An analytical essay on why prediction-based models fail in reflexive, unstable systems. This article argues that accuracy collapses when models influence behavior, and proposes equilibrium and force-based modeling as a more robust framework for understanding pressure, instability, and transitions in AI-shaped systems.
machine-learning artificial-intelligence theory complex-systems decision-support ai-safety explainable-ai ai-ethics applied-ai systems-thinking interpretable-models uncertainty-modeling transition-risk reflexive-systems equilibrium-modeling ml-philosophy force-based-models non-stationary-systems prediction-vs-explanation modeling-instability
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Updated
Dec 13, 2025