Analyzing Frictions in Generalized Second-Price Auction Markets

We investigate the role of frictions in determining the efficiency and bidding behavior in a generalized second-price auction-the most preferred mechanism for sponsored-search advertisements. In particular, we take a twofold approach of Q-learning-based computational simulations in conjunction with human-subject experiments. We find that the lower valued advertisers (who do not win the auction) exhibit highly exploratory behavior. Moreover, we find the presence of market frictions moderates this phenomenon and results in higher allocative efficiency. These results have implications for policymakers and auction-platform managers in designing incentives for more efficient auctions.