A Strategy-Proof Online Auction with Time Discounting Values
Authors: Fan Wu, Junming Liu, Zhenzhe Zheng, Guihai Chen
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented our design of online auction with time discounting values (named OASES in the evaluation), and compare its performance with the off-line VCG mechanism (named Off-line VCG in the evaluation)...Our numerical results show that our design achieves good performance in terms of social welfare, revenue, average winning delay, and average valuation loss. |
| Researcher Affiliation | Academia | Fan Wu, Junming Liu, Zhenzhe Zheng, and Guihai Chen Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University, China {wu-fan, liu-jm, zhengzhenzhe, chen-gh}@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Item allocation algorithm: Alloc(t, Nt) and Algorithm 2 Locating Candidate Winning Slots: Candi(i). |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for evaluation: 'uniformly distribute the agents intrinsic valuations over (0, 1]'. It does not refer to a publicly available or open dataset with concrete access information (link, DOI, repository, or formal citation with authors and year). |
| Dataset Splits | No | The paper describes a simulation-based evaluation with generated data and does not mention training, validation, or test dataset splits or cross-validation settings. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory details, or specific cloud instances) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies, libraries, or solvers with version numbers that were used for the implementation or experiments. |
| Experiment Setup | Yes | In the evaluation setup, we vary the number of agents from 50 to 1000 with a step of 50, uniformly distribute the agents intrinsic valuations over (0, 1], and set the two discounting factors of an agent i to be Fi(t) = 0.9t ai and Di(t) = 0.05 (t ai). We vary the number g of items for sale in each time slot from 1 to 5 with a step of 2, and set the number of time slots to 100. All the results are averaged over 200 runs. |