Per the arXiv abstract (submitted 7 May 2026), Irene Aldridge and 26 coauthors present “Scaling the Queue,” an econometrics paper that applies reinforcement learning to augment complaint intake capacity across six New York City Department of Buildings operational domains. The paper frames each domain as an MDP and trains agents that act as intake routers, assigning incoming complaints to action categories: escalate, batch, defer, inspect now. The authors state the system optimizes throughput, reduces misclassification cost, and explicitly includes equitable classification coverage as a reward objective. The paper reports post-hoc SHAP attribution showing complaint recurrence and neighborhood-level statistics are stronger predictors of actionable violations than raw complaint volume, and the authors note implications for routing given demographic correlates of those features.
