最优按投标付费采购机制设计

Je-ok Choi, D. Sabán, G. Weintraub
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引用次数: 0

摘要

问题定义:我们考虑的机制设计问题是找到一个最优的按出价付费机制,在这个机制中,平台选择各种各样的供应商来平衡两个目标之间的权衡:提供足够的品种以适应不同的买家,但价格低廉。学术/实践相关性:现代购买渠道,包括电子商务和公共采购,通常由一个中介交易的平台组成。通常,这些平台实施简单透明的机制来诱导供应商直接参与,这通常导致供应商设定价格的按出价付费(或首价)机制。方法:我们引入了一种新的分类机制,我们称之为k-软储备(k- srs):如果至少有k个供应商选择低于软储备价格的价格,那么只有这些供应商被添加到分类中;否则,将添加所有供应商。结果:我们展示了k- sr对一类程式化对称模型的最优性,以推导出这些机制背后的直觉。然后,通过广泛的数值模拟,我们提供了k- sr在更一般和现实设置中的鲁棒性的证据。管理启示:我们的研究结果为如何在实践中优化按投标付费分类机制提供了直观和易于使用的处方,重点是公共采购设置。资金:J. Choi感谢三星奖学金和斯坦福大学商学院提供的资金支持。G.温特劳布感谢约瑟夫和劳里·莱科布在2018-2019学年期间作为斯坦福大学商学院约瑟夫和劳里·莱科布教师学者的支持。补充材料:在线附录可在https://doi.org/10.1287/msom.2022.1180上获得。
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The Design of Optimal Pay-as-Bid Procurement Mechanisms
Problem definition: We consider the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers to balance the tradeoff between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. Academic/practical relevance: Modern buying channels, including e-commerce and public procurement, often consist of a platform that mediates transactions. Frequently, these platforms implement simple and transparent mechanisms to induce suppliers’ direct participation, which typically results in pay-as-bid (or first-price) mechanisms where suppliers set their prices. Methodology: We introduce a novel class of assortment mechanisms that we call k-soft reserves (k-SRs): If at least k suppliers choose a price below the soft-reserve price, then only those suppliers are added to the assortment; otherwise, all the suppliers are added. Results: We show the optimality of k-SRs for a class of stylized symmetric models to derive the intuition behind these mechanisms. Then, through extensive numerical simulations, we provide evidence of the robustness of k-SRs in more general and realistic settings. Managerial implications: Our results give intuitive and simple-to-use prescriptions on how to optimize pay-as-bid assortment mechanisms in practice, with an emphasis on public procurement settings. Funding: J. Choi thanks the Samsung Scholarship and Stanford Graduate School of Business for financial support. G. Weintraub thanks Joseph and Laurie Lacob for the support during the 2018–2019 academic year as a Joseph and Laurie Lacob Faculty Scholar at Stanford Graduate School of Business. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1180 .
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