Increasing VCG Revenue by Decreasing the Quality of Items
Authors: Mingyu Guo, Argyrios Deligkas, Rahul Savani
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also quantify how much better marking down is than item burning, and we compare the revenue of both approaches with computational experiments. The following numerical experiment suggests that on average, the revenue after marking down is only slightly higher than the revenue after burning. |
| Researcher Affiliation | Academia | Mingyu Guo mingyu.guo@adelaide.edu.au School of Computer Science University of Adelaide, Australia Argyrios Deligkas and Rahul Savani {A.Deligkas, rahul.savani}@liverpool.ac.uk Department of Computer Science University of Liverpool, UK |
| Pseudocode | Yes | Algorithm 1: Winner Determination Algorithm 2: Marking Down to Maximize d |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code or provide a direct link to a code repository for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('The items qualities and the agents quality requirements are drawn i.i.d. from {1, 2, . . . , H}. The agents valuations are drawn i.i.d. from U(0, 1)'), but does not mention using or providing access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes data generation and averaging over cases ('We average over 100 cases.'), but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper describes computational experiments but does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running them. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The experimental setup is as follows: m = H = 10. The items qualities and the agents quality requirements are drawn i.i.d. from {1, 2, . . . , H}. The agents valuations are drawn i.i.d. from U(0, 1). We average over 100 cases. |