Tinder is Uber, but for Dating
It wasn’t until I started reading up on the ‘Sharing Economy’ that I finally figured out what bothered me about dating apps.
I’ve realised something important about dating apps. They’re sharing economy platforms. It’s a big claim, I know — but stick with me here.
According to Tom Slee in What’s Yours is Mine: Against the Sharing Economy, there are basically four things that define something as a Sharing Economy platform:
- they are peer-to-peer platforms that do not provide a service to end users beyond connecting them,
- they have in-built rating systems that provide algorithmic regulation,
- they turn individuals into micro-entrepreneurs, and
- they take a non-market phenomenon powered by human relationships and turn them into a market exchange.
Tinder — and all its copycat apps, including Bumble, Hinge, Happn, OKCupid, and more, meet all four of these criteria. This isn’t a good thing — the sharing economy is basically a repeat of the 1990s dot com bubble, with slick young techrepreneurs and venture capitalists conspiring to overvalue unsustainable technology companies to sell on the open market for unimaginable profits. On the way there, they exploit people, disrupt lives, and change the face of cities for the worse.
I would add that they dissolve human connection and replace it with a ruthless and mercenary market logic and in the process, strip us of our empathy for one another. So why on Earth do we trust our most intimate decisions to one of these apps? And more importantly, what is it doing to us?
A peer-to-peer platform that does not provide services
In a way this is the most obvious point of commonality between Tinder and Uber. Even if it’s mediated by friends or family, the norm of the couple means dating is always a direct peer-to-peer connection. And it is also obvious that dating apps are platform-only offerings. With a small number of specific exceptions, dating apps don’t curate or manage dates, they only deliver the connection, and leave the rest up to the users.
The Sharing Economy heavyweights make the same claim about their business models, usually in the form of legal claims that their drivers or hosts are not employees. Slee gives examples of Homejoy (domestic cleaning services), Handy (domestic handyman services) and Instacart (home delivery services) as Sharing Economy platforms that make this distinction to negate employee entitlement claims and abrogate the need to pay their employees properly.
While dating app users are not concerned with minimum wage, they are still engaging in a dynamic fraught with power. Both Sharing Economy platforms and dating apps involve the intrusion of strangers into people’s domestic lives. When combined with the long history of heterosexual power dynamics and the contractual absolution of the ‘platform’ from any care or regard for their safety, this has chilling results.
There have been a staggering number of assaults and rapes by Uber drivers. AirBnb has had its own rape (and murder) scandals, but also other strange and disquieting crimes that are enabled by the unregulated intrusion of strangers into apparently private domestic spaces. Perhaps because they dangle the tantalising offer of a no-strings hookup in front of its users, internet dating services produce all sorts of negligent and malicious interpersonal behaviour, and more than their fair share of horrible sexual violence.
But the common element here isn’t rape. The common element is the complete disregard by a profit-seeking technology corporation for the welfare of the people using its service, and the denial of any responsibility to those users through the idea that their platform is the service, not the end “product” people seek by engaging in it — a room for the night, a lift, a date.
Algorithmic regulation through a rating system
At first Tinder operated on a simple premise: swipe right for ‘hot’, swipe left for ‘not’. As the number of users grew, problems emerged. Even with a geographical filter that limited potential matches, there were too many people to swipe through. There was no way that the app could guarantee that an unrequited swipee could ‘close the loop’ by swiping right back, because there were just too many people in the queue.
An obvious fix was to move the swiper higher up in the swipee’s queue to minimise the time between a one-sided swipe and a match. That didn’t account for the second problem: the breakdown of the ‘matching hypothesis’, which claims that people date around their own level of desirability. On the Apps, for whatever reason, people tended to ‘swipe up’, hoping to match with people significantly more desirable than themselves. This has consequences for the utility of the app.
Enter what Slee calls “algorithmic regulation”, a key component of Sharing Economy platforms. In November 2016, Austin Carr reported in Fast Company that Tinder had shifted to an Elo score to rank users’ desirability. Although Tinder was tight-lipped on exactly how the system worked, Carr surmised that your ranking went up when users’ right swipes were reciprocated and dropped with a large number of unreciprocated swipes. As a user’s Elo score settled (the system needs at least 50 ‘matches’ to start working), the app began to match users according to Elo score.
