Toward a thermodynamic theory of evolution: a theoretical perspective on information entropy reduction and the emergence of complexity - Mendoza Montano, Carlos (Frontiers in Complex Systems, 2025)
Traditional evolutionary theory explains adaptation and diversification through random mutation and natural selection. While effective in accounting for trait variation and fitness optimization, this framework provides limited insight into the physical principles underlying the spontaneous emergence of complex, ordered systems. A complementary theory is proposed: that evolution is fundamentally driven by the reduction of informational entropy. Grounded in non-equilibrium thermodynamics, systems theory, and information theory, this perspective posits that living systems emerge as self-organizing structures that reduce internal uncertainty by extracting and compressing meaningful information from environmental noise. These systems increase in complexity by dissipating energy and exporting entropy, while constructing coherent, predictive internal architectures, fully in accordance with the second law of thermodynamics. Informational entropy reduction is conceptualized as operating in synergy with Darwinian mechanisms. It generates the structural and informational complexity upon which natural selection acts, whereas mutation and selection refine and stabilize those configurations that most effectively manage energy and information. This framework extends previous thermodynamic models by identifying informational coherence, not energy efficiency, as the primary evolutionary driver. Recently formalized metrics, Information Entropy Gradient (IEG), Entropy Reduction Rate (ERR), Compression Efficiency (CE), Normalized Information Compression Ratio (NICR), and Structural Entropy Reduction (SER), provide testable tools to evaluate entropy-reducing dynamics across biological and artificial systems. Empirical support is drawn from diverse domains, including autocatalytic networks in prebiotic chemistry, genome streamlining in microbial evolution, predictive coding in neural systems, and ecosystem-level energy-information coupling. Together, these examples demonstrate that informational entropy reduction is a pervasive, measurable feature of evolving systems. While this article presents a theoretical perspective rather than empirical results, it offers a unifying explanation for major evolutionary transitions, the emergence of cognition and consciousness, the rise of artificial intelligence, and the potential universality of life. By embedding evolution within general physical laws that couple energy dissipation to informational compression, this framework provides a generative foundation for interdisciplinary research on the origin and trajectory of complexity.