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By fair means or foul: Quantifying collusion in a market simulation with deep reinforcement learning
Schlechtinger, Michael
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Kosack, Damaris
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Krause, Franz
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Paulheim, Heiko
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DOI:
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https://doi.org/10.24963/ijcai.2024/54
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URL:
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https://www.ijcai.org/proceedings/2024/54
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Document Type:
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Conference or workshop publication
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Year of publication:
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2024
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Book title:
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Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 : main track
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Page range:
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485-493
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Conference title:
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IJCAI 2024, the 33rd International Joint Conferences on Artificial Intelligence
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Location of the conference venue:
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Jeju, South Korea
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Date of the conference:
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03.-09.08.2024
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Publisher:
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Larson, Kate
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Place of publication:
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Darmstadt ; Vienna
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Publishing house:
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International Joint Conferences on Artificial Intelligence Organization
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ISBN:
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978-1-956792-04-1
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
School of Law and Economics > Öffentliches Recht, Regulierungsrecht und Steuerrecht (Fetzer 2012-)
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Subject:
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004 Computer science, internet
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Abstract:
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In the rapidly evolving landscape of eCommerce, Artificial Intelligence (AI) based pricing algorithms, particularly those utilizing Reinforcement Learning (RL), are becoming increasingly prevalent. This rise has led to an inextricable pricing situation with the potential for market collusion. Our research employs an experimental oligopoly model of repeated price competition, systematically varying the environment to cover scenarios from basic economic theory to subjective consumer demand preferences. We also introduce a novel demand framework that enables the implementation of various demand models, allowing for a weighted blending of different models. In contrast to existing research in this domain, we aim to investigate the strategies and emerging pricing patterns developed by the agents, which may lead to a collusive outcome. Furthermore, we investigate a scenario where agents cannot observe their competitors’ prices. Finally, we provide a comprehensive legal analysis across all scenarios. Our findings indicate that RL-based AI agents converge to a collusive state characterized by the charging of supracompetitive prices, without necessarily requiring inter-agent communication. Implementing alternative RL algorithms, altering the number of agents or simulation settings, and restricting the scope of the agents’ observation space does not significantly impact the collusive market outcome behavior.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Schlechtinger, Michael
;
Kosack, Damaris
;
Krause, Franz
;
Paulheim, Heiko
Google Scholar:
Schlechtinger, Michael
;
Kosack, Damaris
;
Krause, Franz
;
Paulheim, Heiko
ORCID:
Schlechtinger, Michael  ORCID: 0000-0002-4181-3900 ; Kosack, Damaris ; Krause, Franz ; Paulheim, Heiko ORCID: 0000-0003-4386-8195
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