ML Anthology

Equilibrium Learning in Auction Markets

Stefan Heidekrüger
AAAI 2022 pp. 12882-12883
doi:10.1609/AAAI.V36I11.21578 /aaai/2022/heidekruger2022aaai-equilibrium/

Abstract

My dissertation investigates the computation of Bayes-Nash equilibria in auctions via multiagent learning. A particular focus lies on the game-theoretic analysis of learned gradient dynamics in such markets. This requires overcoming several technical challenges like non-differentiable utility functions and infinite-dimensional strategy spaces. Positive results may open the door for wide-ranging applications in Market Design and the economic sciences.

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Cite

Text

Heidekrüger. "Equilibrium Learning in Auction Markets." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21578

Markdown

[Heidekrüger. "Equilibrium Learning in Auction Markets." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/heidekruger2022aaai-equilibrium/) doi:10.1609/AAAI.V36I11.21578

BibTeX

@inproceedings{heidekruger2022aaai-equilibrium,
  title     = {{Equilibrium Learning in Auction Markets}},
  author    = {Heidekrüger, Stefan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12882-12883},
  doi       = {10.1609/AAAI.V36I11.21578},
  url       = {https://mlanthology.org/aaai/2022/heidekruger2022aaai-equilibrium/}
}