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.

翻译：我们论证治理必须从规范性学科转变为工程性学科，并受超材料物理学的启发，开发了一个形式化框架，使这一转变变得可量化、可检验。通用人工智能主要通过提升决策速度影响文明，而人类验证能力仍保持有限。当验证AI生成产出的成本超过基于其采取行动的预期效用时，理性主体默认选择不作为：这是一种稳定但灾难性的纳什均衡，我们将其称为“冻结均衡”。借鉴超材料中涌现宏观特性源于设计的微观结构这一理念，我们推导出机构协调的现象本构定律：$R_{\mathrm{eff}} = β\cdot (1-ρ) \cdot (1-τ) \cdot (1-γρτ)$，其中$β$是决策分支因子，$ρ$是来源保真度，$τ$是验证速率，$γ\in [0,1]$刻画来源与验证失败之间的相关检测协同效应。该模型预测出自愈（$R_{\mathrm{eff}} < 1$）与自失稳（$R_{\mathrm{eff}} > 1$）状态间的尖锐相变。我们引入三类来源分类法：密码学、制度与上下文绑定，并推导出四个可证伪假设，同时提出一项为期12周的阶梯楔形集群随机试验方案（应用于政府拨款评审小组）。该框架弥合了AI对齐理论与制度设计之间的鸿沟。