Impact of information feedback on bid characteristics in continuous combinatorial auctions

Combinatorial auctions represent a sophisticated market mechanism that has been shown to enhance the economic efficiency of a trade when goods have complementarities. However, the complexity of participation in such auctions has inhibited their general use in the online marketplace. In order to better understand the dynamics of these auctions, based on infrastructure developed in earlier research, we test the effects of varying levels of feedback upon characteristics of bids in a continuous combinatorial auction. We use cluster analysis to categorize bids into four distinct groups in each of the three treatments that correspond to the three feedback policies. We show that, certain types of bids have a greater potential to garner profits for bidders; however, bidders' ability to exploit available information can be influenced by the amount and type of information provided to them.