Structural Distinguishability of Static and Adaptive Policy Regimes in Agent-Based Regulatory Simulation

• URL: http://arxiv.org/abs/2606.20699v1
• Date: Mon, 15 Jun 2026 10:06:32 GMT
• Title: Structural Distinguishability of Static and Adaptive Policy Regimes in Agent-Based Regulatory Simulation
• Abstract summary: This paper contributes a controlled simulation benchmark rather than a new general framework.<n>We evaluate naive fixed policies, tracking-aware fixed policies, and three adaptive controllers: setpoint, safety-margin, and one-sided control.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Interpreting Control Latents for System Identification via Conditional Flow Matching [13.781988133863209]
  We show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.<n>These results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.
  arXiv  Detail & Related papers  (2026-08-24T22:54:44Z)
• Verification of Adaptive Agentic Controllers through Finite Rule Revision [0.0]
  This paper formulates a bounded verification protocol for adaptive agentic controllers represented by finite symbolic rules.<n> diagnostic failures are mapped to predefined rule-level edits, including rule addition, rule deletion, and priority revision.<n> repaired controllers are then evaluated on held-out simulation seeds or cloned initial states.
  arXiv  Detail & Related papers  (2026-07-07T13:38:01Z)
• Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer [0.0]
  This paper proposes a lightweight machine-coached policy-revision layer for adaptive agent-based regulation.<n>The contribution is not a new optimal controller but does not claim formal guarantees for unrestricted machine coaching.
  arXiv  Detail & Related papers  (2026-06-15T10:36:10Z)
• OGPO: Sample Efficient Full-Finetuning of Generative Control Policies [53.42266064673132]
  Generative control policies (GCPs) have emerged as effective parameterizations for robot learning.<n>This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient for finetuning GCPs.<n>OGPO achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control.
  arXiv  Detail & Related papers  (2026-05-04T18:36:40Z)
• PolicyBank: Evolving Policy Understanding for LLM Agents [51.86716874651299]
  PolicyBank maintains structured, tool-level policy insights and iteratively refines them.<n>PolicyBank closes up to 82% of the gap toward a human oracle.
  arXiv  Detail & Related papers  (2026-04-16T20:29:30Z)
• Separating Diagnosis from Control: Auditable Policy Adaptation in Agent-Based Simulations with LLM-Based Diagnostics [10.25117941762109]
  Mitigating elderly loneliness requires policy interventions that achieve both adaptability and auditability.<n>This work proposes a three-layer framework that separates diagnosis from control to achieve both properties simultaneously.<n>Results demonstrate that explicit control rules outperform end-to-end black-box LLM approaches by 11.7% while preserving full auditability.
  arXiv  Detail & Related papers  (2026-03-24T07:52:10Z)
• Authority-Level Priors: An Under-Specified Constraint in Hierarchical Predictive Processing [0.0]
  Authority-Level Priors (ALPs) are meta-structural constraints defining a regulatory-admissible subset of identity-level hypotheses.<n>ALPs are not additional representational states nor hyperpriors over precision; they constrain which hypotheses are admissible for regulatory control.<n>A computational formalisation restricts policy optimisation to policies generated by authorised hypotheses.
  arXiv  Detail & Related papers  (2026-03-19T13:27:47Z)
• Norm-Governed Multi-Agent Decision-Making in Simulator-Coupled Environments:The Reinsurance Constrained Multi-Agent Simulation Process (R-CMASP) [0.0]
  We show how a domain-calibrated synthetic environment that governed multi-agent coordination yields more stable, coherent, and norm-adherent behaviour than deterministic automation.<n>Results suggest that regulated, simulator-driven decision environments are most naturally modelled as norm-governed, simulator-coupled multi-agent systems.
  arXiv  Detail & Related papers  (2025-12-04T10:30:26Z)
• Active Inference with Reusable State-Dependent Value Profiles [0.0]
  We introduce value profiles: a small set of reusable bundles of value-related parameters assigned to hidden states in a generative model.<n>We evaluate this framework in probabilistic reversal learning, comparing static-precision, entropy-coupled dynamic-precision, and profile-based models.
  arXiv  Detail & Related papers  (2025-12-03T04:11:57Z)
• Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes [59.27926064817273]
  We introduce an exploration-agnostic algorithm, called C-PG, which enjoys global last-iterate convergence guarantees under domination assumptions.<n>We empirically validate both the action-based (C-PGAE) and parameter-based (C-PGPE) variants of C-PG on constrained control tasks.
  arXiv  Detail & Related papers  (2025-06-06T10:29:05Z)
• Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning [62.81324245896717]
  We introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-ite convergence guarantees under (weak) gradient domination assumptions. We numerically validate our algorithms on constrained control problems, and compare them with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-07-15T14:54:57Z)
• Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts [61.929388479847525]
  This paper focuses on the problem of detecting and reacting to changes in the distribution of a sensorimotor controller's observables. The key idea is the design of switching policies that can take conformal quantiles as input. We show how to design such policies by using conformal quantiles to switch between base policies with different characteristics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:30Z)
• Hallucinated Adversarial Control for Conservative Offline Policy Evaluation [64.94009515033984]
  We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, we seek to obtain a (tight) lower bound on a policy's performance. We introduce HAMBO, which builds on an uncertainty-aware learned model of the transition dynamics. We prove that the resulting COPE estimates are valid lower bounds, and, under regularity conditions, show their convergence to the true expected return.
  arXiv  Detail & Related papers  (2023-03-02T08:57:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.