SSRNTrading, Microstructure & Execution
Algorithmic Collusion by Reinforcement-Learning Pricing Agents: Simulation Evidence and Implications for Financial Markets and Competition Law
Q-learning pricing agents in simulated duopolies reach supracompetitive outcomes with no communication, achieving collusion indices of 0.778 and 40% profit gains over competitive benchmarks.
Featured in No. 132 on 25 Sep 2026 · 1 day after release
- Released
- 24 Sep 2026
- First featured
- No. 132 · 25 Sep 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 4 of 5
- Identifier
- SSRN 7500483
- Authors
- Vladislav Dolgov
Citations and venue from Semantic Scholar (ODC-BY), refreshed weekly. Summary: Quant Letter (CC BY 4.0).