Michael Schlechtinger

B6, 26, Room B 0.02

Phone:+49 (0) 621 / 181-2651

schlechtinger (at) uni-mannheim.de

Consultation hours by appointment

Research Interests

Kartellrechtskonforme KI (KarekoKI) – Detection and prevention of price agreements within AI-based price regulation

Schlechtinger, M. (2024). Investigating, predicting, and mitigating collusive behavior in deep reinforcement learning-based pricing AIs. Dissertation. Mannheim.
Schlechtinger, M., Kosack, D., Krause, F. and Paulheim, H. (2024). By fair means or foul: Quantifying collusion in a market simulation with deep reinforcement learning. In , Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 : main track (S. 485–493). , International Joint Conferences on Artificial Intelligence Organization: Darmstadt ; Vienna.

Schlechtinger, M., Kosack, D., Paulheim, H., Fetzer, T. and Krause, F. (2023). The price of algorithmic pricing: Investigating collusion in a market simulation with AI agents. In , AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (S. 2748-2750). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.

Schlechtinger, M., Kosack, D., Paulheim, H. and Fetzer, T. (2021). How algorithms work and play together. Concurrences : Revue des Droits de la Concurrence, 2021, 19–23.
Schlechtinger, M., Kosack, D., Paulheim, H. and Fetzer, T. (2021). Winning at any cost – infringing the cartel prohibition with reinforcement learning. In , Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good : The PAAMS collection, 19th international conference, PAAMS 2021, Salamanca, Spain, October 6–8, 2021, proceedings (S. 255–266). Lecture Notes in Computer Science, Springer: Berlin [u.a.].