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March 4, 20260 citationsOpen Access

The Rigidity Trap: Differentiation, Reintegration Failure, and Collapse Dynamics in Scaling Adaptive Systems

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BSBin Seol

Key Points

  • To explore the Rigidity Trap and its effects on adaptive systems as they scale and face environmental changes.
  • Formalizing the Rigidity Trap through theoretical frameworks
  • Identifying scaling-induced differentiation and the Transition–Collapse Fork
  • Conducting toy computational experiments to simulate system collapse dynamics
  • Demonstrated the self-reinforcing loop leading to catastrophic failure in adaptive systems
  • Identified O(n²) conflict channels as a function of system complexity
  • Connected findings to Recovery Theory and the Coherence Maximization Paradox

Abstract

This paper formalizes the Rigidity Trap — a dynamical pathway through which high-performing adaptive systems systematically dismantle their own alarm mechanisms, creating brittle architectures that fail catastrophically upon environmental shift. We identify three interconnected phenomena: (1) scaling-induced differentiation, where growing system complexity produces O (n²) conflict channels and governance saturation at a provable threshold n* = O (CM) ; (2) the Transition–Collapse Fork, a sigmoid-gated bifurcation where high rigidity suppresses alarm sensitivity and closes the transition path, making collapse the default trajectory under sustained success; and (3) the Rigidity Trap proper, a self-reinforcing feedback loop of success → rigidity → alarm suppression → undetected mismatch → catastrophic failure. We derive conditions under which endogenous micro-instability maintenance prevents trap entry, demonstrate the mechanism through toy computational experiments showing accelerated endurance collapse in success-exposed systems, and connect the trap to Recovery Theory's Coherence Maximization Paradox across individual, team, and organizational scales. The framework bridges organizational theory, continual learning, and self-organized criticality, providing quantitative predictions for when and how successful systems become vulnerable to sudden failure.

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Cite This Study

Bin Seol (2026) studied this question.

synapsesocial.com/papers/69a7cce8d48f933b5eed8c87https://doi.org/10.5281/zenodo.18832270
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