Effect of Information Revelation Policies: A Game-Theoretic Analysis

Geographically dispersed sellers in electronic reverse-marketplaces such as those hosted by Freemarkets are uncertain about the number of competitors they face in any given market-session. Over the course of several market-sessions they learn about the competitive nature of the marketplace. How sellers 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 such as the number of sellers in a session, their bidding patterns, etc are revealed to sellers. Since these policies control what sellers learn and how they bid in future sessions, these policies are a determinant of buyer surplus. Before the advent of the Internet, these revelation policies were not feasible, and studying the impact of these policies was irrelevant. Currently, there is little guidance available to buyers in choosing the appropriate information revelation policy. To address this IT-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 find that the policy that generates the least amount of market structure uncertainty for the sellers maximizes buyer surplus. This contradicts the traditional view that under uncertainty, sellers are worse off and buyers are better off. We investigate this apparent anomaly by comparing the behavior of sellers under market structure uncertainty to that under a framework where sellers are uncertain about their own costs. Based on our analysis, we find that it is the nature of the uncertainty, not the mere presence of uncertainty, that impacts buyer surplus. Acknowledgements: This work was funded in part by NSF CISE/IIS/KDI 9873005 and NSF IIS/0118767.