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.

本文提出了用最小化预期 ex post 遗憾取代激励相容性要求来适应统计机器学习技术以进行机制设计的方法。通过使用支持向量机和特殊的判别函数结构，可以训练得出具有良好激励性能的隐式支付规则。该方法适用于多维类型的领域，以及需要考虑计算效率的情况。实验结果表明，这种构建产生的付款规则具有低的 ex post 遗憾，并且处罚分类错误在防止 ex post 单独合理性失败方面是有效的。