Left unchecked, pricing algorithms might unintentionally discriminate and collude to fix prices
Algorithms currently set prices for entire product lines at tech-heavy corporations such as Amazon and compute fares around the clock for ride-sharing services, including Uber and Lyft.
In the past few years, a number of studies have suggested that pricing algorithms can learn to offer different prices to different consumers based on their unique purchasing history or preferences.
A new preprint study, released online in February by researchers at Beijing's Tsinghua University, may provide a surprisingly simple solution: it suggests that price controls-which are among the oldest and most elementary tools in regulating commerce-could be readily used to prevent the economic discrimination that may potentially result from greedy pricing algorithms while still maintaining reasonable profits for the companies using them.
Over the past few years, this once common regulatory tool has attracted fresh attention, in part because of ride-sharing companies' use of "Surge" pricing strategies.
If the true price of a good is $5, but a consumer is somehow able to purchase it for $3, the consumer's surplus would be $2. "Personalized pricing has become common practice in many industries nowadays due to the availability of a growing amount of consumer data," says study co-author Renzhe Xu, a graduate student at Tsinghua University.
"As a result, it is of paramount importance to design effective regulatory policies to balance the surplus between consumers and producers." Xu and his colleagues provided formal mathematical proofs to show how price controls could theoretically balance the surplus between consumers and sellers who use artificial intelligence algorithms.
Pricing algorithms achieve a similar advantage when they estimate an individual's or group's WTP by harvesting data about them from big tech companies, such as search engine operators or social media platforms.
"The purpose of algorithmic pricing is to precisely assess consumers' willingness to pay from the highly granular data of consumers' characteristics," Xu says.
For many of today's algorithmic pricing agents such price-fixing concerns carry less weight.
With slight changes in design, algorithms might learn to collude and fix prices-which is why it is important to study restraints such as price controls.