Constitutional Physics: Empirical Validation of Aitiopoietic Cognition in Artificial Governance Systems Article Swipe

MAKE IMPACT

Arleo, Carlos · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17692900

Addressing the growing Governance Debt—the dangerous gap between our technological power and our institutional wisdom—requires new scientific instruments. This paper introduces the Wisdom Forcing Function (WFF), a new class of system that implements Alignment-by-Architecture. The WFF is built on the Constitutional Physics thesis: that stable, just governance exists within a small, discoverable attractor basin defined by the intersection of Natural and Social Laws. We test this by cross-validating the WFF’s evolutionary dynamics against Descend Field Theory (DFT), a universal law of stability. Our analysis of 15 experimental runsa The integration of artificial intelligence into high-stakes governance has produced a widening “governance gap” between rapid technological capability and slow-moving institutional wisdom. Contemporary alignment approaches—most notably Reinforcement Learning from Human Feedback (RLHF)—frame safety as a behavioral training problem, yielding agents that perform compliant behaviors without developing structural understanding. This work introduces the Wisdom Forcing Function (WFF), a neurosymbolic architecture implementing alignment-by-architecture, in which democratic principles operate as survival laws rather than optimization targets. Building on Veloz’s (2025) theory of aitiopoietic cognition, we hypothesize that robust alignment requires systems to preserve their own organization through causal understanding of viability conditions. We experimentally validate this through a controlled Great Filter test, in which a governance-generating AI faces an abrupt shift from soft to hard constitutional constraints at Generation 4. Upon activation, the system exhibited 100% initial mortality (6/6 frames, fitness = 0.0) caused by metabolic-closure failures—specifically, incomplete capital-interaction matrices violating the Wholeness principle. Rather than accepting extinction, the system initiated a rapid homeostatic repair sequence lasting 4.9 seconds, representing a ~10× spike in computational work (P_work) relative to baseline fitness evaluation. This thermodynamic event was tightly coupled to diagnostic analysis: the system identified missing capital interactions, generated targeted mutations restoring metabolic closure, and revalidated these repairs against constitutional constraints. One frame (ScaffoldedFrame_5_gen4) successfully recovered, achieving fitness = 0.641—a 63.1% improvement over the previous maximum (0.537)—and enabling evolutionary rescue in subsequent generations. These results provide the first empirical demonstration that artificial systems can bridge Veloz’s “thermodynamic disconnect,” exhibiting energy expenditure intrinsically coupled to organizational maintenance rather than output maximization. We show that democratic principles can be encoded not as aspirational norms but as the non-negotiable physics of computational survival—supporting systems that are not merely intelligent, but constitutionally alive. reveals the WFF operates as a metastable system with a 100% recovery rate from 8 distinct stability violations. The core discovery is that recovery is driven by a multi-modal, diagnostic “immune system” with three distinct response modes, validated by a statistically significant negative correlation (r = −0.67, p = 0.07) between violation magnitude and corrective work. This, combined with the system’s demonstrated capacity for autopoiesis (architectural self-creation), validates the WFF as a provably resilient and self-improving governance engine, transforming alignment from a linguistic challenge into a verifiable, experimental science. SIGNIFICANCE This work represents the first empirical demonstration of aitiopoietic cognition  (self-production via causal knowledge) in an artificial system. Unlike current AI alignment approaches that optimize for behavioral compliance, Constitutional Physics  treats democratic principles as survival requirements—violations cause ontological death, not merely lower scores. TECHNICAL AVAILABILITY Implementation code, experimental protocols, and complete session logs available upon request. Commercial pilots available for organizations seeking constitutional governance systems. Contact: [email protected]

Type

other

Landing Page

https://doi.org/10.5281/zenodo.17692900

OA Status

green

OpenAlex ID

https://openalex.org/W7106565256

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W7106565256

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.5281/zenodo.17692900

Digital Object Identifier

Title

Constitutional Physics: Empirical Validation of Aitiopoietic Cognition in Artificial Governance Systems

Work title

Type

other

OpenAlex work type

Publication year

2025

Year of publication

Publication date

2025-11-23

Full publication date if available

Authors

Arleo, Carlos

List of authors in order

Landing page

https://doi.org/10.5281/zenodo.17692900

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://doi.org/10.5281/zenodo.17692900

Direct OA link when available

Concepts

Corporate governance, Attractor, Function (biology), Forcing (mathematics), Intersection (aeronautics), Field (mathematics), Artificial intelligence, Reinforcement learning, Computer science, Democracy, Engineering, Event (particle physics), Management science, Human systems engineering, Class (philosophy), Control (management), Law and economics, Complex system, Cognition, Social system, Power (physics), System dynamics, Artificial general intelligence, Causal reasoning, Rule of law, Economics, Cognitive architecture, Cognitive science, Social learning, Law, Artificial life, Empirical research, Economic system, Risk analysis (engineering), Political science

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Full payload