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Algorithms Are Better Than We Thought – They Can Collaborate Into Cartels | The Continental Telegraph

Antitrust agencies are concerned that the autonomous pricing algorithms increasingly used by online vendors may learn to collude. This column uses experiments with pricing algorithms powered by AI in a controlled environment to demonstrate that even relatively simple algorithms systematically learn to play sophisticated collusive strategies. Most worrying is that they learn to collude by trial and error, with no prior knowledge of the environment in which they operate, without communicating with one another, and without being specifically designed or instructed to collude.

Remember your last online purchase? Chances are, the price you paid was not set by humans but rather by a software algorithm. Already in 2015, more than a third of the vendors on Amazon.com had automated pricing (Chen et al. 2016), and the share has certainly risen since then – with the growth of a repricing software industry that supplies turnkey pricing systems, even the smallest vendors can now afford algorithmic pricing.

Unlike the traditional revenue management systems long in use by such businesses as airlines and hotels, in which the programmer remains effectively in charge of the strategic choices, the pricing programs that are now emerging are much more ‘autonomous’. These new algorithms adopt the same logic as the artificial intelligence (AI) programs that have recently attained superhuman performances in complex strategic environments such as the game of Go or chess. That is, the algorithm is instructed by the programmer only about the aim of the exercise – winning the game, say, or generating the highest possible profit. It is not told specifically how to play the game but instead learns from experience. In a training phase, the algorithm actively experiments with the alternative strategies by playing against clones in simulated environments, more frequently adopting the strategies that perform best. In this learning process, the algorithm requires little or no external guidance. Once the learning is completed, the algorithm is put to work.

From the antitrust standpoint, the concern is that these autonomous pricing algorithms may independently discover that if they are to make the highest possible profit, they should avoid price wars. That is, they may learn to collude even if they have not been specifically instructed to do so, and even if they do not communicate with one another. This is a problem. First, ‘good performance’ from the sellers’ standpoint, i.e. high prices, is bad for consumers and for economic efficiency. Second, in most countries (including Europe and the US) such ‘tacit’ collusion, not relying on explicit intent and communication, is not currently treated as illegal, on the grounds that it is unlikely to occur among human agents and that, even if it did occur, it would be next to impossible to detect. The conventional wisdom, then, is that aggressive antitrust enforcement would be likely to produce many false positives (i.e. condemning innocent conduct), while tolerant policy would result in relatively few false negatives (i.e. excusing anticompetitive conduct). With the advent of AI pricing, however, the concern is that the balance between the two types of error might be altered. Though no real-world evidence of autonomous algorithmic collusion has been produced so far,1 antitrust agencies are actively debating the problem.2

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Algorithms Are Better Than We Thought – They Can Collaborate Into Cartels | The Continental Telegraph.

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