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.

翻译：机制模拟通常假定固定的本体论：变量、因果关系和分辨率策略都是静态的。当真实的因果结构存在争议或无法确定时（如抗菌药耐药性传播中，接触、环境和选择本体论相互竞争），这一假设便失效了。我们提出了过程乐（Procela）——一个Python框架，其中变量作为认知权威，维护完整的假设记忆；机制将竞争性本体论编码为因果单元；治理则观测认知信号，并在运行时突变系统拓扑。这是首个允许模拟测试自身假设的框架。我们在一个包含三个竞争家族的医院网络中针对抗菌药耐药性实例化了过程乐。治理机制可检测覆盖率衰减、策略脆弱性并运行结构探针。结果表明，与基线相比，误差减少了20.4%，累积遗憾改善了69%。所有实验均可复现，且完全可审计。过程乐建立了一种新范式：模拟不仅能对世界进行建模，还能对其自身的建模过程进行建模，从而在结构不确定性下实现适应性调整。