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

Area of Science:

• Theoretical Biology
• Computational Enactivism
• Biosemiotics

Background:

• Life's defining characteristics include autopoiesis, anticipation, and adaptation.
• Semantic closure, a self-referential mechanism, is crucial for understanding these properties.
• Existing models lack a unified framework integrating relational biology, biosemiotics, and ecological psychology.

Purpose of the Study:

• To develop a computational enactivism framework for modeling the evolutionary emergence of semantic closure.
• To integrate concepts from relational biology, physical biosemiotics, and ecological psychology.
• To provide a theoretical basis for the trialectic between autopoiesis, anticipation, and adaptation.

Main Methods:

• Extending Hofmeyr's Fabrication and Assembly systems with temporal parametrization.
• Developing a stepwise computational model from reaction networks to self-constructing chemical systems.
• Applying a unified computational enactivism framework.

Main Results:

• Identified self-reference as necessary for robust self-replication and open-ended evolution.
• Demonstrated the evolution of semantic closure from simple recognition to anticipatory systems.
• Established syntax-pragmatic transformations as essential for life's realization.

Conclusions:

• The proposed model captures critical properties of life, including autopoiesis, anticipation, and adaptation.
• Computational enactivism offers a cohesive theoretical basis for understanding biological information processing and agency.
• This work opens avenues for novel computational models inspired by life's dynamics.