# Contextual Budget Bandit for Food Rescue Volunteer Engagement

arXiv: https://arxiv.org/abs/2509.10777 · HTML: https://mlsanity.com/paper/2509.10777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 13 Sep 2025.

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

- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2206.03883/) (2022)
- [Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints](https://mlsanity.com/paper/2312.10303/) (2023)
- [I Will Have Order! Optimizing Orders for Fair Reviewer Assignment](https://mlsanity.com/paper/2108.02126/) (2021)
- [Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making](https://mlsanity.com/paper/2403.15640/) (2024)
- [Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health](https://mlsanity.com/paper/2308.09726/) (2023)
- [Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits](https://mlsanity.com/paper/2402.04933/) (2024)
- [Global Rewards in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2406.00738/) (2024)
- [Towards Soft Fairness in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2207.13343/) (2022)
- [Redesigning Service Level Agreements: Equity and Efficiency in City Government Operations](https://mlsanity.com/paper/2410.14825/) (2024)
- [ContextWIN: Whittle Index Based Mixture-of-Experts Neural Model For Restless Bandits Via Deep RL](https://mlsanity.com/paper/2410.09781/) (2024)

## Abstract (arXiv)

Volunteer-based food rescue platforms tackle food waste by matching surplus food to communities in need. These platforms face the dual problem of maintaining volunteer engagement and maximizing the food rescued. Existing algorithms to improve volunteer engagement exacerbate geographical disparities, leaving some communities systematically disadvantaged. We address this issue by proposing Contextual Budget Bandit. Contextual Budget Bandit incorporates context-dependent budget allocation in restless multi-armed bandits, a model of decision-making which allows for stateful arms. By doing so, we can allocate higher budgets to communities with lower match rates, thereby alleviating geographical disparities. To tackle this problem, we develop an empirically fast heuristic algorithm. Because the heuristic algorithm can achieve a poor approximation when active volunteers are scarce, we design the Mitosis algorithm, which is guaranteed to compute the optimal budget allocation. Empirically, we demonstrate that our algorithms outperform baselines on both synthetic and real-world food rescue datasets, and show how our algorithm achieves geographical fairness in food rescue.