Effect of information feedback on bidder behavior in continuous combinatorial auctions

Combinatorial auctions-in which bidders can bid on combinations of goods-can increase the economic efficiency of a trade when goods have complementarities. Recent theoretical developments have lessened the computational complexity of these auctions, but the issue of cognitive complexity remains an unexplored barrier for the online marketplace. This study uses a data-driven approach to explore how bidders react to the complexity in such auctions using three experimental feedback treatments. Using cluster analyses of the bids and the clicks generated by bidders, we find three stable bidder strategies across the three treatments. Further, these strategies are robust for separate experiments using a different setup. We also benchmark the continuous auctions against an iterative form of combinatorial auction-the combinatorial clock auction. The enumeration of the bidding strategies across different types of feedback, along with the analysis of their economic implications, is offered to help practitioners design better combinatorial auction environments.