Author List: Sanyal, Pallab;
Information Systems Research, 2016, Volume 27, Issue 2, Page 347-364.
Jump bidding, which refers to bidding above the minimum necessary, is a robust behavior that has been observed in a variety of ascending auctions in the field as well as the laboratory. However, the phenomenon has yet to be studied in combinatorial auctions, which are a type of multiobject auction that allows bidders to bid on a set of objects. Such auctions have been found to be beneficial when objects exhibit synergy, e.g., are complementary. In this paper, we explore jump bidding behavior in combinatorial auctions as a function of design choices of the mechanism. In particular, we examine the effects of price revelation schemes on the nature and extent of jump bidding. Furthermore, we study the effects of jump bidding on the economic performance of the auctions. To conduct our study, first, we develop hypotheses using auction theories and behavioral theories of how people use reference prices as anchors, and second, we conduct a laboratory experiment to test our hypotheses and examine bidder behavior. We find that the nature of the prices that the auctioneer chooses to offer as feedback to the bidders can considerably influence their jump bidding behavior, leading to significant differences in auction outcomes. We demonstrate that in combinatorial auctions, in addition to the theories of jump bidding proposed in the literature, bounded rationality of the bidders plays a part in the nature and extent of jump bidding. Our study reveals that in the cognitively challenging package-bidding environment, bidders often pursue computationally frugal but suboptimal heuristics. Our results have important policy implications for mechanism designers.
Keywords: jump bidding ; combinatorial auctions ; price revelation ; bidder behavior ; bounded rationality
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#91 0.405 auctions auction bidding bidders bid combinatorial bids online bidder strategies sequential prices design price using outcomes behavior theoretical computational efficiency
#75 0.266 behavior behaviors behavioral study individuals affect model outcomes psychological individual responses negative influence explain hypotheses expected theories consequences impact theory
#102 0.130 choice type functions nature paper literature particular implications function examine specific choices extent theoretical design discussion value widely finally adopted
#97 0.118 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing