Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
Edward Glaeserrew Hillis Scott Duke Kominers Michael Luca
2016
Abstract
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. A simple model suggests that open tournaments dominate consulting contracts when cities can tolerate risk and when there is enough labor with low opportunity costs. We also report on an inexpensive Boston-based restaurant tournament, which yielded algorithms that proved reasonably accurate when tested "out-of-sample" on hygiene inspections.
