all threshold violations is weaker than recovering worst loss. Standard marginal calibration remains a report, not a policy change. The contribution is a reusable, interpretable error audit with explicit classical antecedents, paired computational comparisons and two synthetic structural tests, rather than a new envelope algorithm or learned physical law.