A Simulation Analysis of Analytics-Driven Community-Based Re-Integration Programs

We develop a data-driven simulation model in partnership with Tippecanoe County Community Corrections to evaluate assignment policies of reintegration programs. These programs are intended to help clients with their transition back to society after release, with the goal of ending the “revolving door of recidivism.” Leveraging client-level and system-level data, we develop a queueing-based network model to capture the movement of clients in the system. We integrate a personalized recidivism prediction to capture heterogeneous risks, along with estimated effects of reintegration programs from literature. Using simulation, we find that the largest benefit is achieved by implementing any kind of re-integration program, regardless of assignment policy, as the savings in the societal and re-incarceration costs (from recidivism) outweigh program costs. Assignment policy based on predictive analytics achieves a 1.5-time larger reduction in recidivism compared to current practice. In expanding capacity, greater consideration should be given to investing in analytic-driven program assignments.