Black-Box Optimization with Implicit Constraints for Public Policy
This work addresses complex decision-making in public policy, but it is incremental as it builds on existing BBO methods with a novel constraint-handling approach.
The paper tackled the challenge of applying black-box optimization to public policy problems with implicit constraints, such as police redistricting, by introducing the CageBO framework, which improved performance and efficiency in a case study on Atlanta.
Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police redistricting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces a novel BBO framework, termed as the Conditional And Generative Black-box Optimization (CageBO). This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space. The CageBO efficiently handles the implicit constraints often found in public policy applications, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through a case study on large-scale police redistricting problems in Atlanta, Georgia. Our results reveal that our CageBO offers notable improvements in performance and efficiency compared to the baselines.