算法在不对称市场中仍能达成高利润共谋，打破对称性更易合谋的固有认知。
The Spoils of Algorithmic Collusion: Profit Allocation Among Asymmetric Firms
- 用重复古诺模型研究算法间合谋行为，分析企业不对称性影响。
- 无论对称与否，算法均趋向于帕累托前沿利润分配，总福利预测准确。
- 揭示算法合谋可能比传统认知更普遍，尤其在非对称市场中。
我们研究独立算法在重复古诺双头垄断博弈中合谋的倾向。重点考察不同寡头与谈判解对企业不对称性的预测能力。发现消费者和企业均可从不对称中获益：对称时算法产生更具竞争性的结果，极不对称时则相反。尽管静态纳什均衡低估总产量影响、高估利润影响，但在总福利预测上出人意料地准确。最佳解释是等相对收益解，算法在所有不对称程度下均达成或接近帕累托前沿的利润分配。结果表明，当算法日益主导管理决策时，对称行业更易合谋的普遍信念可能不再成立。
原文摘要 · Abstract (English)
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.
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