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[2602.18431] SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Summary
The paper presents SMaRT, an innovative algorithm for online resource allocation in the Kenyan judiciary, focusing on mediator assignment to cases while optimizing for quality and capacity constraints.
Why It Matters
This research addresses a critical challenge in the Kenyan judicial system by improving the efficiency of mediator assignments. By leveraging machine learning techniques, it aims to enhance case resolution rates, which can lead to better access to justice and improved outcomes in legal processes.
Key Takeaways
- SMaRT algorithm effectively assigns mediators to cases based on quality and capacity constraints.
- The approach utilizes a multi-agent bandit framework for learning mediator effectiveness.
- Real-world application shows SMaRT outperforms traditional methods in mediator allocation.
- The algorithm allows control over trade-offs between capacity strictness and case resolution rates.
- Future plans include a randomized controlled trial to validate SMaRT's effectiveness in practice.
Computer Science > Computers and Society arXiv:2602.18431 (cs) [Submitted on 20 Feb 2026] Title:SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary Authors:Shafkat Farabi, Didac Marti Pinto, Wei Lu, Manuel Ramos-Maqueda, Sanmay Das, Antoine Deeb, Anja Sautmann View a PDF of the paper titled SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary, by Shafkat Farabi and 6 other authors View PDF HTML (experimental) 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 allocati...