4 research outputs found
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Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
Strategyproof mechanisms provide robust equilibrium
with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms
that simultaneously satisfy other desirable properties,
we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof “reference” mechanism that solves a problem relaxation.
Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regret-based metrics are not, and can be used for online selection of an effective mechanism.Engineering and Applied Science
Payment Rules through Discriminant-Based Classifiers
In mechanism design it is typical to impose incentive compatibility and then
derive an optimal mechanism subject to this constraint. By replacing the
incentive compatibility requirement with the goal of minimizing expected ex
post regret, we are able to adapt statistical machine learning techniques to
the design of payment rules. This computational approach to mechanism design is
applicable to domains with multi-dimensional types and situations where
computational efficiency is a concern. Specifically, given an outcome rule and
access to a type distribution, we train a support vector machine with a special
discriminant function structure such that it implicitly establishes a payment
rule with desirable incentive properties. We discuss applications to a
multi-minded combinatorial auction with a greedy winner-determination algorithm
and to an assignment problem with egalitarian outcome rule. Experimental
results demonstrate both that the construction produces payment rules with low
ex post regret, and that penalizing classification errors is effective in
preventing failures of ex post individual rationality