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

TL;DR

This paper combines agent-based modeling, reinforcement learning, and explainable AI to simulate and evaluate social behaviors and policy interventions for private well testing in Ireland, aiming to improve public health outcomes.

Contribution

It introduces an integrated ABM framework with XAI and reinforcement learning to analyze and optimize water testing policies based on survey data.

Findings

Free well testing plus communication campaign significantly increased participation.

Free testing alone achieved over 75% testing participation.

Faster policy learning occurred with free testing scenarios, converging in 1000 episodes.

Abstract

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.