A computational framework for analyzing dynamic auctions: The market impact of information sharing

• 成果类型: Article
• 署名作者: Asker, John; Fershtman, Chaim; Jeon, Jihye; Pakes, Ariel
• 署名单位: University of California System; University of California Los Angeles; National Bureau of Economic Research; Tel Aviv University; Boston University; Harvard University
• 刊物名称: RAND JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0741-6261
• DOI: 10.1111/1756-2171.12341
• 发表日期: 2020
• 页码: 805-839
• 关键词: Collusion; COMPETITION; exchange

摘要

This article develops a computational framework to analyze dynamic auctions and uses it to investigate the impact of information sharing among bidders. We show that allowing for the dynamics implicit in many auction environments enables the emergence of equilibrium states that can only be reached when firms are responding to dynamic incentives. The impact of information sharing depends on the extent of dynamics and provides support for the claim that information sharing, even of strategically important data, need not be welfare reducing. Our methodological contribution is to show how to adapt the experience-based equilibrium concept to a dynamic auction environment and to provide an implementable boundary-consistency condition that mitigates the extent of multiple equilibria.