Fast Iterative Combinatorial Auctions via Bayesian Learning
Authors: Gianluca Brero, Sébastien Lahaie, Sven Seuken1820-1828
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach via simulations on CATS instances. Our results show that our Bayesian CA outperforms even a highly optimized benchmark in terms of clearing percentage and convergence speed. |
| Researcher Affiliation | Collaboration | Gianluca Brero University of Zurich brero@ifi.uzh.ch S ebastien Lahaie Google Research slahaie@google.com Sven Seuken University of Zurich seuken@ifi.uzh.ch |
| Pseudocode | Yes | Algorithm 1: Bayesian Auction Framework Algorithm 2: Belief Update Rule Algorithm 3: Price Update Rule |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate our Bayesian auction on instances from the Combinatorial Auction Test Suite (CATS), a widely used instance generator for CAs (Leyton-Brown, Pearson, and Shoham, 2000). |
| Dataset Splits | No | The paper mentions splitting data into training and test sets but does not provide specific percentages, sample counts for each split, or explicit details about a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions “publicly available GPML Matlab code (Williams and Rasmussen, 2006)” but does not provide specific version numbers for the software components. |
| Experiment Setup | Yes | We evaluate our Bayesian auction on instances with 12 items and 10 bidders. These instances are sampled from four distributions provided by the Combinatorial Auction Test Suite (CATS)… We repeat this process 300 times for each distribution to create 300 auction instances… For the remainder of this paper, we will use ℓ= 27. |