Society is run by algorithms, and in many cases, these algorithms interact with participants who have a stake in the outcome. The participants may behave strategically in an attempt to "game the system," resulting in unexpected or suboptimal outcomes. In order to accurately predict an algorithm's outcome and quality, we must design it to be robust to strategic manipulation. This is the subject of algorithmic mechanism design, which borrows ideas from game theory and economics to design robust algorithms. 

 

In this talk, I will show how results from the theoretical foundations of algorithmic mechanism design can be used to solve problems of societal concern. I will focus on applications in carbon license allocations, health insurance markets, and online labor markets.

Kira Goldner is a postdoctoral researcher in the Computer Science Department and at the Data Science Institute at Columbia University, hosted by Tim Roughgarden, and supported by NSF Math Sciences and Data Science Institute fellowships. Goldner uses her background in the foundations of mechanism design to address societal problems, e.g., in healthcare, climate change, and privacy.  She has also worked on core problems concerning revenue maximization, simplicity, and robustness.  As part of this agenda, Goldner co-founded Mechanism Design for Social Good (MD4SG), an interdisciplinary initiative working to improve access to opportunity for historically disadvantaged communities.  She received her Ph.D. in computer science and engineering from the University of Washington under the advisement of Anna Karlin and was supported by a Microsoft Research PhD Fellowship and a Google Anita Borg Scholarship. She has received many awards for her work, including the EC 2019 Best Paper with a Student Lead Author Award and the EC 2020 Best Presentation by a Student or Postdoctoral Researcher Award.