Title: Learning and Trust in Auction Markets,
Abstract: In this paper, we study behavior of bidders in an experimental launch of a
new advertising auction platform by Zillow, as Zillow switched from negotiated
contracts to using auctions in several geographically isolated markets. A
unique feature of this experiment is that the bidders in this market are real
estate agents that bid on their own behalf, not using third-party
intermediaries. To help bidders, Zillow also provided a recommendation tool
that suggested a bid for each bidder.
Our main focus in this paper is on the decisions of bidders whether or not to
adopt the platform-provided bid recommendation. We observe that a significant
proportion of bidders do not use the recommended bid. Using the bid history of
the agents we infer their value, and compare the agents' regret with their
actual bidding history with results they would have obtained following the
recommendation. We find that for half of the agents not following the
recommendation, the increased effort of experimenting with alternate bids
results in increased regret, i.e., they get decreased net value out of the
system. The proportion of agents not following the recommendation slowly
declines as markets mature, but it remains large in most markets that we
observe. We argue that the main reason for this phenomenon is the lack of trust
in the platform-provided tool.
Our work provides an empirical insight into possible design choices for
auction-based online advertising platforms. While search advertising platforms
(such as Google or Bing) allow bidders to submit bids on their own, many
display advertising platforms (such as Facebook) optimize bids on bidders'
behalf and eliminate the need for bids. Our empirical analysis shows that the
latter approach is preferred for markets where bidders are individuals, who
don't have access to third party tools, and who may question the fairness of
platform-provided suggestions.