Optimal Payment Methods of Platform Position Auctions
15:30-17:00, Friday, December 17, 2021
Tencent Meeting (Meeting ID: 169 922 724)
Dr. Dazhong WANG is the assistant professor of Business School, Nanjing University. He was granted the Ph.D. in Economics from Shanghai University of Finance and Economics in June 2015, and conducted Post-doc research at Sun Yat-sen University. Dr. Dazhong WANG's research interests lie at Digital Economics, Innovation Economics, and Mechanism Design. His research works have been published in Journal of Mathematical Economics, Social Choice and Welfare, Economics Letters, and Economic Research Journal (Jingji Yanjiu).
In the platform economy, positions are very important network resources. Thus, how to effectively allocate positions is an important issue to promote sustainable development of platform economy. From the perspective of platform's revenue maximization, this paper studies the choice of auction payment method. First, based on the generalized second price auction mechanism (GSP mechanism) which is widely used in practice, this paper analyzes the seller's equilibrium bidding strategies under "pay-per-click"(PPC), "payment per transaction with fixed payment"(PPTF), and "pay per transaction with revenue-sharing" (PPTS), respectively. Then compares these three payment methods in terms of three criteria: expected revenue of platform, expected payoff of the sellers, as well as the allocative efficiency. It shows that the PPTS brings the highest expected revenue to the platform, followed by the PPTF and then is the PPC. In terms of the seller's expected payoff, the PPC yields the most, and then is the PPTF, while the PPTS generate the least. When it comes to allocative efficiency, both the PPTF and the PPTS are more efficient than the PPC. This study could provide theoretical reference for the design of position auction mechanism in platform economy.
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