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Explore a complex systems lecture that delves into the intricate relationship between micro-scale and macro-scale variables in dynamic systems. Examine how the maximum information entropy inference procedure (MaxEnt) can predict micro-scale probability distributions in steady-state conditions, and investigate the challenges that arise when systems are disturbed or far from equilibrium. Discover a hybrid theory combining mechanism and MaxEnt approaches to describe the dynamics of disturbed systems with reciprocal cross-scale influences. Gain insights into the rich behaviors emerging from this theory through a toy model that illustrates general concepts. Learn from John Harte of the University of California, Berkeley, as he presents this thought-provoking analysis of cross-scale dynamics in complex systems.
