让模拟程序自我审视假设，应对未知因果结构的挑战
Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty
- 变量作为认知权威，机制代表不同因果假说，系统动态调整结构
- 在医院感染模拟中减少20.4%误差，累计后悔值降低69%
- 适合需应对不确定因果关系的复杂系统研究者
机制化模拟通常假设本体论固定：变量、因果关系和分辨率策略不变。但当真实因果结构存在争议或无法识别时（如抗菌药物耐药性传播中，接触、环境与选择等本体论相互竞争），这一假设失效。我们提出Procela，一个基于Python的框架，其中变量作为认知权威，保留完整假设记忆；机制编码竞争性本体为因果单元；治理模块观测认知信号，在运行时变异系统拓扑。这是首个能让模拟自检其假设的框架。我们在医院网络中构建了三种竞争家族的抗微生物耐药性模型。治理模块检测到覆盖率下降、政策脆弱性，并执行结构探测。结果表明，相比基线，误差降低20.4%，累计后悔值改善69%。所有实验可复现且具备完整可审计性。Procela确立了新范式：模拟不仅建模世界，也建模自身建模过程，从而在结构不确定性下实现适应。
原文摘要 · Abstract (English)
Mechanistic simulations typically assume fixed ontologies: variables, causal relationships, and resolution policies are static. This assumption fails when the true causal structure is contested or unidentifiable-as in antimicrobial resistance (AMR) spread, where contact, environmental, and selection ontologies compete. We introduce Procela, a Python framework where variables act as epistemic authorities that maintain complete hypothesis memory, mechanisms encode competing ontologies as causal units, and governance observes epistemic signals and mutates system topology at runtime. This is the first framework where simulations test their own assumptions. We instantiate Procela for AMR in a hospital network with three competing families. Governance detects coverage decay, policy fragility, and runs structural probes. Results show 20.4% error reduction and 69% cumulative regret improvement over baseline. All experiments are reproducible with full auditability. Procela establishes a new paradigm: simulations that model not only the world but their own modeling process, enabling adaptation under structural uncertainty.
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