This archive is distributed in association with the INFORMS Journal on Computing under the General Public License v3.0.
The software in this repository is a snapshot of the software that was used in the research reported on in the paper Learning Equilibria in Asymmetric Auction Games by Martin Bichler, Stefan Heidekrüger (@heidekrueger), Nils Kohring (@kohring). The snapshot is based on the bnelearn software library, and corresponds to this release in the development repository.
Important: The underlying bnelearn software is being developed on an on-going basis at
https://github.com/heidekrueger/bnelearn/. Please go there if you would like to get a more recent version or would like support. The documentation of the bnelearn software can be found at https://bnelearn.readthedocs.io/.
To cite this software, please cite the paper using its DOI and the software itself, using the suggested citation in the bnelearn software repository linked above.
If you need to cite this specific version of the code, you may use the citation below:
@article{bnelearn-asymmetric,
author = {Bichler, Martin and Heidekr\"uger, Stefan and Kohring, Nils},
publisher = {INFORMS Journal on Computing},
title = {{bnelearn-asymmetric} {V}ersion v2021.0151},
year = {2023},
doi = {https://zenodo.org/badge/latestdoi/571825331},
note = {available for download at https://github.com/INFORMSJoC/2021.0151}
}
The goal of this software is to demonstrate the demonstration of the empirical convergence behavior of the NPGA algorithm in asymmetric auction games. Please see the paper for details.
To install the software and it's requirements, please follow the instructions at https://bnelearn.readthedocs.io/en/latest/usage/installation.html. The specific requirements for this snapshot of the code can also be found here.
The following table presents all asymmetric sealed-bid auctions were we successfully deployed the equilibrium learning algorithm with references to the corresponding sections in the IJOC article and in the code.
| Auction Setting | Reference in Paper | Reference in Code |
|---|---|---|
| single-item uniform overlapping FPSB | Subsection 6.1.1 | Script L31 |
| single-item uniform non-overlapping FPSB | Subsection 6.1.1 | Script L32 |
| single-item beta asymmetric FPSB | Subsection 6.1.2 | Script L33 |
| multi-unit with 4 units | Section 6.2 | Script L139 |
| multi-unit with 8 units | Section 6.2 | Script L139 |
| multi-unit with 12 units | Section 6.2 | Script L139 |
| LLG, adapted VCG | Section 6.3 | Script L69 |
| split-award FPSB | Section 6.4 | Script L106 |
| LLLLGG FPSB | Section 6.5 | Script L190 |
| LLLLRRG FPSB | Section 6.5 | Script L278 |
To replicate the experiments in the paper and the figures above, please follow the installation instructions above, then run this script.
This code is being developed on an on-going basis at the author's GitHub site.
For support in using this software, submit an issue.