Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914859455610880 |
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| author | Chica, Cristian Guo, Yinglong Lerman, Gilad |
| author_facet | Chica, Cristian Guo, Yinglong Lerman, Gilad |
| contents | Algorithmic price collusion facilitated by artificial intelligence (AI) algorithms raises significant concerns. We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits. Higher user heterogeneity or greater utility from outside options generally reduce collusion, while higher discount rates increase it. Tacit collusion remains feasible even at low discount rates. To mitigate collusive behavior and inform potential regulatory measures, we propose incorporating a penalty term in the Q-learning algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets Chica, Cristian Guo, Yinglong Lerman, Gilad General Economics Economics Artificial Intelligence Computer Science and Game Theory Algorithmic price collusion facilitated by artificial intelligence (AI) algorithms raises significant concerns. We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits. Higher user heterogeneity or greater utility from outside options generally reduce collusion, while higher discount rates increase it. Tacit collusion remains feasible even at low discount rates. To mitigate collusive behavior and inform potential regulatory measures, we propose incorporating a penalty term in the Q-learning algorithm. |
| title | Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets |
| topic | General Economics Economics Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2407.04088 |