What question did this study set out to answer?

The aim is to examine how AI influences epistemic coherence in specialist populations and identify critical thresholds for irreversible change.

March 28, 2026Open Access

An Epistemic Cascade Model with Directed Percolation Equivalence: AI-Accelerated Population-Specialist Decoupling and the Critical Threshold for Irreversibility

Puntos clave

The aim is to examine how AI influences epistemic coherence in specialist populations and identify critical thresholds for irreversible change.
Developed a two-variable ordinary differential equation (ODE) model for epistemic coherence.
Established equivalence to a two-layer directed percolation process.
Identified a critical threshold for epistemic degradation using numerical analysis.
Derived five testable predictions with confidence bounds.
Identified a critical threshold E_CRIT of approximately 0.44 related to a 56 percentage point partisan gap.
Current US measurements are at 48 percentage points, below E_CRIT.
Predicted crossing of the threshold between 2028 and 2033, with the central estimate at 2030.

Resumen

We present a two-variable ODE governing coupled epistemic coherence in a population-specialist system, and prove it is formally equivalent to a two-layer directed percolation process with an absorbing-state transition at a critical threshold ECRIT. The model identifies a mechanistic pathway by which AI-accelerated epistemic degradation can cross an irreversible threshold, after which specialist epistemic coherence declines without targeted external intervention. ECRIT ≈ 0. 44 corresponds to a Pew Research partisan gap of ~56 percentage points — a directly observable quantity. Current measurements place the US at 48pp, 8 points below ECRIT. The model predicts crossing between 2028–2033 (central estimate ~2030). Five testable predictions are derived with pre-specified confidence bounds. Developed through human-AI collaboration; full disclosure in Author Note. Email author: mo@spiralsystemsai. com

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