Over at Managerial Econ, Luke Froeb highlights a nice example of the winner’s curse. Like Google, Yahoo uses automated auctions to sell ads. One wrinkle is that some advertisers prefer to pay for impressions, some prefer to pay for clicks, and some prefer to pay only for resulting sales. Yahoo thus needs some mechanism to put these different payment approaches on a comparable footing:
To choose the highest-valued bidder, Yahoo develops predictors of how many clicks and sales result from each impression. For example, if one click occurs for every ten impressions, an advertiser would have to bid more than 10 times as high for a click as for an impression in order to win the auction.
Yahoo was very proud of its predictors, but was puzzled that they systematically over-predicted the actual number of clicks or sales after the auctions closed.
This is the winner’s curse in action. As auction guru (and Yahoo VP) Preston McAfee explains in the paper Luke cites:
In a standard auction context, the winner’s curse states that the bidder who over-estimates the value of an item is more likely to win the bidding, and thus that the winner will typically be a bidder who over-estimated the value of the item, even if every bidder estimates in an unbiased fashion. The winner’s curse arises because the auction selects in a biased manner, favoring high estimates. In the advertising setting, however, it is not the bidders who are over-estimating the value. Instead, the auction will tend to favor the bidder whose click probability is overestimated, even if the click probability was estimated in an unbiased fashion.
McAfee then goes on to explain how Yahoo overcame this self-inflicted winner’s curse, and other strategies to improve auction performance.