Closing the loop: how semantic closure enables open-ended evolution?

Summary

Semantic closure—the self-referential mechanism by which symbols construct and interpret their own contexts—arises evolutionarily from simple reaction networks to self-constructing chemical systems with anticipatory capabilities. By extending relational biology models with temporal parameters, the work identifies self-reference as necessary for robust self-replication and open-ended evolution. A computational enactivist framework integrates autopoiesis, anticipation, and adaptation to address the problem of relevance realization, providing a theoretical basis for naturalizing agency and cognition.

Study at a glance

Abstract

This study 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 (Fabrication, Assembly) systems-a continuation of Rosen's (Metabolism, Repair) systems-with a temporal parametrization, we develop a model capable of capturing critical properties of life, including autopoiesis, anticipation and adaptation. Our stepwise model traces the evolution of semantic closure from simple reaction networks that recognize regular languages to self-constructing chemical systems with 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 syntax-pragmatic transformations into realizations of life, 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 avenues for new models of computation that can better capture the dynamics of life, naturalize agency and cognition and provide fundamental principles underlying biological information processing.