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Evolution of Energies and Forces Into Biological Intelligence

Malicse, Angelito
PhilArchive · Papers
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philosophy
Abstract The origin of biological intelligence from the fundamental energies and forces of the universe remains a central question across physics, biology, and cognitive science. This paper provides an extended systems-theoretical explanation of this evolutionary trajectory, integrating principles from thermodynamics, complexity science, evolutionary theory, cybernetics, and network science. The core contribution of this manuscript is the formal incorporation of Angelito Malicse’s Universal Formula of Natural Laws—composed of the Law of System Integrity, the Universal Law of Balance in Nature, the Universal Feedback Loop Mechanism, and the Universal Interconnected Nodes Principle—into a unified scientific framework applicable across physical, biological, ecological, cognitive, and sociotechnical systems. The manuscript argues that complexity arises naturally through the interplay of structure, energy flow, and feedback across scales, ultimately producing intelligence and consciousness as emergent properties. Human societies, consisting of billions of individuals functioning as interconnected nodes, represent the most complex emergent network system known. A special focus is given to how overpopulation, as exponential node expansion, intensifies competition, increases information exchange, accelerates innovation, and elevates collective intelligence while simultaneously generating systemic instability when balance is violated. The paper expands the scope of the Universal Formula, demonstrating its compatibility with modern scientific theories including non-equilibrium thermodynamics, autopoiesis, neural network theory, cultural evolution, and global systems dynamics. Through 40+ scholarly references, diagrams, and extended analysis, the manuscript proposes that the Universal Formula provides a unified conceptual architecture capable of explaining natural evolution, intelligence emergence, and sociotechnical complexity in a single coherent framework.
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