Slee points out that on most Sharing Economy platforms, ratings conform the ‘j-curve’, which means that ratings tend to cluster at the top and bottom of the five-point scale. There are vanishingly small numbers of 2 or 3 ratings, a tiny proportion of 4 ratings, and a huge number of 5s and 1s. In practise there is very little difference between a 5-point scale where everyone only rates people 5 or 1, and a binary hot/not scale.
But ratings serve a different function on Tinder and Uber. There’s no public aspect to Tinder ratings, while other Sharing Economy platforms maintain public ratings as a form of regulation. Even when rating systems are present they don’t seem to do anything useful. Slee points out that an Uber rating is most likely based on politeness and cleanliness — the products of emotional labour — rather than whether the brakes on the car have been well maintained or the driver has a history of sexual assault.
He argues that communities manage trust and reputation through the intersubjective ties that constitute them, giving the example of a hypothetical plumber whose reputation spreads by word of mouth. Being able to tell your friend not to hire someone — or date someone — because they were good on paper but in person something was ‘off’ is a valuable subjective insight that an algorithm cannot reproduce.
The algorithms at the regulatory heart of the Sharing Economy are terrible replacements for community. Algorithmic rating systems condense a complex subjective process into a simple, unaccountable yes/no. The problem is, you can’t distil ‘red flags’ to a 0 or 1. When those ratings aren’t even public, their already minimal regulatory capacity dwindles to nothing.
Tinder ratings and the algorithms they drive are based on the entrepreneurial investment users make into what might be thought of as their ‘dating capital’. A Tinder profile is based on a few photos and a line or two of text, but the difference between a good profile and an average one is how much work goes into those elements. This work is understood as effort towards making the self more attractive and appealing, to maximise likes, followers, and — on dating apps — right swipes.
This bears an eerie resemblance to the theory of Human Capital, which for Bruce Pietrykowski involves people thinking about themselves “an investor seeking to maximise the rate of return on their asset. And they are the asset”. Nikolas Rose and Peter Miller specify that by thinking about ourselves this way, we are “solicited as allies of economic success” through investment in the “management, presentation, promotion and enhancement” of our own capital.
There is a genre of Instagram post in which ‘models’ and ‘influencers’ reveal just how complex the staging of every good Instagram photo is. They usually talk about the work of framing, composition, lighting, environment, and the learned skill of managing facial and bodily comportment as essential investments in a ‘hot’ profile pic. Often the production of hotness requires at least one assistant. There are also financial and skill investments in dressing and maintaining the body, which involve interpreting fashion, shopping for, purchasing, learning how to use and applying clothes and cosmetics, as well as time-consuming and difficult diet and exercise regimes that produce certain kinds of desirable bodies. And that’s before you even get to the luck of having been born thin, or white, or late enough to capitalise on your youth online.
The theory of Human Capital helped produce what Foucault describes as the ‘entrepreneurial subject’. Airbnb CEO Brian Chesky is fond of using the word “micro-entrepreneurs” to describe users of his platform. In an interview with Stephen Colbert in 2014, he said “what the Sharing Economy really means is that now people in 60 seconds can be micro-entrepreneurs”. He also voiced a dream that “everybody should be able to participate in the economy like a corporation” This is almost exactly what Foucault meant when he described human capital as a theory under which the worker “appears as a sort of enterprise for himself”.
It’s telling that Stephen Colbert opened his interview with Chesky with a joke question that connected Sharing Economy platforms with sex: “What’s is the difference between Airbnb and home prostitution?” Those who are most successful on both Tinder and Airbnb are those who invest enormous amounts of time, energy and resources into their own human capital, learning to present themselves or their homes as available and desirable. The uneven rewards of Sharing Economy platforms, combined with the isolation of individuals as the only ones accountable for their own success or failure in the ‘markets’ these platforms create, mean that everyone who participates is bound to think about themselves in these entrepreneurial terms.
Turning human relationships into a market exchange
Tinder abandoned its Elo ranking system in 2018. It seemed that the vast majority of users without the skills or resources to make their profiles stand out didn’t enjoy seeing that failure reflected in the unappealing profiles they were served up by the algorithm. Disenchantment was one predictable response. So Tinder changed the algorithm again, probably to something called the “Gayle-Shapley algorithm”.
There are two important things about the Gayle-Shapley algorithm for our purposes. Firstly, it is a solution to something called the ‘stable marriage problem’. Secondly, when Lloyd Shapley won a Nobel Prize in 2012 for a different statistical solution to the same problem, the citation was for “the theory of stable allocations and the practice of market design”.
