In this multi-agent system, macroscopic critical-like avalanche statistics arise from the interplay between internal agent dynamics and interaction structure, with slightly subcritical micro regimes supporting near-critical macro dynamics across a wider range of network parameters.
Are biological systems poised at criticality?
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abstract
Many of life's most fascinating phenomena emerge from interactions among many elements--many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this "inverse" approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space--a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.
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Word usage cycles in languages track and interact with macroeconomic rhythms through a feedback process near a Hopf bifurcation.
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Emergent Macro-Criticality from Micro-Critical Agents
In this multi-agent system, macroscopic critical-like avalanche statistics arise from the interplay between internal agent dynamics and interaction structure, with slightly subcritical micro regimes supporting near-critical macro dynamics across a wider range of network parameters.