Closing the loop: how semantic closure enables open-ended evolution?
Amahury Jafet López-díaz, Carlos Gershenson
Journal of the Royal Society, Interface December 17, 2025 DOI: 10.1098/rsif.2025.0784 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Adaptation Anticipation Autopoiesis Biocomputation Biosemiotics Open-ended evolution Origins of life Relational biology Self-reference Semantic closure |
| Citations | 2 |
| Key points | Argues that self-reference is necessary for robust self-replication and open-ended evolution, and that semantic closure emerges from simple reaction networks to self-constructing chemical systems with anticipatory capabilities. |
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.