We developed a common algorithmic solution addressing the problem of
resource-constrained outreach encountered by social change organizations with
different missions and operations: Breaking Ground -- an organization that
helps individuals experiencing homelessness in New York transition to permanent
housing and Leket -- the national food bank of Israel that rescues food from
farms and elsewhere to feed the hungry. Specifically, we developed an
estimation and optimization approach for partially-observed episodic restless
bandits under $k$-step transitions. The results show that our Thompson sampling
with Markov chain recovery (via Stein variational gradient descent) algorithm
significantly outperforms baselines for the problems of both organizations. We
carried out this work in a prospective manner with the express goal of devising
a flexible-enough but also useful-enough solution that can help overcome a lack
of sustainable impact in data science for social good.

我们开发了一种常见的算法解决方案，解决了社会变革组织在不同任务和运营中遇到的资源不足的问题。我们针对部分观测的短期不安宁盗贼问题，研究了估计和优化方法，并开发了一种基于汤普森抽样和马尔可夫链恢复的算法，将其应用于一个帮助纽约无家可归者过渡至永久住房的组织和以解救来自农场等地的食物来喂饥饿者的以色列国家食物银行。结果表明，我们的算法在这两个组织的问题上显著优于基准算法。我们以前瞻性的方式进行了这项工作，旨在设计出一种足够灵活但足够有用的解决方案，以克服数据科学在社会公益中可持续影响力不足的问题。