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

arXiv: https://arxiv.org/abs/2602.18431 · HTML: https://mlsanity.com/paper/2602.18431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health](https://mlsanity.com/paper/2109.08075/) (2021)
- [Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources](https://mlsanity.com/paper/1711.08345/) (2017)
- [Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health](https://mlsanity.com/paper/2202.00916/) (2022)
- [Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices](https://mlsanity.com/paper/1611.09219/) (2016)
- [Bandits with Replenishable Knapsacks: the Best of both Worlds](https://mlsanity.com/paper/2306.08470/) (2023)
- [Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits](https://mlsanity.com/paper/2402.04933/) (2024)
- [Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach](https://mlsanity.com/paper/2410.08377/) (2024)

## Abstract (arXiv)

Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, 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 (newly onboarded mediators), soft capacity constraints (due to the mandate to assign cases without delay), and 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 some stylized instances of the mediator allocation problem. We then turn to considering its application to real-world data on cases and mediators from the Kenyan Judiciary. SMaRT outperforms baselines and allows for controlling the tradeoff between the strictness of the capacity constraints and overall case resolution rates, both in situations where mediator quality is known beforehand and when the problem is bandit-like in that learning is part of the problem definition.