Empirical Mechanism Design: Methods, with Application to a Supply-chain Scenario
Y Vorobeychik, C Kiekintveld, and MP Wellman
In Seventh ACM Conference on Electronic Commerce, pages 306–315, June 2006.
Copyright (c) 2006, ACM. This is the author's version of the work.
It is posted here by permission of ACM for personal use, not for
redistribution. The definitive version can be found here.
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Abstract
Our proposed methods employ learning and search techniques to
estimate outcome features of interest as a function of mechanism
parameter settings. We illustrate our approach with a design task
from a supply-chain trading competition. Designers adopted several
rule changes in order to deter particular procurement behavior,
but the measures proved insufficient. Our empirical mechanism
analysis models the relation between a key design parameter and
outcomes, confirming the observed behavior and indicating that no
reasonable parameter settings would have been likely to achieve
the desired effect. More generally, we show that under certain
conditions, the estimator of optimal mechanism parameter setting
based on empirical data is consistent.
