Algorithmic collusion under competitive design

Ivan Conjeaud

Papers from arXiv.org

Abstract: We study a simple model of algorithmic collusion in which Q-learning algorithms are designed in a strategic fashion. We let players (\textit{designers}) choose their exploration policy simultaneously prior to letting their algorithms repeatedly play a prisoner's dilemma. We prove that, in equilibrium, collusive behavior is reached with positive probability. Our numerical simulations indicate symmetry of the equilibria and give insight for how they are affected by a parameter of interest. We also investigate general profiles of exploration policies. We characterize the behavior of the system for extreme profiles (fully greedy and fully explorative) and use numerical simulations and clustering methods to measure the likelihood of collusive behavior in general cases.

Date: 2023-12, Revised 2024-09
New Economics Papers: this item is included in nep-cmp, nep-gth and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2312.02644 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2312.02644

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().