Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer

Researchers propose a machine-coached policy-revision layer for adaptive agent-based models (ABMs) used in regulatory simulation, enabling real-time feedback and contestability of policy decisions through explainable symbolic rules rather than black-box optimization. The approach demonstrates practical application in emissions-regulation scenarios, balancing policy objectives while maintaining regulatory guardrails.

This research addresses a critical gap in computational policy design: the disconnect between simulation diagnostics and actual controller behavior. Traditional ABM workflows generate insights after the fact, but those findings rarely feed back into the regulatory decision-making process in systematic ways. By introducing a machine-coached contestability layer, the authors create a feedback loop where policy failures identified during simulation can directly inform rule modifications, priority adjustments, or constraint relaxations.

The work emerges from growing recognition that AI-driven regulatory systems need explainability and human oversight. Rather than pursuing "optimal" controllers through black-box reinforcement learning, this approach maintains symbolic, human-readable rules that can be challenged, modified, and re-evaluated. This aligns with broader regulatory trends toward algorithmic transparency and contestability, particularly in financial and environmental domains.

For the policy and regulatory technology sector, this represents a methodological advancement enabling regulators to stress-test interventions before deployment. The emissions-regulation case study demonstrates real-world applicability: the system identified over-conservatism in a VPVA (Voluntary Provident Virtue Assessment) regime and automatically proposed corrective rules without compromising safety constraints. This pattern scales to financial regulation, healthcare policy, and climate intervention design.

The significance lies not in claiming optimality but in operationalizing controller-level contestability—making policy decisions auditable and revisable. As regulatory bodies increasingly adopt computational models for complex socio-technical systems, tools that integrate explainability, diagnostics, and rule revision become essential infrastructure. The framework positions machine coaching as complementary to existing causal and information-theoretic diagnostic methods rather than replacing them.

• →Machine-coached policy revision enables real-time feedback from simulation diagnostics into regulatory controller decisions via symbolic rule modifications
• →The approach prioritizes explainability and contestability over formal optimality guarantees, making policy decisions auditable and human-revisable
• →Demonstrated application in emissions regulation shows system can reduce policy failures while preserving safety and volatility constraints
• →Framework extends explainable AI principles to the controller level, addressing transparency requirements for algorithmic regulation
• →Method scales across policy domains including finance, healthcare, and climate intervention by maintaining symbolic, human-readable decision rules