Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track
Yuan Deng, Vahab Mirrokni, Hanrui Zhang
We study posted price auctions and dynamic prior-independent mechanisms for (ROI-constrained) value maximizers. In contrast to classic (quasi-linear) utility maximizers, these agents aim to maximize their total value subject to a minimum ratio of value per unit of payment made. When personalized posted prices are allowed, posted price auctions for value maximizers can be reduced to posted price auctions for utility maximizers. However, for anonymous posted prices, the well-known $\frac 1 2$ approximation for utility maximizers is impossible for value maximizers and we provide a posted price mechanism with $\frac12(1 - 1/e)$ approximation. Moreover, we demonstrate how to apply our results to design prior-independent mechanisms in a dynamic environment; and to the best of our knowledge, this gives the first constant revenue approximation with multiple value maximizers. Finally, we provide an extension to combinatorial auctions with submodular / XOS agents.