The ‘Stable Marriage problem’ is one of matching preferences. It assumes the existence of a gender-balanced group of heterosexual people, and asks, how can we match them up in stable pairings? All pairings are stable when no two matched people would rather date each other over their current partners. In a set of stable marriages, unrequited extramarital desire is possible, but not a full-blown affair.
The fact that marriage is used as a metaphor for free market behaviour tells us that the designers of the problem and its solution think about dating as a problem of resource allocation. This is echoed by the title of the University of Michigan study that concluded that users on Tinder tend to ‘swipe up’: “Aspirational pursuit of mates in online dating markets”.
The Sharing Economy model is ultimately very simple: find an aspect of human relationships that can be reconceptualised as a problem of exchange and replace it with an algorithm-driven market. Airbnb was born out of the idea that people like to keep airbeds, sofa beds and guestrooms so that their friends and loved ones can use them. We’ve all helped friends move house or build Ikea furniture, Airtasker commodifies that. There was even a short-lived burst of Sharing Economy apps — Neighbourgoods, 1000tools, and Open Shed — that turned neighbours lending each other power tools into a market transaction.
Slee quotes a venture capitalist who calls Sharing Economy apps “people marketplaces”. This is exactly what Tinder has turned dating into, except the value that is traded is desirability and dating capital, and the exchanges are of bodily fluids in preference to money. The proliferation of ‘Sugar babies’ — or the trading of youthful femininity for money — means that there is now an internet dating specifically for rich men to buy the companionship of attractive young women. In the Sharing Economy, dating is governed by some very old forms of exchange.
Tinder fits all four criteria of a Sharing Economy platform. It’s a peer-to-peer internet platform with an algorithmic rating system that turns individuals into micro-entrepreneurs and turns human relationships into a market. And we all use it: nearly 40% of (US) couples report having met through internet dating.
Using a Sharing Economy platform is never a values-neutral proposition. There are conceptual terms and conditions you have to accept — mainly, the twin pillars of neoliberalism, which argue that the market is the best regulator and rational, self-interested decision-making within that market will always lead to the best outcome for the individual. Sharing Economy apps embody the regime of thought we live under; one in which we are all endlessly competing against one another for scarce resources, and in that Randian nightmare, owe each other nothing. The Elo ranking system that Tinder flirted with was designed to rate chess players who were engaged in competition with one another, to figure out who was better at the game. Whether by accident or design, Tinder is attempting to remodel romantic relationships into a competitive free market.
In Debt: The First 5,000 Years, David Graeber argues persuasively that so-called pre-industrial ‘barter’ economies were in fact rich and dense networks of mutual trust and obligation that meant resources could freely change hands without the intervention of a medium of exchange. Money, on the other hand, was what people used when they dealt with someone from outside their network, who they had no way to trust. A free market is always already characterised by a lack of obligation beyond a mercenary exchange of value and a concomitant suspicion of the other.
It’s telling that the sociologists cited above who study dating apps called them a ‘dating market’. The inherent lack of trust in markets — even those governed by so-called reputation ratings — makes any unregulated market a good place for scammers. This lack of trust is perfectly demonstrated through the range of awful behaviours that are so prevalent in online dating that we’ve had to invent a rich vocabulary of new names for.
The assumption behind Uber, Airbnb and Tinder is that is we want to look beyond our networks. That we’ve already assessed our friendship networks, or communities, and figured out they can’t meet our transportation, accommodation or romantic needs. But throwing ourselves on the mercies of strangers to meet these needs puts us in a much more suspicious, ruthless, and above all competitive relation to others. Who are we competing with, exactly? If it’s a competition, what is the ‘prize’ for winning? Women are used to treating first dates as potential threats to their bodily security. Far from eliminating dating’s incipient sense of danger, the Sharing Economy mindset could make it proliferate to everyone. And what, exactly, happens when everyone thinks of a potential partner as a resource to be nakedly competed for (and not in the fun way)?
We shouldn’t underplay how massive this conceptual shift is, or how at odds it is with the romantic ideals of coupling we claim to hold. If we let the apps teach us to treat dating like a market, alienation and suspicion are the overwhelmingly likely consequences, in which case we really are in the ‘Dating Apocalypse’.