Analyzing Frictions in Generalized Second-Price Auction Markets

Generalized second-price auction is the preferred mechanism for sponsored-search advertisements. In this paper, we take a twofold approach using Q-learning-based simulations and human-subject experiments to show that the low-value advertisers (who do not win the auction) exhibit highly exploratory behavior and impact the allocative efficiency of the mechanism. Moreover, we find the presence of bid-adjustment frictions (e.g., bid fee) moderates this phenomenon and results in higher allocative efficiency of the auction. Our focus on the bid-adjustment costs is motivated by the fact that both the sponsored-search platforms and policymakers can best observe and influence these types of frictions as compared with frictions that are difficult to observe (e.g., resources spent on the analysis of the market and sophistication of algorithms by the advertisers).