Ontic Physical Computable Governance Causal Flow Formula
Year: 2026
Type: article
Abstract: This paper presents the **Ontic Physical Computable Governance Causal Flow Formula** of the **Ontic Regime**, establishing a rigorous physical turn in political theory by redefining socio-political interactions as measurable biological energy transactions between living systems, standardized through the Joule-denominated **Stellar Equivalent (StellarEq)** metric. Within this physically constrained architecture, individual influence is dynamically calculated in real time through a recursively updated Causal Graph via the root equation **$\text{Power}(t) = \text{Contribution}(t) \times \text{AdoptionRate}(t)$**. To systematically prevent capital accumulation, intellectual hoarding, and neo-feudal stagnation, the framework hardcodes **dynamic Causal Edge Weights ($\text{EdgeWeight}_{n \rightarrow i}(t)$) governed by exponential TimeDecay functions** to naturally dilute historical influence over time. Furthermore, civilizational resilience is cybernetically secured by a dynamic **Civilization Multiplier ($\text{CivilizationMultiplier}(t)$) auditing stability, human empowerment, and cosmic truth-seeking capability**. To bridge this mathematical physics model with practical application, the paper introduces a **38-step algorithmic Power Flow Protocol** designed to construct a foundational pathway that makes building the **Computable Political Language (CPL)** easier in the future. Crucially, to preserve the macro-scale focus on civilizational energy flow, **this paper does not discuss the concrete compilation mechanics of CPL, explicitly omitting its syntax, operational semantics, types, variables, conditions, and state transitions.** Serving as a scalable, protocol-based "Civilization OS," the Ontic model provides a phased, layered transition from subjective ideological power struggles to a physically grounded, empirically auditable, and self-correcting collective intelligence.
Author: FU, Sum Wan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171874231
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