Self-Organization in Non-Equilibrium Thermodynamic Systems: Agent-Based Modeling
ORAL
Abstract
This research unravels the dynamics of self-organization in open, complex, non-equilibrium thermodynamic systems, employing agent-based modeling. The study pivots on the principle of Maximum Entropy Production (MEP), hypothesizing that such systems evolve into states that maximize entropy production, underlining the formation of structured environments.
Drawing upon the principle of least action, we propose that open thermodynamic systems navigate energy and matter transmission paths that maximize entropy production, thereby creating an internal structure and reducing internal entropy. Correspondingly, the system's information increases during structure formation, necessitating more data to detail the system's structure.
Our study examines this by simulating an ant colony navigating between a food source and its nest. Utilizing Netlogo for agent-based modeling and Python for data visualization and analysis, we measure self-organization via the calculated entropy decrease in the system, contingent on the ants' distribution and possible states in the simulated environment. The simulation allows for the observation of external entropy production, information increase, and action efficiency improvement as the system adheres to the least action principle.
We observe that the system starts with maximal entropy, which decreases as paths are formed over time. System behavior varies with ant numbers.
Our methodology focuses on identifying the transition point from disorder to order and calculating the slope at this juncture. Coupled with extrapolation to the final path entropy, this offers insight into the system's eventual entropy state and the time frame for its establishment, thereby allowing for the inference of the self-organization rate. The study presents a unique approach to investigating self-organization in thermodynamic systems, offering a correlation between internal entropy decrease rate and external entropy production rate.
Our research aims to offer a replicable model for analyzing self-organization processes in any open thermodynamic system. It lays a foundation for deeper investigations into complex system behaviors, propelling the development of efficient, self-organizing systems across various domains.
Drawing upon the principle of least action, we propose that open thermodynamic systems navigate energy and matter transmission paths that maximize entropy production, thereby creating an internal structure and reducing internal entropy. Correspondingly, the system's information increases during structure formation, necessitating more data to detail the system's structure.
Our study examines this by simulating an ant colony navigating between a food source and its nest. Utilizing Netlogo for agent-based modeling and Python for data visualization and analysis, we measure self-organization via the calculated entropy decrease in the system, contingent on the ants' distribution and possible states in the simulated environment. The simulation allows for the observation of external entropy production, information increase, and action efficiency improvement as the system adheres to the least action principle.
We observe that the system starts with maximal entropy, which decreases as paths are formed over time. System behavior varies with ant numbers.
Our methodology focuses on identifying the transition point from disorder to order and calculating the slope at this juncture. Coupled with extrapolation to the final path entropy, this offers insight into the system's eventual entropy state and the time frame for its establishment, thereby allowing for the inference of the self-organization rate. The study presents a unique approach to investigating self-organization in thermodynamic systems, offering a correlation between internal entropy decrease rate and external entropy production rate.
Our research aims to offer a replicable model for analyzing self-organization processes in any open thermodynamic system. It lays a foundation for deeper investigations into complex system behaviors, propelling the development of efficient, self-organizing systems across various domains.
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Publication: Planned paper on the same topic.
Presenters
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Georgi Georgiev
Assumption University
Authors
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Georgi Georgiev
Assumption University
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Matthew Brouillet
Assumption University