pragmatism-filter-uniform-across-biology-evaluation-economics

IN derived (depth 13)

Created 2026-06-21T14:21:09+00:00 · Reviewed 2026-06-21T15:37:01+00:00

Pragmatism operates as a uniform selection filter across all ML dimensions — filtering biological inspiration to retain efficiency while discarding robustness, and independently filtering evaluation instruments to be sensitive to performance while deaf to reliability signals — revealing a single systematic distortion rather than domain-specific accidents.

Justifications

SL — Two independent observations of pragmatism's filtering behavior (on inputs and on evaluation) unify into a cross-dimensional selection principle

Antecedents (all must be IN):

IN biological-plausibility-inversely-correlated-with-pragmatic-adoption — Unsupervised deep learning methods are considered more biologically plausible than backpropagation, yet pragmatism's filtering of biological inspiration tends to retain efficiency properties (local connectivity, weight sharing, gating) while discarding robustness properties (redundancy, homeostasis, graceful degradation) — suggesting that the most biologically faithful approaches may be among the least pragmatically favored, and that the biological inspirations most readily adopted are those that enhance capability rather than reliability.
IN crisis-signals-detectable-but-evaluation-deaf — ML's crisis signals are detectable but its evaluation instruments are deaf to them — the persistence of manual feature engineering is a canary for the deeper crisis dynamic, yet standard evaluation methodologies (holdout, k-fold, bootstrap) and standard defenses (dropout, regularization) address only training-test generalization, not the deployment failure modes the canary signals, creating a systematic gap between what the field can detect informally and what it can measure formally.