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A computational framework for analyzing dynamic auctions: The market impact of information sharing
Author(s) -
Asker John,
Fershtman Chaim,
Jeon Jihye,
Pakes Ariel
Publication year - 2020
Publication title -
the rand journal of economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.687
H-Index - 108
eISSN - 1756-2171
pISSN - 0741-6261
DOI - 10.1111/1756-2171.12341
Subject(s) - common value auction , computer science , incentive , consistency (knowledge bases) , microeconomics , information sharing , economics , artificial intelligence , world wide web
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.