Non-decreasing Payment Rules for Combinatorial Auctions
Authors: Vitor Bosshard, Ye Wang, Sven Seuken
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we evaluate the runtime of our algorithm in two different CA domains. We show that it outperforms the state-of-the-art algorithm by multiple orders of magnitude. |
| Researcher Affiliation | Academia | Vitor Bosshard1 , Ye Wang2 and Sven Seuken1 1Department of Informatics, University of Zurich 2School of Computer and Communication Sciences, EPFL |
| Pseudocode | Yes | Algorithm 1: Utility Planes BNE Algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement of releasing the code for the described methodology, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper describes experimental domains (LLG, LLLLGG) and refers to prior work for their definitions, but it does not provide concrete access information (e.g., specific links, DOIs, repositories, or explicit dataset citations) for a publicly available or open dataset used in the experiments. |
| Dataset Splits | No | The paper mentions parameters for simulation (e.g., grid resolution, number of samples) but does not provide specific training/validation/test dataset splits (percentages or sample counts) as would be typical for empirical evaluations on pre-existing datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states 'Both algorithms are written in Java 8' but does not list any other specific software components or libraries with version numbers. |
| Experiment Setup | Yes | For each domain, we set a target ε. This target is 0.001 for LLG, and 0.02 for LLLLGG. For each algorithm, we measure the runtime required to reach a strategy profile that is proven to be an ε-BNE. The starting strategy profile is one where all bidders bid truthfully. Both algorithms are written in Java 8 and share as much of their implementation as possible. There is often a need to integrate over the bids b-i, e.g. when computing expected utilities or utility planes. This integration is approximated using Monte Carlo sampling with common random numbers, as described in [Bosshard et al., 2017]. The number of samples used is 20,000 for LLG and 200,000 for LLLLGG. |