用智能体模型和可解释AI模拟爱尔兰农户用水行为，优化检测政策。
Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland
- 构建融合强化学习与可解释AI的智能体模型，模拟私人水井检测决策。
- 免费检测加宣传可使参与率从5%升至77.5%，最快1000轮达成稳定行为。
- 通过SHAP分析揭示关键影响因素，助力制定精准公共卫生干预措施。
爱尔兰约50%农村人口依赖不受监管的私人水井，易受农业径流和未处理污水污染。全国高发的志贺毒素大肠杆菌（STEC）等水源性传染病与井水暴露相关。定期井水检测对公共健康至关重要，但缺乏政府激励使家庭承担全部费用。理解环境、认知与物质因素对检测行为的影响极为重要。本研究基于国家调查数据，采用智能体模型（ABM）模拟政策干预效果。该框架整合深度Q网络强化学习与可解释人工智能（XAI），使用递归特征消除（RFE）结合十折交叉验证筛选关键特征，以SHAP（Shapley Additive Explanations）提供政策建议的可解释性。测试了14种政策情景。最有效方案为“免费检测+宣传运动”，使561个智能体中有435人参与，较基线约5%显著提升；“免费检测+监管”也表现良好，433人参与。仅免费检测即推动参与率超75%，部分个体年检多次。含免费检测的方案学习效率更高，1000轮内收敛，其他需2000轮，表明适应更慢。研究证明了ABM与XAI在公共卫生政策中的价值，为环境健康行为干预提供了可评估框架。
原文摘要 · Abstract (English)
Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. Periodic well testing is essential for public health, yet the lack of government incentives places the financial burden on households. Understanding environmental, cognitive, and material factors influencing well-testing behavior is critical. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data. The ABM framework, designed for private well-testing behavior, integrates a Deep Q-network reinforcement learning model and Explainable AI (XAI) for decision-making insights. Key features were selected using Recursive Feature Elimination (RFE) with 10-fold cross-validation, while SHAP (Shapley Additive Explanations) provided further interpretability for policy recommendations. Fourteen policy scenarios were tested. The most effective, Free Well Testing plus Communication Campaign, increased participation to 435 out of 561 agents, from a baseline of approximately 5 percent, with rapid behavioral adaptation. Free Well Testing plus Regulation also performed well, with 433 out of 561 agents initiating well testing. Free testing alone raised participation to over 75 percent, with some agents testing multiple times annually. Scenarios with free well testing achieved faster learning efficiency, converging in 1000 episodes, while others took 2000 episodes, indicating slower adaptation. This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.
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