用上下文预算机制公平分配志愿者资源，缓解食物救援中的地域不公。
Contextual Budget Bandit for Food Rescue Volunteer Engagement
- 基于上下文的动态预算分配，针对不同区域匹配率调整投入。
- 在真实与合成数据上均优于基线，显著提升救援量与公平性。
- 适合关注社会公益算法、资源公平分配的研究者与实践者。
志愿型食物救援平台通过将过剩食物匹配给有需要的社区来减少食物浪费。这类平台面临双重挑战：维持志愿者参与度并最大化救援食物量。现有算法虽能提升志愿者参与，却加剧了地理分布不均，导致部分社区长期处于劣势。为此，本文提出上下文预算贝叶斯（Contextual Budget Bandit），将上下文依赖的预算分配引入非静止多臂赌博机模型，该模型支持状态化任务。通过向匹配率较低的社区分配更高预算，有效缓解地理差异。为解决此问题，我们设计了一种计算高效的启发式算法；当活跃志愿者稀缺时，其近似效果较差，因此进一步提出保证最优解的Mitosis算法。实验表明，我们的算法在合成与真实食物救援数据集上均超越基线，并实现食物救援的地理公平性。
原文摘要 · Abstract (English)
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.
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