Abstract

Recently fair division theory has emerged as a promising approach for the allocation of multiple computational resources among agents. Previous work on fair division has studied static settings where all the relevant information is known upfront. However in reality, not all the agents are present in the system simultaneously, and for the present agents, not all the resource requirements are known upfront.

In this talk, we consider specific dynamic settings where such information is obtained over time. On the conceptual level, we develop a dynamic model of fair division, and propose desirable axiomatic properties for dynamic resource allocation mechanisms. On the technical level, we construct two novel mechanisms that provably satisfy some of these properties, and analyze their performance using real data. We will also discuss several future extensions and their motivations in real-life computing systems.

Based on joint work with Ian Kash (MSRC) and Ariel Procaccia (Carnegie Mellon University).