🤖 AI Summary
This study addresses the challenge in agent-based policy simulation of disentangling whether regulatory effects stem from agent adaptation, policy adaptation, or their interaction. To this end, the authors construct a controllable simulation benchmark using a single, configurable emission-regulation agent-based model (ABM) to systematically compare four combinations of static/adaptive agents and static/adaptive policies. They propose an evaluation paradigm centered on “institutional distinguishability,” integrating scalar metrics, symbolic diagnostics, trajectory patterns, and visual analytics to uncover hidden mechanistic differences despite similar average performance. Experiments replicate characteristic behaviors of various adaptive controllers—such as set-point, safety-margin, and one-sided control—and demonstrate that reliance solely on aggregate outcomes can obscure critical structural distinctions, thereby underscoring the necessity of comprehensive policy mechanism evaluation.
📝 Abstract
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.