Effects of Information-Revelation Policies Under Market-Structure Uncertainty

Geographically dispersed sellers in electronic reverse marketplaces such as those hosted by market-makers like Ariba are uncertain about the number of competitors they face in any given market session. We refer to this uncertainty about the number of competitors as market-structure uncertainty. Over the course of several market sessions sellers learn about the competitive nature of the marketplace. How they learn to reduce the market-structure uncertainty depends on the market-transparency scheme, or the revelation policy adopted. A revelation policy determines the extent to which information—the number of sellers in a session, their bidding patterns, etc.—is revealed to sellers. Because these policies control what sellers learn and how they bid in future sessions, they determine buyer surplus. Possibly because market-structure uncertainty is more prevalent in information technology-enabled marketplaces than traditional ones, prior work has not addressed the impact of revelation policies on this type of uncertainty. Currently, there is little guidance available to buyers in choosing the appropriate revelation policy. To address this information-technology-enabled problem, we use game theory to compare the buyer surplus generated under a set of revelation policies commonly used in electronic reverse marketplaces. We demonstrate that the policy that generates the least amount of market-structure uncertainty for the sellers always maximizes buyer surplus. We further investigate to provide intuition regarding how bidders’ reactions to overcome uncertainty differs with the nature of uncertainty, and how those reactions impact buyer surplus.

• 저널Management Science · 53(8) · 1234–1248
• 토픽Auction Theory and Applications · Management Science and Operations Research
• DOI10.1287/mnsc.1060.0688
• 저자Ashish Arora, Amy Greenwald, Karthik Kannan, Ramayya Krishnan