CivOS Safety Kernel: Bounded Stability and Dynamic Risk Mitigation via Commutative Safety Protocols

Modern complex socio-technological and civilizational systems face cascading systemic risks (WR1–WR5), ranging from resource blockades and economic suffocation to infrastructure collapses. Traditional post-hoc governance mechanisms fail to mitigate such adversarial dynamics due to execution-order dependency and cumulative governance latency penalties. This work introduces the CivOS Safety Kernel, a category-theoretic and dynamically adaptive governance architecture that enforces structural safety and bounded stability across multi-layered civilizational systems. By integrating commutative safety protocols with a threshold-triggered re-normalization mechanism, the kernel guarantees invariance of state transitions under adversarial shock permutations when safety strength αₜ = 1.0. Dynamic stress-testing across Constant, Burst, and Ramp attack patterns demonstrates that unprotected systems exhibit unbounded cumulative drift (Δ₍cum₎ → ∞), whereas the CivOS Safety Kernel transforms divergence into strictly bounded sawtooth stability. Furthermore, by rendering attacker payoff strictly negative (Uₐₜₜₐcₖₑᵣ < 0), the kernel establishes a Subgame Perfect Equilibrium where non-attack becomes the unique dominant strategy. This preprint provides the mathematical and operational foundation for civilizational-scale structural deterrence and resilient AI-native OS architecture.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-25
DOI
https://doi.org/10.5281/zenodo.21557383
Primary Topic
Security and Verification in Computing
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

CivOS Safety Kernel: Bounded Stability and Dynamic Risk Mitigation via Commutative Safety Protocols

Yuji Marutani
Zenodo (CERN European Organization for Nuclear Research)
Security and Verification in Computing
preprint

CivOS Safety Kernel: Bounded Stability and Dynamic Risk Mitigation via Commutative Safety Protocols

Yuji Marutani
preprint en

Abstract

Modern complex socio-technological and civilizational systems face cascading systemic risks (WR1–WR5), ranging from resource blockades and economic suffocation to infrastructure collapses. Traditional post-hoc governance mechanisms fail to mitigate such adversarial dynamics due to execution-order dependency and cumulative governance latency penalties. This work introduces the CivOS Safety Kernel, a category-theoretic and dynamically adaptive governance architecture that enforces structural safety and bounded stability across multi-layered civilizational systems. By integrating commutative safety protocols with a threshold-triggered re-normalization mechanism, the kernel guarantees invariance of state transitions under adversarial shock permutations when safety strength αₜ = 1.0. Dynamic stress-testing across Constant, Burst, and Ramp attack patterns demonstrates that unprotected systems exhibit unbounded cumulative drift (Δ₍cum₎ → ∞), whereas the CivOS Safety Kernel transforms divergence into strictly bounded sawtooth stability. Furthermore, by rendering attacker payoff strictly negative (Uₐₜₜₐcₖₑᵣ < 0), the kernel establishes a Subgame Perfect Equilibrium where non-attack becomes the unique dominant strategy. This preprint provides the mathematical and operational foundation for civilizational-scale structural deterrence and resilient AI-native OS architecture.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Security and Verification in Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.