Policy-oriented agent-based models are increasingly used to study regulatory interventions in complex adaptive socio-technical systems. Recent adaptive ABM frameworks distinguish between static and adaptive agents, fixed and adaptive policies, and alternative controller designs. However, most diagnostic workflows remain ex post: trajectories are analysed after simulation, but the resulting evidence is not systematically fed back into the policy controller. This paper proposes a lightweight machine-coached policy-revision layer for adaptive agent-based regulation. The layer represents policy decisions as defeasible rules with explicit conflicts and priorities, generates explanations for controller actions, and allows diagnostic failures to be translated into rule additions, removals, or priority changes. The contribution is not a new optimal controller and does not claim formal guarantees for unrestricted machine coaching. Instead, it provides a simulation-compatible operationalization of controller-level contestability: policy decisions can be explained, challenged, revised, and re-evaluated in held-out simulation runs. A stylized emissions-regulation ABM is used as the experimental component. A controlled simulation experiment focuses on an over-conservatism failure in the VPVA regime. The predefined coaching template adds a relaxation rule to the symbolic controller, reducing over-conservatism recurrence under held-out seeds while preserving violation, overshoot, and volatility guardrails. The paper argues that machine coaching is best understood as a controller-level extension of explainable adaptive ABM, complementary to causal, information-theoretic, and trajectory-based diagnostics.
Agent-based models are widely used to evaluate policy interventions in complex socio-technical systems, yet many policy-oriented ABMs represent regulation as a fixed scenario parameter. This limits their ability to distinguish whether regulatory conclusions depend on agent adaptation, policy adaptation, or the interaction between both. Building on a previously proposed four-regime architecture, this paper contributes a controlled simulation benchmark rather than a new general framework. Using a single configurable emissions-regulation ABM, we compare constant policy/constant agents, constant policy/adaptive agents, adaptive policy/constant agents, and adaptive policy/adaptive agents under matched simulation conditions. We evaluate naive fixed policies, tracking-aware calibrated fixed policies, and three adaptive controllers: setpoint, safety-margin, and one-sided control. The benchmark recovers expected controller archetypes: setpoint control tracks the cap but produces frequent boundary crossings, safety-margin control reduces violations through conservatism, and one-sided control can limit violations but may ratchet toward over-conservatism when combined with adaptive agents. The contribution is methodological: scalar indicators, cap-relative symbolic diagnostics, trajectory motifs, and visual inspection jointly reveal how regulatory conclusions can differ even when average outcomes appear similar. Adaptive policy-oriented ABMs should therefore be evaluated through regime distinguishability, not only through average performance.