SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary

2026-02-20

AI Summary

This work addresses the challenge of online mediator assignment in Kenya’s judicial system, where limited resource capacity, heterogeneous mediator qualifications, complex task–resource matching, and unknown mediator quality pose significant obstacles. To tackle these issues, the authors propose the SMaRT algorithm, which integrates quadratic programming for real-time high-dimensional combinatorial resource allocation and employs a multi-agent multi-armed bandit framework to learn mediator quality online while respecting soft capacity constraints. This study presents the first unified model combining high-dimensional heterogeneous resource allocation, soft constraints, and online learning. Evaluated on large-scale real-world judicial data, SMaRT significantly outperforms baseline methods, effectively balancing resource utilization and case resolution rates. A randomized controlled trial deployment is currently planned.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Online Learning & Bandits
• Search and Optimization: Algorithm Configuration

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• online resource allocation
• mediator assignment
• capacity constraints
• task compatibility
• judicial mediation

Innovation

• online resource allocation
• multi-agent bandits
• quadratic programming
• soft capacity constraints
• heterogeneous compatibility