Dartmouth Events

Abstract: Traditional economic research often focuses on solutions that are exactly optimal. However, these exact optima frequently prove undesirable, due to concerns surrounding incentives, robustness, fairness, computational efficiency, and more. This has led to the formulation of several renowned "impossibility theorems." More recently, the emerging interdisciplinary field of *economics and computation* has brought about a shift in perspective, embracing an approximation-based approach to classical problems. This shift opens up avenues for novel economic solutions that both hold theoretical significance and provide practical guidelines to complex real-world applications. In this presentation, I will explore this approximation viewpoint applied to various well-established economic concepts, highlighting its power of uncovering the once-impossible possibilities.

Bio: Kangning Wang is currently a Motwani Postdoctoral Fellow at Stanford University. He earned his Ph.D. from Duke University in 2022 and subsequently held the position of J.P. Morgan Research Fellow at the Simons Institute at UC Berkeley. Kangning's research is at the interface of computer science, economics, and operations research, with a focus on developing economic and societal solutions from an algorithmic perspective. His research has been recognized by an ACM SIGecom Doctoral Dissertation Award Honorable Mention, a Duke CS Best Dissertation Award, and Best Paper Awards at SODA 2024 and WINE 2018.