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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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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2024
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Buchtitel:
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Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 : main track
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Seitenbereich:
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485-493
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Veranstaltungstitel:
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IJCAI 2024, the 33rd International Joint Conferences on Artificial Intelligence
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Veranstaltungsort:
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Jeju, South Korea
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Veranstaltungsdatum:
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03.-09.08.2024
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Herausgeber:
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Larson, Kate
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Ort der Veröffentlichung:
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Darmstadt ; Vienna
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Verlag:
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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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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Fakultät für Rechtswissenschaft und Volkswirtschaftslehre > Öffentliches Recht, Regulierungsrecht und Steuerrecht (Fetzer 2012-)
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Fachgebiet:
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004 Informatik
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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. |
 Suche Autoren 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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