Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty

TL;DR

Procela is a novel Python framework enabling mechanistic simulations to self-test and adapt their underlying assumptions in real-time, improving accuracy under structural uncertainty.

Contribution

It introduces the first simulation framework where models can test and modify their own assumptions during runtime to handle contested causal structures.

Findings

Achieved 20.4% error reduction in AMR simulation.

Improved cumulative regret by 69% over baseline.

Demonstrated adaptability in modeling competing ontologies.

Abstract

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.