Dynamic Budget Throttling in Repeated Second-Price Auctions
Authors: Zhaohua Chen, Chang Wang, Qian Wang, Yuqi Pan, Zhuming Shi, Zheng Cai, Yukun Ren, Zhihua Zhu, Xiaotie Deng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides a theoretical panorama of a single advertiser s dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser s values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. |
| Researcher Affiliation | Collaboration | Zhaohua Chen*1, Chang Wang*2, Qian Wang*1, Yuqi Pan3, Zhuming Shi4, Zheng Cai5, Yukun Ren5, Zhihua Zhu5, Xiaotie Deng1,6 1 CFCS, School of Computer Science, Peking University 2 Northwestern University 3 School of Electronics Engineering and Computer Science, Peking University 4 Stony Brook University 5 Tencent Technology (Shenzhen) Co., Ltd. 6 CMAR, Institute for Artificial Intelligence, Peking University |
| Pseudocode | Yes | Algorithm 1. The OGD-CB Algorithm. |
| Open Source Code | No | The paper does not contain any statements about releasing code, nor does it provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not perform experiments on datasets, thus no information about public datasets, links, DOIs, or specific citations for datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on datasets, thus no information about training/test/validation splits or cross-validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe running empirical experiments, therefore no specific hardware (CPU, GPU models, memory, etc.) used for experiments is mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters, training configurations, or system-level settings. |