Original Article
A computational framework for analyzing dynamic auctions: The market impact of information sharing
John Asker, Chaim Fershtman, Jihye Jeon, Ariel Pakes,
John Asker, Chaim Fershtman, Jihye Jeon, Ariel Pakes,
We would like to thank numerous seminar audiences for their comments and questions. We are particularly grateful to Gautam Gowrisankaran and Mark Satterthwaite for extensive comments. El Hadi Caoui and Chuqing Jin provided excellent research assistance. Financial Assistance from the US–Israel Binational Science Foundation is greatly appreciated.
Abstract
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.
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