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

John Asker*, Chaim Fershtman, Jihye Jeon, Ariel Pakes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)805-839
Number of pages35
JournalRAND Journal of Economics
Volume51
Issue number3
DOIs
StatePublished - 1 Sep 2020

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