We introduce and consider the problem of effectively organizing a population of workers of varying abilities. We assume that arriving tasks for the workforce are homogeneous, and that each is characterized by an unknown and one-dimensional difficulty value x. Each worker i is characterized by their ability w_i, and can solve the task if and only if x < w_i. If a worker is unable to solve a given task it must be forwarded to a worker of greater ability. For a given set of worker abilities W and a distribution P over task difficulty, we are interested in the problem of designing efficient forwarding structures for W and P. We give efficient algorithms and structures that simultaneously (approximately) minimize both the maximum workload of any worker, and the number of workers that need to attempt a task. We identify broad conditions under which workloads diminish rapidly with the workforce size, yet only a constant number of workers attempt each task.

@Article{heidari2013depth,
author = {Hoda Heidari and Michael Kearns},
title = {Depth-Workload Tradeoffs for Workforce Organization},
journal = {Proceedings of the Conference on Human Computation \& Crowdsourcing (HCOMP)},
year = {2013}}