Optimal Auction Design for Mixed Bidders
Authors: Xiaohui Bei, Pinyan Lu, Zhiqi Wang, Tao Xiao, Xiang Yan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we investigate revenue-maximizing auction design for selling a single item to a mix of UMs and VMs. Crucially, we assume the UM/VM type is private information of a bidder. Under this setting, we first characterize the optimal auction structure for auctions with a single bidder. We also extend our study to multi-bidder setting and present an algorithm for deriving the optimal lookahead auction with multiple mixed types of bidders. ... We then apply the variation method to solve the optimal allocation function and present an algorithm to compute the optimal mechanism for a single bidder. We also provide an analytical description of the optimal mechanism when the bidder s value follows uniform distribution; Finally, we provide an algorithm to solve the optimal lookahead auction in the multiple mixed bidders setting. ... Figure 1: Optimal allocation when n = 2, q = 0.4, the value of UMs and VMs both follow uniform distribution on [0, 1] |
| Researcher Affiliation | Collaboration | Xiaohui Bei1, Pinyan Lu2, Zhiqi Wang2, Tao Xiao3, Xiang Yan3 1School of Physical and Mathematical Sciences, Nanyang Technological University 2Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics 3Huawei Taylor Lab |
| Pseudocode | Yes | Algorithm 1: Find the optimal allocation for a single bidder; Algorithm 2: Search optimal allocation for multiple bidders |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | In this paper, we assume FU is regular, and there is no assumption on FV. ... Figure 1 illustrates the optimal allocation for both bidder types when n = 2, q = 0.4, the value of UMs and VMs both follow uniform distribution on [0, 1]. The paper uses theoretical distributions (uniform distribution) for modeling purposes, not empirical datasets. |
| Dataset Splits | No | The paper uses theoretical distributions (uniform distribution) for modeling and numerical illustrations, and therefore does not involve empirical datasets or their splits. |
| Hardware Specification | No | The paper does not specify any hardware details used for running experiments or simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers. |
| Experiment Setup | No | Figure 1 illustrates the optimal allocation for both bidder types when n = 2, q = 0.4, the value of UMs and VMs both follow uniform distribution on [0, 1]. The paper describes parameters for its theoretical model and numerical algorithms (e.g., n, q, distribution type) rather than specific hyperparameter values or training configurations for an empirical experiment. |