The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910782082514944 |
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| author | Martin, Simon Normann, Hans-Theo Püplichhuisen, Paul Werner, Tobias |
| author_facet | Martin, Simon Normann, Hans-Theo Püplichhuisen, Paul Werner, Tobias |
| contents | We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of asymmetry between firms. We find that both consumers and firms can benefit from asymmetry. Algorithms produce more competitive outcomes when firms are symmetric, but less when they are very asymmetric. Although the static Nash equilibrium underestimates the effect on total quantity and overestimates the effect on profits, it delivers surprisingly accurate predictions in terms of total welfare. The best description of our results is provided by the equal relative gains solution. In particular, we find algorithms to agree on profits that are on or close to the Pareto frontier for all degrees of asymmetry. Our results suggest that the common belief that symmetric industries are more prone to collusion may no longer hold when algorithms increasingly drive managerial decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms Martin, Simon Normann, Hans-Theo Püplichhuisen, Paul Werner, Tobias General Economics Economics Artificial Intelligence We study the propensity of independent algorithms to collude in repeated Cournot duopoly games. Specifically, we investigate the predictive power of different oligopoly and bargaining solutions regarding the effect of asymmetry between firms. We find that both consumers and firms can benefit from asymmetry. Algorithms produce more competitive outcomes when firms are symmetric, but less when they are very asymmetric. Although the static Nash equilibrium underestimates the effect on total quantity and overestimates the effect on profits, it delivers surprisingly accurate predictions in terms of total welfare. The best description of our results is provided by the equal relative gains solution. In particular, we find algorithms to agree on profits that are on or close to the Pareto frontier for all degrees of asymmetry. Our results suggest that the common belief that symmetric industries are more prone to collusion may no longer hold when algorithms increasingly drive managerial decisions. |
| title | The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms |
| topic | General Economics Economics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.07178 |