This paper addresses the challenge of enhancing public policy decision-making by efficiently solving principal-agent models (PAMs) for public-private partnerships, a critical yet computationally demanding problem. We develop a fast, interpretable, and generalizable approach to support policy decisions under these settings.
We propose an interpretable ensemble heuristic (EH) that integrates Machine Learning (ML), Operations Research (OR), and Game Theory. First, we reformulate a PAM as a mixed-integer program to improve efficiency. Next, we solve thousands of PAM instances under varying configurations to generate training data for ensemble tree-based ML models that identify key solution patterns. These patterns form a hierarchical heuristic that provides feasible and interpretable solutions. We demonstrate the EH’s efficacy in managing the Emerald Ash Borer (EAB) infestation, an urgent public-policy threat to U.S. ash trees. Empirical results show that the EH produces high-quality solutions with 1–2\% optimality gaps while significantly reducing computational time compared to exact optimization. Furthermore, the heuristic explains predictions using an average of 4.5 of 9 input features, enhancing transparency.
Our findings demonstrate that the EH promotes rapid, informed, and accountable policy decisions by balancing interpretability with computational efficiency. Practically, it supports real-time simulations for stakeholders without specialized ML or OR expertise. Methodologically, it demonstrates a robust integration of optimization and machine learning to solve complex policy models. Beyond the EAB application, this approach provides a scalable framework for real-time decision support where transparency and justification are paramount.