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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024-12-06
摘要
In many 複雜系統s encountered in the natural and 社會 sciences, mechanisms governing system dynamics at a microscale depend upon the values of state variables characterizing the system at coarse- grained, macroscale (Goldenfeld and Woese, 2011, Noble et al., 2019, and Chater and Loewenstein, 2023). State variables, in turn, are averages over relevant probability distributions of the microscale variables. Neither inferential Top-Down nor mechanistic Bottom-Up modeling alone can predict responses of such scale- entwined systems to perturbations. We describe and explore the properties of a dynamic theory that combines Top-Down 資訊- theoretic inference with Bottom- Up , state- variable- dependent mechanisms. The theory predicts the functional form of nonstationary probability distributions over micro
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