Timezone: »

Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Yuan Deng · Sébastien Lahaie · Vahab Mirrokni · Song Zuo

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @ None #None

Online advertisements are primarily sold via repeated auctions with reserve prices. In this paper, we study how to set reserves to boost revenue based on the historical bids of strategic buyers, while controlling the impact of such a policy on the incentive compatibility of the repeated auctions. Adopting an incentive compatibility metric which quantifies the incentives to shade bids, we propose a novel class of reserve pricing policies and provide analytical tradeoffs between their revenue performance and bid-shading incentives. The policies are inspired by the exponential mechanism from the literature on differential privacy, but our study uncovers mechanisms with significantly better revenue-incentive tradeoffs than the exponential mechanism in practice. We further empirically evaluate the tradeoffs on synthetic data as well as real ad auction data from a major ad exchange to verify and support our theoretical findings.

Author Information

Yuan Deng (Google Research)
Sébastien Lahaie (Google Research)
Vahab Mirrokni (Google Research)
Song Zuo (Google)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors