A Public Option for Dating Apps

Solving the under-provision of a public good this Valentine's Day

Hinge operates two feeds. The first, Discover, is free, with standard browsing, while Standouts is a curated feed of high-attention profiles. Standouts can only be contacted by sending a Rose, a paid currency. This system, in the dating app “designed to be deleted”, has caused much consternation, hacks, and workarounds, with the“Rose Jail” hashtag on TikTok reaching one million views. Users are spending time and ingenuity in fighting the discovery mechanism rather than discovering compatible people! Given that this is an app that markets itself most intensely on its reputation as a place for stable matches, the phenomena is a poor sign for the model of the for-profit dating app.

The funeral procession of statistics on American romance is well-known. 86% of adults 18-24 and 42% of 25-39 are unpartnered. 29% of US households are single-person, rising from 19% in 1974. 1 in 4 Americans aged 40 have never been married, but 69% of never-married 18-34 year olds say that they do want to marry someday. The percentage of adults under thirty that haven’t had sex in the last year has doubled since 2010. The staggering decline in coupling has entailed a collapse in childrearing, while the average number of births to married couples has remained steady.

Part of this decline is that dating apps have displaced in-person and within social-circle romance. The perceived optionality of dating apps has increased the cost of asking people out in other contexts. We have allowed a small group of firms to capture a crucial component of human social life, and the outcomes have been grim. The average dating app user spends around 51 minutes a day on dating apps. Dating app users show worse depression, loneliness, anxiety, and psychological distress. Self-selection is a plausible co-founder here, but longitudinal evidence for mental health effects of dating apps exist. Importantly, the matches are poor, with meeting on dating apps associated with lower relationship satisfaction. At this point, essentially everyone knows this, and has either seceded from dating entirely, continued to let the Match Group consume their time and attention, or taken the correct route of attempting to meet potential partners in person even with the high costs.

For-profit dating apps do not work.

The apps monetize engagement, not match quality. A successful match that leads to a lasting relationship removes two active users. The platform’s revenue-maximizing strategy is to keep users swiping, not to efficiently pair them off. Subscription, especially freemium models, reinforce this. Features that improve matching, such as unlimited likes, seeing who liked you, and better filters, are paywalled. We have become subject to a system of courtship equivalent to a labor market where recruiters are paid per interview rather than per successful hire.

A host of market failures doom the apps.

Congestion externalities. Highly desired users receive far more attention than they can process, and most users receive very little. The market is thin for the majority. Each message to a popular user reduces the probability any single message is read.

Adverse selection. Good matches leave the platform, concentrating the remaining pool with users who are harder to match or less serious. The average quality degrades over time, discouraging users and accelerating the cycle.

Moral hazard in effort. Low-cost, low-probability of success swiping incentivizes mass, low-effort signaling, flooding the system with noise and making genuine interest indistinguishable from spam.

Unverifiable quality. Profiles are cheap talk. Users can’t credibly signal attributes that actually matter for compatibility, whether humor, values, or conflict resolution styles. The attributes most predictive of relationship success are thus unobservable from a profile.

No cost to swiping. Without a budget constraint or meaningful costs for the action, there is no mechanism to force revealed preferences. Even crude attempts at imposing scarcity cut against profitability through user retention.

Algorithm opacity. ELO and other ranking systems, alongside opaque recommendation algorithms create hidden stratification, allow the platform to manipulate visibility for engagement, and force users to adopt strategies that do not optimize for stable matches.

No outcome feedback. Apps rarely collect or incorporate post-match outcome data. Without this, the algorithm can’t learn what a good match even is. Of course, collecting and utilizing this data is directly in opposition to the business model of the apps.

Not all of these are the direct result of the profit model, but there is no incentive for the apps to resolve them either. Solving congestion removes a site of profitability that can be exploited by providing paid tiers for prioritization or filtering.

New dating apps constantly deck the New York subway with advertisements, and many of them probably make real innovations in matching algorithms, but the bootstrapping hurdles are too high. Without a large, active base of users, the moat of Match Group and Bumble is impassable. Regardless, any improvement along the dimension of effectively making stable matches counteracts profitability as long as users are still highly resistant to paying for subscription services. The market failures require a mechanism design intervention that the private sector structurally cannot provide.

