Environmental policy in the context of complex systems: Statistical optimization and sensitivity analysis for ABMs

2026-02-18

AI Summary

This study addresses the high computational cost and limited efficiency of agent-based models (ABMs) in environmental policy optimization by proposing a machine learning framework that integrates reinforcement learning with statistical sensitivity analysis. Using the Sugarscape model as an experimental platform, the approach substantially improves policy search efficiency while identifying optimal policies that outperform baseline strategies. Importantly, it also yields economically interpretable insights into dynamic agent behaviors and parameter sensitivities. By innovatively combining reinforcement learning with explainable analysis, this work not only enhances the practical applicability of ABMs in complex human–environment systems but also provides policymakers with a theoretically grounded and computationally efficient decision-support tool for environmental governance.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Coupled human-environment systems are increasingly being understood as complex adaptive systems (CAS), in which micro-level interactions between components lead to emergent behavior. Agent-based models (ABMs) hold great promise for environmental policy design by capturing such complex behavior, enabling a sophisticated understanding of potential interventions. One limitation, however, is that ABMs can be computationally costly to simulate, which hinders their use for policy optimization. To address this, we propose a new statistical framework that exploits machine learning techniques to accelerate policy optimization with costly ABMs. We first develop a statistical approach for sensitivity testing of the optimal policy, then leverage a reinforcement learning method for efficient policy optimization. We test this framework on the classic ``Sugarscape'' model, an ABM for resource harvesting. We show that our approach can quickly identify optimal and interpretable policies that improve upon baseline techniques, with insightful sensitivity and dynamic analyses that connect back to economic theory.

Problem

• agent-based models
• environmental policy
• complex adaptive systems
• policy optimization
• computational cost

Innovation

• agent-based modeling
• policy optimization
• machine learning
• sensitivity analysis
• reinforcement learning