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“Internal models” as a book category centers on how minds, organizations, and machines construct and use inner representations to predict, interpret, and act in the world. It spans cognitive science and neuroscience (perception as prediction, motor control, self-models), psychology and decision-making (mental models, metacognition, heuristics and biases, therapeutic frameworks like Internal Family Systems), organizational strategy and risk (the modeling assumptions that drive forecasts and choices), and AI/robotics/control theory (model-based learning, simulators, cybernetics). The unifying theme is how these inner maps are formed, why they succeed or fail, and how to test, debug, and refine them. Books in this genre blend explanation with practical tools for surfacing assumptions, improving judgment, and engineering better predictive models—personally, collectively, or computationally.

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