A tutorial at IJCAI 2018, July 15, 2018, Stockholm, Sweden
Societies around the world face challenges of enormous scale: preventing and treating disease, shifting to renewable energy sources, confronting poverty, 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 incentives for adopting renewable energy). However, such interventions are 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 has witnessed incredible growth, providing us with unprecedented computational tools with which to contribute to solving societal problems. This tutorial will introduce AI students and researchers to the use of algorithmic techniques to enhance the delivery of policy or community-level interventions aimed at addressing social challenges, an emerging area which we refer to as algorithmic social intervention. We will focus on four related sub-areas: social networks, epidemiology, safety analytics, and agent-based modeling. The tutorial will cover existing methodologies in these areas, highlight the connections and differences between them, and discuss open problems in both the development of new algorithmic techniques and deployment in real-world settings. The goal of this tutorial is to provide a unified view of computational methods for guiding social interventions and spark new research cutting across the sub-areas we cover.
Influence maximization: classical algorithmic techniques and challenges in deployment
Beyond influence maximization: modifying network structure, peer group interventions, and other network intervention problems
Learning models from data
Introduction: SIS/SIR-type models
Compartmental models and algorithmic approaches
Graph-based models; immunization problems and algorithms
Bipartite graph models and epidemic control algorithms
Spatio-temporal safety analytics
Urban incident forecasting: the tale of crime and traffic accidents
Minimizing response time
Models for agent decision making
Learning and validating models with empirical data
Optimizing policies using ABMs
Bryan Wilder is a PhD student at the University of Southern California, where he is advised by Milind Tambe. His research focuses on algorithmic social intervention: computational approaches for optimally targeting social and behavioral interventions in domains such as public health and homelessness. For instance, his algorithms have been deployed by Los Angeles area homeless shelters to prevent HIV spread. He is supported by a National Science Foundation Graduate Research Fellowship and his work was nominated for a best paper award at AAMAS 2017.
Yevgeniy Vorobeychik is an Assistant Professor of Computer Science, Computer Engineering, and Biomedical Informatics at Vanderbilt University. Previously, he was a Principal Research Scientist at Sandia National Laboratories. Between 2008 and 2010 he was a post-doctoral research associate at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design, optimization, agent-based modeling, complex systems, network science, and epidemic control. Dr. Vorobeychik received an NSF CAREER award in 2017, and was invited to give an IJCAI-16 early career spotlight talk. He was nominated for the 2008 ACM Doctoral Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.