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We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs).
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Language models are few-shot learners
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Sustainable and unchallenged algorithmic tacit collusion
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The risks of using algorithms in business: Artificial price collusion, 2020
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Quantifying the high-frequency trading “arms race”
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Competition in pricing algorithms
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Algorithmic collusion with imperfect monitoring
E. Calvano, G. Calzolari, V. Denicoló, and S. Pastorello · 2021
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Autonomous algorithmic collusion: Q-learning under sequential pricing
T. Klein · 2021
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Constitutional AI: Harmlessness from AI feedback
Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li, E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite, L. Lovitt, M. Sellitto, N. Elhage, N. Schiefer, N. Mercado, N. DasSarma, R. Lasenby, R. Larson, S. Ringer, S. Johnston, S. Kravec, S. E. Showk, S. Fort, T. Lanham, T. Telleen-Lawton, T. Conerly, T. Henighan, T. Hume, S. R. Bowman, Z. Hatfield-Dodds, B. Mann, D. Amodei, N. Joseph, S. McCandlish, T. Brown, and J. Kaplan · 2022
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M. Banchio and G. Mantegazza · 2022
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Artificial intelligence and auction design
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Artificial collusion: Examining supracompetitive pricing by Q-learning algorithms
A. V. den Boer, J. M. Meylahn, and M. P. Schinkel · 2022
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Algorithmic pricing: A recipe for antitrust disaster?
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I. Abada, J. E. Harrington, Jr, X. Lambin, and J. M. Meylahn · 2024
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