The Origin and Evolution of Information Handling

• URL: http://arxiv.org/abs/2404.04374v5
• Date: Fri, 07 Feb 2025 16:54:53 GMT
• Title: The Origin and Evolution of Information Handling
• Authors: Amahury Jafet López-Díaz, Hiroki Sayama, Carlos Gershenson,
• Abstract summary: Information-first approach integrates Hofmeyr's (F, A)-systems with temporal parametrization and multiscale causality.<n>Our model traces the evolution of information handling from simple reaction networks that recognize regular languages to self-replicating chemical systems with memory and anticipatory capabilities.
• Score: 0.6963971634605796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding the emergence and evolution of information handling is essential for unraveling the origins of life. Traditional genetic-first and metabolism-first models fall short in explaining how instructional information control systems naturally arise from molecular dynamics. To address this gap, we adopt an information-first approach, integrating Hofmeyr's (F, A)-systems -- an extension of Rosen's (M, R)-systems -- with temporal parametrization and multiscale causality. These models, which embody closure to efficient causation while remaining open to formal causation, provide a robust framework for primitive autopoiesis, anticipation, and adaptation. We establish a formal equivalence between extended (F, A)-systems and communicating X-machines, resolving self-referential challenges and demonstrating the hypercomputational nature of life processes. Our stepwise model traces the evolution of information handling from simple reaction networks that recognize regular languages to self-replicating chemical systems with memory and anticipatory capabilities. This transition from analog to digital architectures enhances evolutionary robustness and aligns with experimental evidence suggesting that chemical computation does not require life-specific chemistry. Furthermore, we incorporate open-ended evolutionary dynamics driven by computational undecidability and irreducibility, reinforcing the necessity of unconventional computing frameworks. This computational enactivist perspective provides a cohesive theoretical basis for a recently proposed trialectic between autopoiesis, anticipation and adaptation in order to solve the problem of relevance. By highlighting the critical role of hypercomputational processes in life's emergence and evolution, our framework offers new insights into the fundamental principles underlying biological information processing.

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