Abstract

There is growing evidence that tacit collusion can be autonomously achieved by machine learning technology, at least in some real-life examples identified in the literature and experimental settings. Although more work needs to be done to assess the competitive risks of widespread adoption of autonomous pricing agents, this is still an appropriate time to examine which possible remedies can be used in case competition law shifts towards the prohibition of tacit collusion. This is because outlawing such conduct is pointless unless there are suitable remedies that can be used to address the social harm. This article explores how fines and structural and behavioural remedies can serve to discourage collusive results while preserving the incentives to use efficiency-enhancing algorithms. We find that this could be achieved if fines and remedies can target structural conditions that facilitate collusion. In addition, the problem of unfeasibility of injunctions to remedy traditional price coordination changes with the use of pricing software, which in theory can be programmed to avoid collusive outcomes. Finally, machine-learning methods can be used by the authorities themselves as a tool to test the effects of any given combination of remedies and to estimate a more accurate competitive benchmark for the calculation of the appropriate fine.

中文翻译：

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算法隐性合谋的补救措施

摘要

越来越多的证据表明，至少在一些文献和实验设置中确定的现实生活中，可以通过机器学习技术自动实现默契合谋。尽管需要做更多的工作来评估广泛采用自动定价代理人的竞争风险，但是这仍然是一个适当的时机，以检查在竞争法转向禁止默契合谋的情况下可以使用哪些可能的补救措施。这是因为除非有可用于解决社会危害的适当补救措施，否则将这种行为定为非法是没有意义的。本文探讨了罚款，结构和行为补救措施如何阻止合谋结果，同时又保留了使用效率提高算法的诱因。我们发现，如果罚款和救济可以针对有利于串通的结构条件，就可以实现这一目标。此外，使用定价软件来解决传统的价格协调禁令不可行的问题，在理论上可以对定价软件进行编程以避免合谋结果。最后，当局自己可以使用机器学习方法作为工具来测试任何给定的补救措施组合的效果，并为计算适当的罚款估算更准确的竞争基准。