Why and How do Complex Systems Self-Organize at All? Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization

TL;DR

This paper introduces average action efficiency as a dynamic measure and mechanism for understanding and predicting self-organization in complex systems, supported by data and simulations.

Contribution

It proposes a novel dynamic measure, average action efficiency, and demonstrates its effectiveness in explaining and predicting self-organization in complex systems.

Findings

Average action efficiency correlates with self-organization levels.

Positive feedback loops drive exponential growth in system characteristics.

Model predictions are confirmed by data and agent-based simulations.

Abstract

Self-organization in complex systems is a process in which randomness is reduced and emergent structures appear that allow the system to function in a more competitive way with other states of the system or with other systems. It occurs only in the presence of energy gradients, facilitating energy transmission through the system and entropy production. Being a dynamic process, self-organization requires a dynamic measure and dynamic principles. The principles of decreasing unit action and increasing total action are two dynamic variational principles that are viable to utilize in a self-organizing system. Based on this, average action efficiency can serve as a quantitative measure of the degree of self-organization. Positive feedback loops connect this measure with all other characteristics of a complex system, providing all of them with a mechanism for exponential growth, and indicating power law relationships between each of them as confirmed by data and simulations. In this study, we apply those principles and the model to agent-based simulations. We find that those principles explain self-organization well and that the results confirm the model. By measuring action efficiency we can have a new answer to the question: "What is complexity and how complex is a system?". This work shows the explanatory and predictive power of those models, which can help understand and design better complex systems.