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Procela is introduced, a Python framework for mechanistic simulations that dynamically adapts to structural uncertainty by allowing variables to act as epistemic authorities maintaining hypothesis memory, mechanisms to encode competing ontologies, and governance to mutate system topology at runtime. Applied to antimicrobial resistance (AMR) modeling in a hospital network with competing causal ontologies, Procela uses governance to detect coverage decay and policy fragility, and to run structural probes. Experiments demonstrate a 20.4% error reduction and 69% cumulative regret improvement compared to baseline models with fixed ontologies.
Simulations can now test and adapt their own assumptions, slashing error by 20% in complex domains like antimicrobial resistance.
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.