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Using Machine Learning for Modeling Human Behavior and Analyzing Friction in Generalized Second Price Auctions

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  • Karthik Kannan
  • Vandith Pamuru
  • Yaroslav Rosokha

Abstract

Recent advances in technology have reduced frictions in various markets. In this research, we specifically investigate the role of frictions in determining the efficiency and bidding behavior in a generalized second price auction (GSP) – the most preferred mechanism for sponsored search advertisements. First, we simulate computational agents in the GSP setting and obtain predictions for the metrics of interest. Second, we test these predictions by conducting a human-subject experiment. We find that, contrary to the theoretical prediction, the lower-valued advertisers (who do not win the auction) substantially overbid. Moreover, we find that 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.

Suggested Citation

  • Karthik Kannan & Vandith Pamuru & Yaroslav Rosokha, 2019. "Using Machine Learning for Modeling Human Behavior and Analyzing Friction in Generalized Second Price Auctions," Purdue University Economics Working Papers 1344, Purdue University, Department of Economics.
    • Handle: RePEc:pur:prukra:1344
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    File URL: https://business.purdue.edu/research/working-papers-series/2024/1344.pdf
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