Machine Learning-Powered Combinatorial Clock Auction
Authors: Ermis Nikiforos Soumalias, Jakob Weissteiner, Jakob Heiss, Sven Seuken
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our ML-based demand query mechanism in several spectrum auction domains and compare it against the most established real-world ICA: the combinatorial clock auction (CCA). Our mechanism significantly outperforms the CCA in terms of efficiency in all domains, it achieves higher efficiency in a significantly reduced number of rounds, and, using linear prices, it exhibits vastly higher clearing potential. |
| Researcher Affiliation | Academia | 1University of Zurich 2ETH Zurich 3ETH AI Center |
| Pseudocode | Yes | Algorithm 1: TRAINONDQS; Algorithm 2: ML-CCA |
| Open Source Code | Yes | Our source code is publicly available on Git Hub at https://github.com/marketdesignresearch/ML-CCA. |
| Open Datasets | Yes | To generate synthetic CA instances, we use the GSVM, LSVM, SRVM, and MRVM domains from the spectrum auction test suite (SATS) (Weiss, Lubin, and Seuken 2017) (see Appendix D.1 for details). |
| Dataset Splits | Yes | Additionally, we mark the bundle x CCA X from this last CCA iteration (i.e., the one resulting from p50) with a black star. Moreover, we present two different validation sets on which we evaluate m MVNN configurations in our hyperparameter optimization (HPO): Validation set 1 (red circles), which are 50, 000 uniformly at random sampled bundles x X, and validation set 2 (green circles), where we first sample 500 price vectors {pr}500 r=1 where the price of each item is drawn uniformly at random from the range of 0 to 3 times the average maximum value of an agent of that type for a single item, and then determine utility-maximizing bundles x i (pr) (w.r.t. vi) at those prices (cp. Equation (1)). |
| Hardware Specification | Yes | All experiments were performed on a cluster equipped with AMD EPYC 7742 (2.25 GHz) CPUs (8 cores, 16 threads, 64 MB L3 cache), NVIDIA A100 GPUs (40 GB RAM) with CUDA 11.2, and 256 GB RAM. |
| Software Dependencies | Yes | Our implementation does not use GPUs for training or inference, however, since Algorithm 1 requires solving a mixed-integer program (MIP) in each iteration, which we do via Gurobi 9.5.2. |
| Experiment Setup | Yes | For both mechanisms, we allow a maximum of 100 clock rounds per instance, i.e., we set Qmax = 100. For CCA, we set the price increment to 5%... In GSVM, LSVM and SRVM we set Qinit = 20 for ML-CCA, while in MRVM we set Qinit = 50. |