AmirhosseinHonardoust/Prediction-Fails-When-Systems-Move
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.
This analytical essay helps decision-makers, strategists, and analysts understand why traditional prediction models often fail when applied to dynamic systems, especially those involving human behavior. It explains that models can influence the very systems they aim to predict, leading to unreliable outcomes. The essay provides a framework for understanding system behavior based on forces and equilibrium, rather than just predicting future states.
Use this if you are developing or deploying AI/ML models in complex, adaptive environments (like markets, social systems, or organizational structures) and frequently find predictions become inaccurate or even self-defeating once implemented.
Not ideal if your primary goal is to improve the statistical accuracy of predictive models in stable, weakly coupled systems where the model's influence on the system is negligible.
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MIT
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Dec 13, 2025
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