The Origin and Evolution of Information Handling

2024-04-05

AI Summary

This paper addresses the fundamental problem of how directive information-control systems naturally emerge from abiotic molecular dynamics in the origin of life, transcending the limitations of both genetics-first and metabolism-first paradigms through an “information-first” theoretical framework. Methodologically, it establishes, for the first time, a rigorous equivalence between Hofmeyr’s (F,A)-systems and communication X-machines, integrating time-parameterized dynamics, multiscale causal modeling, and hypercomputation theory to formally characterize the evolutionary transition from analog to digital information architectures. Key contributions include: (i) revealing the hypercomputational nature of living processes; (ii) establishing a dialectical unification of autopoiesis, predictivity, and adaptivity as co-constitutive principles; and (iii) proving that chemical computation does not require life-specific chemistry. The work provides the first computationally tractable and empirically falsifiable information-theoretic ontology for the origin of life.

Technology Category

• Machine Learning: Information Theory
• Humans and AI: Other Foundations of Human Computation & AI
• Search and Optimization: Evolutionary Computation

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Explains origins of life through information handling evolution.
• Resolves self-referential challenges in primitive autopoiesis and adaptation.
• Integrates computational undecidability in evolutionary dynamics framework.

Innovation

• Information-first approach
• Hofmeyr's (F, A)-systems integration
• Hypercomputational processes framework