The Recursive Reliability Effect - Gaconnet

This paper introduces the Recursive Reliability Effect as the named phenomenon for a structural mechanism confirmed across multiple independent research traditions but not previously unified, formally derived, or named: human systems under structural load cannot accurately self-assess, and the degradation of self-assessment accuracy is recursive rather than linear. The effect is established by converging evidence from cognitive load theory (Sweller, 1988), clinical self-assessment research (Davis et al., 2006; Eva Webster et al., 2018), and the ACE study’s dose-response compounding findings (Felitti et al., 1998; N = 17,000). The established literature confirms that self-assessment under load is systematically unreliable, that the unreliability does not improve with expertise, that the degradation compounds rather than remaining static, and that external physiological measurement is necessary for accuracy. The novel contributions of this paper are: (1) the unified naming of the phenomenon; (2) the formal derivation of the recursive mechanism from two published laws within the Recursive Sciences framework—the Law of Recursion (Gaconnet, 2026a) and the Law of Obligated Systems (Gaconnet, 2026b); (3) Monte Carlo simulation at 10,000 cases quantifying the specific rates within the near-capacity executive population (81.4% domain mismatch, 95% CI: 80.7–82.2%; 73.0% depth minimization, CI 72.1–73.9%; 61.1% compound risk, CI 60.1–62.0%); (4) the formalization of three manifestation trajectories—acute collapse, chronic degradation equilibrium, and environmental distortion; (5) the structural invariance argument establishing the decision-maker’s self-assessment as the one evaluation input universally present and universally unverified across all professional risk assessment scenarios; and (6) five falsification criteria, each genuinely testable. Population-scale empirical data from the Psychosocial Pressure Index—1,179,446 raw data points from 28 independent streams, 406 measurement points over 194 days, 20,844,229 normalized dimensional observations—demonstrates structural decoupling of collective self-assessment from material reality (T1-T3 r = 0.056) with recursive lock-in of the resulting perception-reality gap (lag-1 autocorrelation r = 0.984), 100% chaotic dynamics across 357 Lyapunov measurements, 21 self-fulfilling dynamics events where reality degraded toward perception, and coupled dimensional degradation (threat-resilience r = −0.895). The general principle for interrupting the recursive loop—external structural measurement that does not take the self-report as primary input—is stated as a scientific implication consistent with the established literature’s recommendation of physiological and biometric measurement for accuracy under stress. Specific implementation methodology is proprietary. Keywords: recursive reliability, self-assessment degradation, performance-embodiment divergence, inverse reliability, structural load, recursive sciences, Monte Carlo validation, cognitive systems engineering, fiduciary risk assessment, population pressure dynamics