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This lecture by John Harte from the University of California, Berkeley and Santa Fe Institute presents a groundbreaking approach to complex systems dynamics. Explore how combining information-theoretic top-down inferential methods with agent-based process modeling creates a new theoretical framework that addresses the limitations of traditional methods when applied to dynamic systems. Discover how this innovative approach predicts both the time evolution of state variables and probability distributions over microvariables, featuring unexpected analytic expressions for Lagrange multipliers that enable rapid solutions even in high-dimensional systems. The presentation includes a worked-out ecological example demonstrating the theory's structure and discusses potential applications across diverse fields including non-equilibrium chemical thermodynamics, epidemiology, economics, and ecology. Learn about a proposed test of the theory using an exothermic, gas-phase oxidation reaction in a calorimeter, offering insights into systems exhibiting bi-directional cross-scale causation.
