Understanding Fairness in Online Allocation

Understanding Fairness in Online Allocation (via Zoom)

Abstract: In many settings, resources are allocated among agents over time without the use of monetary transfers: cloud resources among researchers, food among food banks, vaccines among states, etc. The stated aim is to try and be 'fair' in these allocations...but what exactly do we mean?

Understanding fairness in allocation settings is one of the most beautiful and relevant topics today, with deep connections to market design, optimization and normative philosophy. I will start from a foundational result of Varian's that relates these streams, and in a certain sense, orients our current approach to fairness. Building off from this, I will describe some of our work in (a) understanding market mechanisms for fair online allocation based on artificial credits, (b) the power and limits of optimization approaches, and (c) how modern ideas in control and online decision-making give surprisingly strong algorithms and performance guarantees for practical online fair allocation.

Bio: Sid Banerjee is an Assistant Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell, as well as a field member in the CS and ECE Departments and the Center for Applied Mathematics. His research is on stochastic modeling and control, and the design of algorithms and incentives for large-scale systems. He got his PhD in ECE from UT Austin, and worked as a postdoctoral researcher in the Social Algorithms Lab at Stanford, as well as a technical consultant at Lyft. His work is supported by an NSF CAREER award, and grants from the NSF and ARL.