Acquiring and Aggregating Information in Societal Contexts

  • Bo Waggoner | University of Pennsylvania

The modern world is full of algorithms that use data to make decisions. The data often come from people; the predictions or decisions often affect people. Yet classically, the study of e.g. learning algorithms does not take into account the behavior or priorities of these participants. So: how does this “societal context” impact the understanding and design of systems that acquire and aggregate information?

This talk will discuss the design of systems that take into account, and indeed leverage, strategic behavior of participants in order to gather information and use it to make inferences, predictions, or decisions. We will break down the kinds of challenges and objectives that arise in these settings and several approaches for overcoming them using tools from game theory as well as machine learning.

Speaker Details

Bo Waggoner is a postdoctoral fellow at the University of Pennsylvania’s Warren Center for Network and Data Sciences. His work focuses on systems for learning or aggregating information in contexts with strategic behavior, privacy, or fairness considerations. He received his PhD from Harvard in 2016 with advisor Yiling Chen.

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