SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
2026-02-20 • Computers and Society
Computers and Society
AI summaryⓘ
The authors study how to assign mediators to cases in Kenya’s judicial system where tasks arrive one by one and must be instantly matched with mediators who differ in skill and availability. They create a new method called SMaRT that learns mediator quality over time and handles complicated overlaps in which mediators can work on which cases while respecting limits on mediator workload. Their approach performs better than existing methods on both simulated problems and real judicial data, balancing case completion rates with workload fairness. They plan to test SMaRT in a real trial soon.
online resource allocationcapacity constraintsmulti-armed banditquadratic programmingmediator assignmenttask schedulingjudicial systemlearning algorithmsrandomized controlled trialKenyan judiciary
Authors
Shafkat Farabi, Didac Marti Pinto, Wei Lu, Manuel Ramos-Maqueda, Sanmay Das, Antoine Deeb, Anja Sautmann
Abstract
Motivated by the problem of assigning mediators to cases in the Kenyan judicial, we study an online resource allocation problem where incoming tasks (cases) must be immediately assigned to available, capacity-constrained resources (mediators). The resources differ in their quality, which may need to be learned. In addition, resources can only be assigned to a subset of tasks that overlaps to varying degrees with the subset of tasks other resources can be assigned to. The objective is to maximize task completion while satisfying soft capacity constraints across all the resources. The scale of the real-world problem poses substantial challenges, since there are over 2000 mediators and a multitude of combinations of geographic locations (87) and case types (12) that each mediator is qualified to work on. Together, these features, unknown quality of new resources, soft capacity constraints, and a high-dimensional state space, make existing scheduling and resource allocation algorithms either inapplicable or inefficient. We formalize the problem in a tractable manner using a quadratic program formulation for assignment and a multi-agent bandit-style framework for learning. We demonstrate the key properties and advantages of our new algorithm, SMaRT (Selecting Mediators that are Right for the Task), compared with baselines on stylized instances of the mediator allocation problem. We then consider its application to real-world data on cases and mediators from the Kenyan judiciary. SMaRT outperforms baselines and allows control over the tradeoff between the strictness of capacity constraints and overall case resolution rates, both in settings where mediator quality is known beforehand and in bandit-like settings where learning is part of the problem definition. On the strength of these results, we plan to run a randomized controlled trial with SMaRT in the judiciary in the near future.