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Are online prices higher because of pricing algorithms? | #itsecurity | #infosec | #hacking | #aihp


When online markets first emerged, there was widespread optimism that they would be fair and competitive. Unlike physical stores, consumers would be able to choose among dozens, perhaps hundreds, of websites with simply a click. This would drive intense competition to offer the lowest price, making markets more efficient and benefiting consumers.

It is now clear that the promises of online markets have only been partially realized. A few retailers dominate online markets. In some cases, online prices are slightly lower than in physical stores, but not always.1 Moreover, the price of a product can vary widely across online retailers. Prices on websites such as Amazon can fluctuate over the course of a day and are sometimes significantly higher than the suggested retail price. Clearly, online markets are not as competitive as some initially thought.

Retailers in online markets increasingly use pricing algorithms. Rather than a human setting prices, a computer program can quickly monitor market conditions, including behavior of rival retailers, and autonomously adjust prices in near real-time. The fact that these developments have happened in tandem—more responsive pricing behavior, along with large price differences that consumers might pay for identical products—runs counter to the initial expectations about competition in online markets. Shouldn’t more responsive pricing lead to greater competition?

This article reviews recent work examining pricing strategies of major online retailers and the potential effects of pricing algorithms. We describe how pricing algorithms can lead to higher prices in a number of ways, even if some characteristics of these algorithms may appear, at first glance, to increase competition. A key feature of many pricing algorithms is that they automatically react to rivals’ prices. Some have argued that this could facilitate collusion. However, this feature can also soften price competition when rivals do not collude. Even very simple pricing algorithms can raise prices. Finally, we discuss potential policy responses to encourage competition in online retail and other markets with pricing algorithms.

The competitive effects of algorithms are increasingly important given that online sales have surpassed $4 trillion worldwide and continue to grow rapidly. In addition to online markets, pricing algorithms are now being used to set prices for gas stations, airline tickets, hotels, entertainment, and ride sharing. The competitive effects of pricing algorithms will become only more important as they are increasingly adopted in a wide array of markets.

What is a pricing algorithm?

At a high level, a pricing algorithm is a computer program that autonomously adjusts prices based on current and past data related to demand, cost, or rivals’ prices. Initially, algorithms were used in only a few industries, such as airline ticket pricing. With the rise of online markets, there has been a sea change in terms of the number of markets affected by the adoption of algorithms, and there have been major investments to improve pricing algorithms along several dimensions.

There are a variety of hypotheses about the effects of pricing algorithms. Economists largely agree that algorithms that adjust prices based on demand conditions and/or costs have the potential to increase efficiency. However, there is growing concern that other aspects of pricing algorithms could reduce competition and increase prices.

Because pricing algorithms act without human intervention, algorithmic pricing has two key features that distinguish it from traditional methods where prices are set manually by a pricing manager or analyst. First, algorithms can allow retailers to set prices based on rules that are encoded by software. The rules can be fixed, or they can adjust over time based on, for example, machine learning protocols or human intervention. The fact that firms can commit to these rules in the short run can have important implications for competition and prices, as we discuss below.

Second, because computers can perform intensive calculations quickly, algorithms can incorporate more information and make changes faster than traditional pricing methods. Often, retailers set up their algorithms to run at regular intervals—once per day or once per hour—to update prices based on information that arrives at a high frequency. Information that may lead to a price change includes recent sales, inventories, or external features such as weather forecasts. The ability to adapt to rapidly changing conditions is what gives algorithms the potential to provide goods more efficiently than traditional pricing.

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