Artificial Intelligence and Pricing: The Impact of Algorithm Design
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- Fershtman, Chaim & Asker, John & Pakes, Ariel, 2021. "Artificial intelligence and Pricing: The Impact of Algorithm Design," CEPR Discussion Papers 15880, C.E.P.R. Discussion Papers.
References listed on IDEAS
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Cited by:
- Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
- Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
- Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
- Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
- Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
- Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
- Andreas Haupt & Aroon Narayanan, 2022. "Risk Preferences of Learning Algorithms," Papers 2205.04619, arXiv.org, revised Dec 2023.
- Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
- Andreas A. Haupt & Phillip J. K. Christoffersen & Mehul Damani & Dylan Hadfield-Menell, 2022. "Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL," Papers 2208.10469, arXiv.org, revised Jan 2024.
- Aniko …ry & Ali Horta su & Kevin Williams, 2022. "Dynamic Price Competition: Theory and Evidence from Airline Markets," Cowles Foundation Discussion Papers 2341R1, Cowles Foundation for Research in Economics, Yale University, revised Apr 2023.
- Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021. "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers 2104.12895, arXiv.org.
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More about this item
JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
- D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
- K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
- L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
- L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
- L4 - Industrial Organization - - Antitrust Issues and Policies
- L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
- O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-COM-2021-03-15 (Industrial Competition)
- NEP-CWA-2021-03-15 (Central and Western Asia)
- NEP-GTH-2021-03-15 (Game Theory)
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