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Crowdsourced Bayesian Auctions

Abstract: 
"We investigate the problem of optimal mechanism design, where an auctioneer wants to sell a set of goods to buyers, in order to maximize revenue. In a Bayesian setting the buyers' valuations for the goods are drawn from a prior distribution D, which is often assumed to be known by the seller. In this work, we focus on cases where the seller has no knowledge at all, and the buyers know each other better than the seller knows them. In our model, D is not necessarily common knowledge. Instead, each buyer individually knows a posterior distribution associated with D. Since the seller relies on the buyers' knowledge to help him set a price, we call these types of auctions crowdsourced Bayesian auctions."
Author: 
Pablo Azar, Jing Chen, Silvio Micali
Institution: 
Computer Science and Artificial Intelligence Laboratory, MIT
Year: 
2012
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