A Bayesian Clearing Mechanism for Combinatorial Auctions
Authors: Gianluca Brero, Sébastien Lahaie
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical evaluation over a range of valuation domains demonstrates that our Bayesian auction mechanism is highly competitive against the combinatorial clock auction in terms of rounds to convergence, even under the most favorable choices of price increment for this baseline.In this section we evaluate our Bayesian auction design with two different kinds of experiments: a small experiment to illustrate the behavior of our auction under biased and unbiased prior information, and a larger-scale experiment to compare our auction against a competitive baseline. Our simulations are conducted in Matlab. |
| Researcher Affiliation | Collaboration | Gianluca Brero University of Zurich brero@ifi.uzh.ch Sébastien Lahaie Google Research slahaie@google.com |
| Pseudocode | No | The paper includes a schematic representation of the auction process in Figure 1, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using 'publicly available GPML Matlab code' but does not state that the code for their described methodology is open-source or provide a link. |
| Open Datasets | Yes | For our second set of experiments, we generate instances using the Combinatorial Auction Test Suite (CATS), which offers four generator distributions: paths, regions, arbitrary, and scheduling (Leyton-Brown, Pearson, and Shoham 2000).Each input file is partitioned into a training set and test set , each with 500 bids. From the test set, 10 bids (representing 10 single-minded agents) are sampled uniformly at random. The training set is used to fit the prior knowledge of our Bayesian auction. |
| Dataset Splits | No | The paper explicitly mentions 'training set and test set' for the CATS experiments, but does not describe a separate validation set or a three-way split for the data. |
| Hardware Specification | No | The paper states 'Our simulations are conducted in Matlab' but does not provide any specific hardware details like GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Our simulations are conducted in Matlab' and 'GPML Matlab code', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In all our experiments, we assume that agents best respond to the proposed prices (i.e., they always bid on their most profitable bundle), and that the auctioneer considers their bids as if they were generated from the response model presented in (8) with β = 10.For the price update, the objective (10) is maximized using the active-set algorithm in Matlab. To avoid numerical singularities we place a lower bound of 0.01 on the variance of valuation estimates.The baseline clock auction is parametrized by a step size, or price increment.We therefore set a limit of 100 rounds for each auction run, and record reaching this limit as a failure to clear the market. |