We propose and analyze a family of information processing systems, where a finite set of experts or servers are employed to extract information about a stream of incoming jobs. Each job is associated with a hidden label drawn from some prior distribution. An inspection by an expert produces a noisy outcome that depends both on the job's hidden label and the type of the expert, and occupies the expert for a finite time duration. A decision maker's task is to dynamically assign inspections so that the resulting outcomes can be used to accurately recover the labels of all jobs, while keeping the system stable. Among our chief motivations are applications in crowd-sourcing, diagnostics, and experiment designs, where one wishes to efficiently learn the nature of a large number of items, using a finite pool of computational resources or human agents. We focus on the capacity of such an information processing system. Given a level of accuracy guarantee, we ask how many experts are needed in order to stabilize the system, and through what inspection architecture. Our main result provides an adaptive inspection policy that is asymptotically optimal in the following sense: the ratio between the required number of experts under our policy and the theoretical optimal converges to one, as the probability of error in label recovery tends to zero. Joint work with Laurent Massoulie (MSR-Inria, Palaiseau, France).
Kuang Xu has been an assistant professor of Operations, Information and Technology at the Stanford Graduate School of Business since July 2015. His research seeks to understand fundamental design principles in large-scale dynamic decision-making problems in stochastic systems. Kuang received his PhD in June 2014 from the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology. During the 2014-2015 academic year, he was a postdoctoral fellow at the Microsoft Research- Inria Joint Center in Palaiseau, France.