How Decision Complexity Affects Outcomes in Combinatorial Auctions

Procurement mechanisms used by businesses have evolved from simple single-item auctions to complex multi-unit, multi-attribute, and multi-object auctions and their combinations. These state-of-the-art mechanisms offer many economic benefits but also introduce challenges such as the increased complexity in decision-making for the participants. Our primary goal in this study is to study how decision complexity affects the economic outcomes and acceptability of an advanced economic mechanism: the continuous combinatorial auction. We identify three aspects of complexity—auction size, competition, and the number of active bids. We examine these complexity aspects with five types of auctions conducted with a general consumer population in an experimental environment with real payoffs. We find that various aspects of complexity affect economic outcomes in different ways. Furthermore, competition is a critical factor for conducting efficient auctions. We also conduct a secondary analysis of bidder behavior through a granular examination of clickstream data collected during the auctions. We find that decision complexity influences bidder strategies leading to differences in auction outcomes. Based on our analysis, we develop valuable insights for auction practitioners.