How Competition Shapes Information in Auctions
Connections Workshop: Mathematics and Computer Science of Market and Mechanism Design September 07, 2023 - September 08, 2023
Location: SLMath: Eisenbud Auditorium, Online/Virtual
auctions
information acquisition
competition
How Competition Shapes Information in Auctions
We consider auctions where buyers can acquire costly information about their valuations and those of others, and investigate how competition between buyers shapes their learning incentives. In equilibrium, buyers find it cost-efficient to acquire some information about their competitors so as to only learn their valuations when they have a fair chance of winning. We show that such learning incentives make competition between buyers less effective: losing buyers often fail to learn their valuations precisely and, as a result, compete less aggressively for the good. This depresses revenue, which remains bounded away from what the standard model with exogenous information predicts, even when information costs are negligible. Finally, we examine the implications for auction design. First, setting an optimal reserve price is more valuable than attracting an extra buyer, which contrasts with the seminal result of Bulow and Klemperer (1996). Second, the seller can incentivize buyers to learn their valuations, hence restoring effective competition, by maintaining uncertainty over the set of auction participants.
![]() |
How Competition Shapes Information in Auctions
|
Download |
How Competition Shapes Information in Auctions
Please report video problems to itsupport@slmath.org.
See more of our Streaming videos on our main VMath Videos page.