Closing the Loop: How Semantic Closure Enables Open-Ended Evolution (2404.04374v6)

Abstract: This manuscript explores the evolutionary emergence of semantic closure -- the self-referential mechanism through which symbols actively construct and interpret their own functional contexts -- by integrating concepts from relational biology, physical biosemiotics, and ecological psychology into a unified computational enactivism framework. By extending Hofmeyr's (F, A)-systems -- a continuation of Rosen's (M, R)-systems -- with temporal parametrization and multiscale causality, we develop a model capable of capturing critical life properties, including autopoiesis, anticipation, and adaptation. We then establish a formal equivalence between our extended (F, A)-systems and swarms of communicating automata, resolving self-referential challenges concerning the realizability of relational models. Our stepwise model traces the evolution of semantic closure from simple reaction networks that recognize regular languages to self-replicating chemical systems with memory and anticipatory capabilities, identifying self-reference as necessary for robust self-replication and open-ended evolution. Such a computational enactivist perspective underscores the essential necessity of implementing symbol-matter transformations into computing architectures, providing a cohesive theoretical basis for a recently proposed trialectic between autopoiesis, anticipation, and adaptation to solve the problem of relevance realization. Thus, our work opens pathways to new models of computation for life, agency and cognition, offering fundamental principles underlying biological information processing.

• The paper demonstrates a sequential emergence from finite-state automata to Turing machine-like behaviors in pre-life chemical interactions.
• The study uses reaction network models, including bimolecular reactions and pH dynamics, to reveal how simple chemical processes develop memory and complexity.
• The research bridges theoretical models with experimental observations, outlining future in vitro and in silico directions for studying life's computational origins.

Exploring the Computational Underpinnings of Life's Origin

Introduction

The paper conducted by Lopez-Diaz, Sayama, and Gershenson explores one of the most foundational enigmas of biochemistry and systems biology: the emergence of life and the subsequent evolution of information handling capabilities within prebiotic chemistries. Through the lens of computational theory, the research meticulously constructs a paradigm that elucidates the sequential and hierarchical emergence of computational capabilities, ranging from simple finite state automata to complex Turing machine-like behaviors, within the context of pre-life chemical interactions.

Computational Foundations of Life

Finite-State Automata and the Onset of Information Processing

The paper commences by conceptualizing the planet as a nascent computational landscape, wherein elementary molecular interactions forge the cradle for life's computational ascendancy. The authors argue that the simplest non-living chemical interactions can be interpreted as finite-state automata (FSAs), primarily engaged in rudimentary information processing tasks. The illustrative use of bimolecular reactions underscores the transition from mere chemical interactions to the instantiation of computational rudiments.

Transition to Push-Down Automata: Emergence of Memory

Building on the foundation of FSAs, the paper proceeds to explore how complex reaction networks, akin to pH-reaction networks, evolve to exhibit behaviors characteristic of push-down automata (PDAs). This leap in computational complexity introduces the capability for information storage or memory, laying the groundwork for evolutionarily robust systems that can adapt to environmental vicissitudes through more sophisticated information handling.

The Evolution Toward Turing Machines

The advent of Turing machine-like capabilities within chemical systems marks a zenith in the paper's narrative. Employing the Belousov-Zhabotinsky reaction as a paradigm, the authors delineate how certain reaction networks could instantiate the behaviors of Linear Bounded Automata (LBAs). This conceptual leap signifies not merely an increase in computational complexity but also the emergence of information transmission faculties, heralding the onset of systems capable of richer, more complex forms of information handling.

Theoretical and Practical Implications

Bridging Theoretical Models and Experimental Validation

The paper does not merely rest within the theoretical domain; it ambitiously entwines these computational theories with experimental methodologies, inviting a compelling dialogue between theoretical assertions and empirical substantiation. The reference to experiments by Duenas-Díez and Perez-Mercader as a springboard for their theoretical propositions demonstrates a rigorous approach to grounding theoretical insights in empirical observations.

Speculation on Future Research Directions

Looking forward, the paper posits intriguing avenues for future research, notably the exploration of biological phenomena across various spatiotemporal scales through the computational lens elucidated herein. Furthermore, the call for in vitro and in silico validation underlines the authors' commitment to moving beyond speculative models towards a more tangible, experimentally grounded understanding of life's computational genesis.

Conclusion

This research paper traces a compelling narrative from the simple computational machinations of chemical interactions to the threshold of life’s complex information processing faculties. By framing the emergence of life within a computational paradigm, the authors not only provide a novel vantage point on the origin of life but also extend a methodological bridge towards understanding the evolution of computational capabilities in nature. As such, the implications of this work span both the theoretical realms, offering a fresh conceptual framework for life's origin, and the practical, proposing a roadmap for future experimental explorations of life's computational underpinnings.