Entropy-Driven Evolution: A Thermodynamic Framework for the Emergence and Directionality of Biological Complexity

This project proposes Entropy-Driven Evolution (EDE), a theoretical framework that integrates thermodynamics, systems theory, and evolutionary biology to explain the emergence and long-term directionality of life. Unlike traditional Darwinian models that emphasize random mutation and passive selection, EDE models life as a dissipative system formed under stellar entropy pressure. Evolution is reframed as a process driven by the system’s efficiency in regulating energy and information flow. The model introduces entropy-handling efficiency ( 𝜂 η), information density ( Φ Φ), and cooperative capacity ( 𝜓 ψ) as core evolutionary variables. A new entropy-selective fitness function is proposed, and a Python-based simulation (SimEDE) demonstrates how entropy gradients influence the evolution of complexity and cooperation. The model offers testable predictions and applications in ecology, genetics, AI modeling, and astrobiology. This work aims to bridge the gap between physical science and biology by offering a logically consistent, cross-disciplinary explanation for life’s structural and informational complexity.