Over the past several years, there has been significant interest in the question of how advertising-supported, “free” online services such as search and social networking should be viewed through an antitrust lens. But somewhat less attention has been paid to the rise (and some of the consequences) of another feature of the digital economy: the growing use of algorithms to set and adjust prices.
Two recent articles look at pricing algorithms and consumer harm. The first article, “Personalized Pricing as Monopolization” by Ramsi Woodcock focuses on the ability of firms to use data, analytics, and artificial intelligence to show each consumers his or her own individually-tailored price. In this scenario, which Woodcock predicts will grow increasingly prevalent in the economy, a firm is able to collect and analyze enough information about an individual’s willingness to pay to set a price at the individual’s “reservation price,” that is, the highest price an individual is willing to pay and not walk away. In effect, companies will use our own data against us. The goal of personalized pricing, as Woodcock sees it, is for the seller to capture all the gains of trade (“consumer surplus”), leaving the buyer effectively no better off than if he or she had not transacted at all. Personalized pricing approximates what economists call “perfect price discrimination.” In the past, perfect price discrimination was largely confined to theory, but no longer.
The second article, “Limiting Algorithmic Cartels” by Michal Gal, builds on experimental evidence suggesting that certain pricing algorithms are able to learn on their own to coordinate and to push prices above a competitive level. Like effective cartels, these algorithms are able to return to high prices rapidly by “punishing” a “cheater” that cuts its price. Significantly, there does not have to be an agreement or “meeting of the minds,” a required element for an antitrust violation involving collusion. Companies do not need to use identical pricing algorithms. Nor does anyone need to intentionally develop a pricing algorithm with the object of price coordination in mind. Rather, having been programmed to maximize profits, an algorithm may figure out this pricing strategy on its own through trial and error. In other words, computerized price setting may give rise to new and perfectly legal form of price-fixing, even though price-fixing is the “supreme evil” of antitrust. To date, these “algorithmic cartels” are largely confined to the laboratory setting, but there is some evidence that this behavior is beginning to show up in the real world.
From a consumer’s standpoint, both of these scenarios are troubling. In both, automated price setting via algorithms and artificial intelligence can make us pay significantly higher prices. Both result in what the authors term “negative welfare effects.” As Gal puts it, “the threat to consumers is no longer science fiction.”
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