# Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence

arXiv: https://arxiv.org/abs/2606.00235 · HTML: https://mlsanity.com/paper/2606.00235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 10 of 10)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Measuring Progress on Scalable Oversight for Large Language Models](https://mlsanity.com/paper/2211.03540/) (2022)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Is Power-Seeking AI an Existential Risk?](https://mlsanity.com/paper/2206.13353/) (2022)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [AI Research Considerations for Human Existential Safety (ARCHES)](https://mlsanity.com/paper/2006.04948/) (2020)
- [Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI](https://mlsanity.com/paper/2507.06398/) (2025)

## Abstract (arXiv)

We argue that governance must transition from a normative discipline to an engineering discipline, and we develop a formal framework, inspired by the physics of metamaterials, to make this transition quantitative and testable. Artificial General Intelligence affects civilization primarily by increasing decision velocity while human verification capacity remains bounded. When the cost of validating AI-generated outputs exceeds the expected utility of acting on them, rational agents default to inaction: a stable but catastrophic Nash equilibrium we term the Freezing Equilibrium. Drawing on metamaterials, where emergent macro-properties arise from designed microstructure, we develop a phenomenological constitutive law for institutional coordination: Reff = β · (1-ρ) · (1-τ) · (1-γ ρ τ), where β is the decision branching factor, ρ is provenance fidelity, τ is the verification rate, and γ ∈ [0,1] captures correlated-detection synergy between provenance and verification failures. The model predicts a sharp phase transition between self-healing (Reff < 1) and self-destabilizing (Reff > 1) regimes. We introduce a three-class provenance taxonomy: cryptographic, institutional, and context binding, and derive four falsifiable hypotheses with a proposed 12-week stepped-wedge cluster-randomized trial in government grant review panels. The framework bridges AI alignment theory and institutional design.