Crowd Dynamics and Behavior

The document critiques the evaluation of systems (AI, economics, cognition) based on visible performance, showing that functional appearance is often mistaken for actual intelligence, value, or stability. It argues that internal structure—constraints, regulation, and dynamics—precedes function, and that surface metrics lead to fragile overestimations. The CBD (Constraints-Equilibrium-Dynamics) framework is proposed as a unifying principle for judging the consistency, robustne

The Collective Psychodynamic Formula (Extended CBD) provides a universal framework for analyzing human and complex systems by modeling how memory, emotional energy, thresholds, time, and constraints interact to produce stability or systemic shifts. Rather than predicting events, it offers a structured reading of latent tensions and divergence activation that explain why systems persist, reorganize, or abruptly transition. Its strength lies in its cross-disciplinary applicabil

The CBD formula describes collective dynamics not as random phenomena, but as structured processes governed by thresholds, memory, and emotional energy. The key phases : incubation, tipping point, persistence, and dissipation—show that crises arise from invisible accumulations rather than isolated shocks. Human systems collapse or stabilize through memory overload or erosion, not through a lack of information. Individual rationality is limited, but an emergent collective cohe

This document proposes an architecture for stable and aligned AI, inspired by collective psychodynamics, by transposing concepts such as mimetic memory, emotional thresholds, and CBD (Convergence-Tilt-Dissipation) regimes to artificial systems. It critiques current AI architectures for their reliance on exogenous feedback, leading to instability, reward hacking, and out-of-distribution fragility, and suggests cumulative internal constraints for endogenous stability. The unive

The Universal Model of Crowd and Market Dynamics extends Dow Theory by integrating collective psychology, mimetic memory, and supply-demand imbalances within a predictive mathematical framework. It is based on a fundamental formula: P(t) = A ψ(S, R, V, M, D, C) × [O(t) ↔ D(t)], formalizing the interactions between psychological thresholds, emotional volume (V via RSI), and the narrative modulator ψ. The internal variables (V(t), ∥D(t), M, ψ(t)) ensure causality and timing, wi