Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty
This work addresses the challenge of contested causal structures in simulations, such as antimicrobial resistance spread, by enabling adaptive modeling, representing a new paradigm rather than an incremental improvement.
The paper tackles the problem of fixed ontologies in mechanistic simulations by introducing Procela, a Python framework that allows simulations to test their own assumptions and adapt under structural uncertainty, resulting in a 20.4% error reduction and 69% cumulative regret improvement over baseline in antimicrobial resistance spread simulations.
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.