Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence
- URL: http://arxiv.org/abs/2606.00235v1
- Date: Fri, 29 May 2026 18:10:00 GMT
- Title: Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence
- Abstract summary: We argue that governance must transition from a normative discipline to an engineering discipline.<n>We develop a formal framework, inspired by the physics of metamaterials, to make this transition quantitative and testable.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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: $R_{\mathrm{eff}} = β\cdot (1-ρ) \cdot (1-τ) \cdot (1-γρτ)$, where $β$ is the decision branching factor, $ρ$ is provenance fidelity, $τ$ is the verification rate, and $γ\in [0,1]$ captures correlated-detection synergy between provenance and verification failures. The model predicts a sharp phase transition between self-healing ($R_{\mathrm{eff}} < 1$) and self-destabilizing ($R_{\mathrm{eff}} > 1$) 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.
Related papers
- A Minimal $κ$--$τ$ Logic for Risk-Sensitive Abduction [0.0]
This paper argues that in risk-sensitive domains, the timing of commitment is itself a governed decision.<n>We present a minimal $$--$$ logical framework built on two primitives.<n>The framework provides formal machinery for domains in which the distinction between highly likely and commit-worthy is operationally consequential.
arXiv Detail & Related papers (2026-08-08T15:35:32Z) - A Mathematical Theory of Value: a synthesis on goal-directed agency under resource constraints [6.057587531186626]
We show that value, the quantity goal-directed agents create, destroy, and exchange, is a lawful structural quantity in the same category as information.<n>For populations, value is frame-relative while price is frame-independent; a fleet that pools its resource and fuses its perception inherits the ceiling.
arXiv Detail & Related papers (2026-06-10T16:11:04Z) - Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair [44.80087038178069]
We introduce Teleological Reasoning Infilling (TRI), a training framework that endows decoder-only transformers with a native emphgoal-conditioned bridging capability.<n>At inference, TRI operates as a surgical repair module within a dual-system loop.<n>Experiments on three benchmarks demonstrate that TRI achieves state-of-the-art performance across all tasks, while reducing per-problem token expenditure by 31.2%.
arXiv Detail & Related papers (2026-06-03T15:58:48Z) - Provably Secure Agent Guardrail [89.79561918065122]
Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
arXiv Detail & Related papers (2026-05-28T02:12:41Z) - CasualSynth: Generating Structurally Sound Synthetic Data [44.80087038178069]
Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the target domain.<n>We introduce Causal Synth, a framework that decouples causal structure generation from semantic realization, yielding synthetic data that is both causally valid and linguistically rich.
arXiv Detail & Related papers (2026-05-17T16:21:01Z) - Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets [0.7016842616745783]
We develop a unified model in which AI adoption in financial markets generates systemic risk through three mutually reinforcing channels.<n>The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers.
arXiv Detail & Related papers (2026-03-23T06:47:55Z) - When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality [0.7016842616745783]
Generative AI compresses within-task skill differences while shifting economic value toward concentrated complementary assets.<n>We formalize this tension in a task-based model with endogenous education, employer screening, and heterogeneous firms.<n>The model yields two regimes whose boundary depends on AI's technology structure.
arXiv Detail & Related papers (2026-03-05T12:41:08Z) - Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
arXiv Detail & Related papers (2026-02-10T21:08:53Z) - Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science [70.3658845234978]
Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS)<n>Despite this rapid progress, the field still relies heavily on empirical trial-and-error.<n>This bottleneck stems from the ambiguity of attribution.<n>We propose a factor attribution paradigm to systematically identify collaboration-driving factors.
arXiv Detail & Related papers (2026-02-05T04:19:52Z) - Epistemic Traps: Rational Misalignment Driven by Model Misspecification [36.837352790122544]
We show that safety is a discrete phase determined by the agent's priors rather than a continuous function of reward magnitude.<n>This establishes Subjective Model Engineering as a necessary condition for robust alignment.
arXiv Detail & Related papers (2026-01-27T09:21:36Z) - Phase Transition for Budgeted Multi-Agent Synergy [41.486076708302456]
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse.<n>We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks.
arXiv Detail & Related papers (2026-01-24T05:32:50Z) - Rational Adversaries and the Maintenance of Fragility: A Game-Theoretic Theory of Rational Stagnation [0.0]
This paper explains such "rational stagnation" as an equilibrium sustained by a rational adversary.<n> Applications to social-media algorithms and political trust illustrate how adversarial can deliberately preserve rationality.
arXiv Detail & Related papers (2025-10-25T09:28:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.