The Mathematics (and Ethics) of Fair Resource Allocation by Dr. Siddhartha Banerjee (Cornell University)

Abstract: In many settings, a finite supply of some public resource is allocated among people over time, without using money: a computing cluster among researchers, food among food-banks, medical supplies between hospitals, funding between non-profit projects, fellowships among admitted students, etc. The underlying aim is often to try and be ‘fair’ in these allocations…but what exactly do we mean?

Understanding fairness in sequential decision-making is one of the most urgent (but also, intellectually beautiful) topics today, with deep connections to control theory, economics, optimization, and normative philosophy. I will demonstrate this using two case studies: one where we know the exact numerical utility each agent gets from being allocated; and one where we do not, but still want some rules as to which agents should have priority over others, and why. Through these examples, I will try to convince you of one big idea: as applied mathematicians, our main task should be to characterize trade-offs between fairness and efficiency in different settings, as a guide to policy-makers to help determine what is socially relevant. Luckily, doing so turns out to be very intellectually rewarding — I will try and describe surprising connections between our problems and a host of other topics — physics, information theory, combinatorics, computational complexity, and many more!

Bio:  Sid Banerjee is an associate professor in the School of Operations Research at Cornell, working on topics at the intersection of data-driven decision-making, network algorithms and market design. His research is supported by grants from the NSF (including an NSF CAREER award), ARO, and AFOSR, and has received multiple awards including the Erlang Prize (2022), and best paper awards at ACM SIGMETRICS (2026) and the INFORMS Applied Probability Society (2021). He completed his PhD from the ECE Department at UT Austin, and was a postdoctoral researcher in the Social Algorithms Lab at Stanford. He also served as a technical consultant with the research science group at Lyft from 2014-18.