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Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty

Kinson Vernet

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.

Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty

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.

Paper Structure

This paper contains 101 sections, 6 theorems, 14 equations, 12 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. The problem with fixed-ontology simulation
  3. Current approaches and their limits
  4. Procela: A new paradigm
  5. Contributions
  6. Paper roadmap
  7. Reproducibility
  8. Procela core abstractions
  9. Variables as memory-bearing epistemic authorities
  10. Resolution policies
  11. Mechanisms as causal units
  12. Auditability and source tracking
  13. Governance as a first-class citizen
  14. Invariants
  15. Hooks
  16. ...and 86 more sections

Key Result

Theorem 1

Under the assumption that hypothesis errors are independent and normally distributed with variance inversely proportional to confidence, the weighted voting policy $\pi_{WV}(\mathcal{H}) = \frac{\sum_i c_i v_i}{\sum_i c_i}$ minimizes expected squared error. $\blacktriangleleft$$\blacktriangleleft$

Figures (12)

  • Figure 1: Mechanism contributions (governance combined) for the AMR case study. The predicted colonized patients is for combined governances (Section \ref{['sec:amr_governance']}).
  • Figure 2: Regime shifts for the AMR case study.
  • Figure 3: Coverage timeline for the baseline.
  • Figure 4: Timeline of policy fragility experiments and confidence share between ontologies. Shaded regions indicate experiment periods for 1 experiment. Successes (green) occur in high-error regimes.
  • Figure 5: Experiment outcomes for coverage governance. (Left): coverage for baseline (no governance), (Right): coverage for coverage decay governance. By comparison to the results with no governance, coverage shows that contact family is continuously disabled from the selection regime.
  • ...and 7 more figures

Theorems & Definitions (22)

  • Definition 1: Variable
  • Definition 2: Hypothesis
  • Definition 3: Mechanism
  • Definition 4: Invariant
  • Definition 5: Hooks
  • Definition 6: Executive
  • Definition 7: Resolution function
  • Theorem 1: Weighted voting optimality
  • proof
  • Corollary 1
  • ...and 12 more