Neophronesis Theory: A Thermodynamic Framework for Adaptive Coherence Under Flux
Neophronesis Theory proposes that wisdom, operationally defined as adaptive coherence under flux, reflects thermodynamic necessity rather than cultural preference. The framework advances a strong claim of physical identity — not metaphorical resemblance — between the coherence-maintenance dynamics established in dissipative-structure thermodynamics (Prigogine), assembly theory (Walker and Cronin), the free energy principle (Friston), scaling laws for complex systems (West), and the cognitive and cultural systems that sustain organized complexity against entropic pressure. The paper integrates independent scientific traditions — thermodynamic economics, governance of the commons, resilience science, cultural evolution, institutional economics, and network theory — around a shared functional requirement (adaptive coherence under flux), and derives testable predictions at three organizational scales: individual (metabolic and neural correlates of wisdom-related reasoning), institutional (Seven-Rs characteristics and cascade-risk reduction), and civilizational (governance and network signatures preceding collapse windows). Each prediction carries explicit numeric falsification criteria. The framework maintains a strict Framework / Theory / Application distinction and uses [Established] / [Theoretical] / [Prediction] tagging throughout. It commits publicly to responsive revision: predictions that fail empirical testing require framework revision or abandonment, not defense. The paper is offered as an invitation to the complexity-science, cognitive-neuroscience, philosophy-of-science, and institutional-economics research communities, and as a target for rigorous refutation.
Authors
- John Ashcraft
Institutions
- ResearchWorks (United States) (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-14
- DOI
- https://doi.org/10.5281/zenodo.22755201
- Primary Topic
- Innovation, Sustainability, Human-Machine Systems
- Type
- preprint