Adversarial competition and collusion in algorithmic markets
Adversarial competition and collusion in algorithmic markets
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DOI：
10.1038/s42256-023-00646-0
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发表时间：
2023-05
影响因子：
23.8
通讯作者：
Luc Rocher;Arnaud J Tournier;Y. de Montjoye
中科院分区：
文献类型：
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作者：
Luc Rocher;Arnaud J Tournier;Y. de Montjoye
算法现在在数字市场中发挥着核心作用，可以设定价格，并对竞争对手的行为做出真实的自动响应。自动定价算法的部署受到经济学家和监管机构的审查，担心其对价格和竞争的影响。迄今为止，现有的研究仅限于所有公司使用相同算法的情况，这表明在这种情况下可能会自发出现反竞争行为。在这里，我们介绍和研究一般的反竞争机制，对抗性共谋，其中一个公司操纵其他卖家使用自己的定价算法。我们提出了一个基于网络的框架来模拟迭代两公司和三公司市场的定价算法的策略。在这个框架中，攻击者学会内化竞争对手的算法，然后推导出一种以牺牲竞争对手为代价人为增加利润的策略。面对利润的大幅损失，竞争对手最终会介入，修改或关闭他们的定价算法。为了抑制这种干预，我们表明，攻击者可以单方面增加其利润和竞争对手的利润。这导致了一个具有对称和超竞争利润的合谋结果，从长远来看是可持续的。总之，我们的研究结果强调了政策制定者和监管机构需要考虑对算法定价的对抗性操纵，这可能目前不属于现行竞争法的范围。
Algorithms are now playing a central role in digital marketplaces, setting prices and automatically responding in real time to competitors’ behaviour. The deployment of automated pricing algorithms is scrutinized by economists and regulatory agencies, concerned about its impact on prices and competition. Existing research has so far been limited to cases where all firms use the same algorithm, suggesting that anti-competitive behaviour might spontaneously arise in that setting. Here we introduce and study a general anti-competitive mechanism, adversarial collusion, where one firm manipulates other sellers that use their own pricing algorithm. We propose a network-based framework to model the strategies of pricing algorithms on iterated two-firm and three-firm markets. In this framework, an attacker learns to endogenize competitors’ algorithms and then derive a strategy to artificially increase its profit at the expense of competitors. Facing a drastic loss of profits, competitors will eventually intervene and revise or turn off their pricing algorithm. To disincentivize this intervention, we show that the attacker can instead unilaterally increase both its profits and the profits of competitors. This leads to a collusive outcome with symmetric and supra-competitive profits, sustainable in the long run. Together, our findings highlight the need for policymakers and regulatory agencies to consider adversarial manipulations of algorithmic pricing, which might currently fall outside of the scope of current competition laws.