A Bayesian Clearing Mechanism for Combinatorial Auctions
A Bayesian Clearing Mechanism for Combinatorial Auctions
复制标题
组合拍卖的贝叶斯清算机制
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Sébastien Lahaie
中科院分区：
文献类型：
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作者：
Gianluca Brero;Sébastien Lahaie
我们将组合拍卖设计问题置于贝叶斯框架中，以便将先验信息纳入拍卖过程并最小化收敛的轮数。我们首先开发了一个代理估值和市场价格的生成模型，使出清价格成为给定观察到的代理估值的最大后验估计。然后，这个生成模型形成了拍卖过程的基础，该过程在精炼代理估值和计算候选清算价格之间交替进行。我们提供了一个使用假设密度过滤来估计估值和期望最大化来计算价格的拍卖实现。对一系列估值领域的实证评估表明，我们的贝叶斯拍卖机制在收敛轮数方面与组合时钟拍卖具有高度竞争力，即使在该基线的最有利价格增量选择下也是如此。
We cast the problem of combinatorial auction design in a Bayesian framework in order to incorporate prior information into the auction process and minimize the number of rounds to convergence. We first develop a generative model of agent valuations and market prices such that clearing prices become maximum a posteriori estimates given observed agent valuations. This generative model then forms the basis of an auction process which alternates between refining estimates of agent valuations and computing candidate clearing prices. We provide an implementation of the auction using assumed density filtering to estimate valuations and expectation maximization to compute prices. An empirical evaluation over a range of valuation domains demonstrates that our Bayesian auction mechanism is highly competitive against the combinatorial clock auction in terms of rounds to convergence, even under the most favorable choices of price increment for this baseline.