Societies around the world face an array of difficult challenges: preventing and treating disease, confronting poverty and homelessness, and a range of other issues impacting billions of people. In response, governments and communities deploy interventions addressing these problems (e.g., outreach campaigns to enroll patients in treatment or offering subsidized public housing). However, these interventions are always subject to limited resources and are deployed under considerable uncertainty about properties of the system; deciding manually on the best way to deploy an intervention is extremely difficult.
At the same time, research in artificial intelligence and multiagent systems has witnessed incredible growth, providing us with unprecedented computational tools with which to contribute to solving societal problems. This tutorial will introduce multiagent systems students and researchers to the use of techniques from optimization and machine learning to enhance the delivery of policy or community-level interventions aimed at addressing social challenges. We will focus in particular on three application areas: public health, social work, and healthcare. On a technical level, the tutorial will introduce methods for aggregating value judgments from multiple agents about an intervention’s goals, discuss the creation of agents which can learn and plan under uncertainty to aid in resource allocation, and showcase examples of how these techniques are used in concrete, deployed applications. The goal of this tutorial is to provide a unified view of computational methods for resource allocation for social good and spark new research cutting across the sub-areas we cover.