Time dynamics of overlapping e-auction mechanisms: Information transfer, strategic user behavior and auction revenue

This paper investigates the time dynamics of user strategic patterns and resulting welfares in a series of overlapping multiple online auctions. An auction mechanism is a dynamic game where the valuation and strategic space of bidders determine the outcomes. When those mechanisms exist concurrently or in a series in a market environment, multiple sources are visible and accessible, such that there is likely to be a form of interdependency across the auctions. While heterogeneous bidder behavior has been studied in some literature, the focus is mainly on an individual auction level and the underlying dynamics regarding the interdependency across the auctions in the market has not been explained. We use a two-phased approach to address this discrepancy. First, we classify user strategy using k-means clustering. Then, we characterize the transition pattern of heterogeneous clusters using a dynamic systems framework. Long-term behavior of the system is effectively and efficiently predicted using system parameters. The empirically calibrated simulation, which supports the analytical properties, provides managerial insights in designing multiple overlapping online auction market.