Departmental Colloquium
Jason Hartline
Northwestern University
Data Science and Mechanism Design
Abstract: Computer systems have become the primary mediator of social and
economic interactions. A defining aspect of such systems is that the
participants have preferences over system outcomes and will manipulate
their behavior to obtain outcomes they prefer. Such manipulation
interferes with data-driven methods for designing and testing system
improvements. A standard approach to resolve this interference is to
infer preferences from behavioral data and employ the inferred
preferences to evaluate novel system designs.
In this talk I will describe a method for estimating and comparing the
performance of novel systems directly from behavioral data from the
original system. This approach skips the step of estimating
preferences and is more accurate. Estimation accuracy can be further
improved by augmenting the original system; its accuracy then compares
favorably with ideal controlled experiments, a.k.a., A/B testing,
which are often infeasible. A motivating example will be the
paradigmatic problem of designing an auction for the sale of
advertisements on an Internet search engine.
Friday October 9, 2015 at 3:00 PM in SEO 636