Integrating Agent-Based Modeling, Quantum Theory, and Far-From-Equilibrium Systems
The #dualAnts model, which explores the interaction of two types of ants via pheromone gradients, serves as a representation of agent-based modeling where the ants compute least-time paths.
The model draws from Fermat's principle, observing least action principles in real-world paths. Ants moving between food sources and nests emulate Wheeler-Feynman and Cramer/Kastner interpretations of quantum mechanics by evolving towards paths of least action.
The TIQM framework, particularly Cramer's and Ruth Kastner's contributions, aligns with Wheeler-Feynman absorber theory, framing interactions as advanced and retarded waves. In this context, #dualAnts pheromone gradients act as wave analogs, with ants operating as particles, adhering to paths consistent with TIQM.
See the Onsager matrix exploration for the far-from-equilibrium application of these principles.
Near phase transitions, systems exhibit critical behavior that opens possibilities for computation. The Dual Ant system's parameters (e.g., diffusion rates, pheromone evaporation) offer insights into symmetry breaking and critical adaptation, analogous to physical phase transitions.
This framework allows for understanding ecological adaptation at critical points and builds on Santa Fe Institute's research on emergent phenomena.
Applying Onsager relations to far-from-equilibrium conditions, we explore structure formation and symmetry breaking as sources of negentropy, such as the Dufour effect and Bénard cells.
By framing action constraints across all action fields, the interplay of transport theory, conjugate variables, and least action principles aligns with Prigogine’s theory of dissipative structures.
Constraining action across all fields forms the basis of a unified model in which least action principles operate in multiple domains. This model integrates agent-based and quantum-mechanical perspectives, leveraging action as a fundamental constraint that operates across #dualAnts, TIQM, and FFE Onsager systems.
For a deeper dive into these explorations, refer to Identity Tensor resources.