An economist will tell you that the fairest price for any product is the “equilibrium price” at which consumer demand matches the product’s available supply1. If supply increases, the price should go down; but if buyer demand increases, the price should rise. So it is no surprise that people often pay thousands of dollars for a single Super Bowl ticket. Or that these prices fluctuate wildly the week before the big game depending on which teams are playing and how deep-pocketed their fan bases are2.
The controversial practice of surge pricing3 used by high-profile rideshare company Uber is based on this logic of fairness. The idea behind surge pricing is to adjust prices of rides to match driver supply to rider demand at any given time. During periods of excessive demand when there are many more riders than drivers, or when there aren’t enough drivers on the road and customer wait times are long, Uber increases its normal fares. They do this with a “multiplier” whose value depends on scarcity of available drivers. On a Friday night in midtown Houston for example, the surge fare may be twice the normal fare (in which case the multiplier is two). When surge pricing is in effect, Uber’s riders are informed that their fares will be higher, and they have to agree to pay the amount. Only then is a driver dispatched to pick them up.
Surge pricing achieves two important goals for Uber and its customers. One is that it increases supply of drivers. Lured by the ability to earn more, new drivers may clock in, or drivers from other areas may home in on the neighborhood with the surge price4. In one study conducted in collaboration with Uber, researchers found that surge pricing doubled the number of drivers during a busy period after a sold-out concert in New York City5. Second, surge pricing is an effective way to control customer demand and allocate available rides to those people who value them more. Some Uber customers may simply find the high surge price to be unacceptable and find other means of transportation. Others, who value the ride more, will be willing to pay the surge price.
Despite these obvious benefits and the ringing endorsements of surge pricing by economists6, it is quite clear that by and large, consumers loathe Uber’s surge pricing.
Surge prices kick in at the worst possible time for riders. By its very design, Uber’s surge pricing kicks in when demand is high, that is under circumstances when many of its customers really want to use the service. And perversely, the more intense their desire for an Uber ride, the higher the surge price is likely to be. The desire for Uber rides (and corresponding high demand) is fueled by different reasons. Large numbers of people want to go out on Friday and Saturday nights, want rides to and from big concerts at around the same time, and want to be shuttled to restaurants on special days like New Year’s Eve and Valentine’s Day. Many also need rides in the middle of a blizzard or a rainstorm10 just when few Uber drivers want to be out. So it is natural for consumers to feel that Uber is taking advantage of them by ramping up prices when they are in direst need. Explaining the rationale for surge pricing does not weaken the inherent sense of injustice. It is not surprising that a Google search with the joint phrases “Uber surge pricing” and “price gouging” generates more than 2,000 hits.
Drastic changes in surge prices creates doubt and uncertainty. Compounding the perception problem is the fact that Uber’s surge prices fluctuate drastically and all too often. One study found that in Washington DC, surge prices were 2.3 times normal price at 1:54 pm on a Tuesday in March 2015, but had returned to normal levels just six minutes later. The study also reported that Uber surge prices changed so rapidly, that many changes occurred every 3 to 5 minutes11. Furthermore, surge prices are also location-specific and may be several times higher in one neighborhood than an adjoining one. When prices are so volatile, many consumers simply stop trusting the company, because they don’t know when to pull the trigger or whether they are getting fleeced12.
It is not clear how the surge price multiplier is calculated. Uber uses a rather sophisticated computational algorithm to figure out how high to raise its surge prices. For riders, the bad news is that the algorithm is a closely guarded secret and much too complicated13 to explain to users anyway. This creates the sense that the surge multiplier calculation is a “black box”. Just as bad, in keeping with economic theory on which surge pricing is based, there is no upper limit on how high prices can go. Figuring out the surge multiplier is like bidding in an auction: as long as there is an imbalance between supply and demand, the multiplier will keep rising, resulting in the possibility of some Uber riders paying obscene prices. Because of the lack of information or what journalism scholar Nick Diakopoulos has called “algorithmic accountability”14, consumers feel powerless and disgruntled when confronted with surge pricing15.
Although its rationale is sound and it is meant to help customers, surge pricing is hated by Uber riders for fundamentally psychological reasons.
Yes, it certainly can. Just like the problems with surge pricing I have discussed in this post, many of the fixes would rely on applying principles of consumer psychology.
My follow-up piece providing the solutions appears on the Harvard Business Review site:
 Economists call this point as the “equilibrium of demand and supply” and the price at this level as the equilibrium price. This Khan Academy video provides an easy-to-understand explanation of these concepts:
 On its own website, Uber has a succinct and clear explanation of surge pricing. It is available here.
 Journalism professor Nick Diakopoulos has a nice discussion of potential effects of surge pricing in this Washington Post article.
 The study was done by two Uber employees and a University of Chicago economics professor. The report is available here.
 See this review article by economist Mark Armstrong, available here. As another example, see the discussion in https://hbr.org/2014/12/what-economists-dont-get-about-ubers-surge-pricing.
 The test conducted in October 2014 compared prices for Uber and a taxicab for the same trip in 21 US cities and found that after factoring in tips for the cab driver, Uber was cheaper than a taxi in every single city. Even without the tips, Uber’s price beat taxi fare everywhere except in New York city. The article is available here.
 See this article detailing the experiences of a New York Times economics reporter with Uber surge pricing during New Year’s festivities: http://www.nytimes.com/2014/01/12/magazine/is-ubers-surge-pricing-an-exa....
 See http://www.click2houston.com/consumer/uber-user-charged-500-for-30-mile-.... Celebrities like Salman Rushdie, http://news.travel.aol.com/2013/11/13/salman-rushdie-complaints-uber/, and Jessica Seinfeld, http://www.businessinsider.com/jerry-seinfelds-wife-spent-415-during-ube... have also famously complained on Twitter and other social media about their experiences.
 The study was conducted by journalism professor Nicholas Diakopoulos during March and April 2015, and is discussed here.
 I have written about this issue in a Harvard Business Review digital article titled: “The risks of changing prices too often”. In it, I argued that rapid price changes confuse, frustrate, and annoy customers, and explained the reasons for this. It can be found here.
 Technology entrepreneur Tim O’Reilly has an interesting discussion about the complexity of algorithms that run the Uber app in his blog post here.
 See Cramer, H., Evers, V., Ramlal, S., Van Someren, M., Rutledge, L., Stash, N., & Wielinga, B. (2008). The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 18(5), 455-496. In this article, the authors show that when a recommendation provided by a recommender system was accompanied by an explanation of how it came up with the recommendation, users were more likely to accept it than without an explanation.