Thus, a modest proposal: governments, especially municipalities, ought to establish a public option for dating apps.

A municipal operator has no incentive to maximize engagement or retain users, the objective is to minimize time to stable match while protecting user privacy and autonomy. Specifically, the government should introduce a database for singles seeking relationships. This database should have a very large number of potential data fields, alongside the option to write longer dating-doc style profiles. Users would be able to select what types of other users they are visible to by any field, and can message any user with mutual visibility. To integrate outcome feedback, users will be required to answer a monthly outcome experience survey to be included on the app, including who they went on dates with and how those dates went. Once nudged, I believe most users would answer the questions honestly in order to improve their own experience, or to improve the experience of others should they have found a stable match.

However, this is a model that users already rejected in the early 2000s. It has high congestion costs and search costs, profiles are still self-reported and messages are still costless. All we would have done is removed the platform’s manipulation but left the information problems intact. This is the motivation behind an algorithm. But does the government know what a good match is for you? We are understandably resistant to seeing relationships like a state, and I share this skepticism. Frankly, users often don’t know their own preferences, with stated preferences diverging significantly from revealed preferences in swipe data.

Bluesky has a clever solution, an open API where users can publish and share their own algorithm. The government should control the data layer (the user pool) and the interaction layer (the messaging), but the discovery layer is entirely in users hands. Anyone can publish ranking, matching, and filtering algorithms that run on shared data. Users can choose which algorithms to use, stack a portfolio of algorithms, or just stick with raw browsing. If a user subscribes to an algorithm, they agree to share the necessary data, meaning that the market will likely converge to high-information algorithms if users are prioritizing match quality over privacy.

On Tinder, you can’t see why you are being shown the profiles you are shown. Here, algorithms are published and swappable. The wide diversity of matching preferences is respected. One algorithm could optimize for value alignment, another for pure geographic proximity, and other highly weighting behavioral data such as the quality of the outcome survey responses. Users could just have Claude sort through their messages or use an algorithm that sorts based on similarity to your sleep cycle with smart watch biometrics data. The space of possible methods is vast, and the government has no statement on the matter beyond obvious prohibitions on algorithmic harassment.

The state has removed the bootstrapping costs for innovation. The thing that kills alternative dating apps is that better mechanisms on small platforms lose to worse mechanisms on larger platforms. A great new matching algorithm that goes viral gets immediate access to the full user pool on day one. I imagine that the ecosystem for developing algorithms would be rather robust. Universities, community organizations (religious groups, sub-cultures), individual users, or the Gottman Institute could all enter the fray. It’s likely that users will gravitate towards popular algorithms, but running multiple simultaneously still allows for robust competition.

How does a non-technical user evaluate which algorithm is good? Users can recommend algorithms to others, rate them, or report them for violations of platform policy or fraud. Algorithms will be required to publish match outcome statistics. LLMs would of course be useful for reading an algorithm’s public code and describing it to lay users.

The private sector can and will not build this. The central value proposition of a commercial dating app is control over discovery. A private firm building such a platform would bear all of the infrastructure costs but let third parties capture the value in discovery, something no investor would fund. The experience of OkCupid after being purchased by the Match Group, its slow disintegration and convergence to Tinder, illustrates this.

Scott Alexander, two Valentine’s Days ago, wrote a paean to the fact that dating is one of the areas where the state leaves us to our own devices and doesn’t impose a wall of regulatory barriers. He asks us to imagine a world where:

• Nobody is allowed to date without a license. These work like drivers’ licenses; you have to take a short class, and pass a short test demonstrating that you understand consent and basic relationship skills.

• Dating licenses can be revoked for sufficiently serious crimes - eg cheating, domestic abuse, or persistent alcoholism/drug use.

• Three month waiting period for marriage.

• Centralized government database of who is in a relationship with whom at any given time. You can check the database to make sure your partner isn’t leading a double life.

• After three messy breakups, you have to take a remedial relationship skills class before you can date again.

• You can’t use race as a criterion for choosing partners. If someone thinks you rejected them because of their race, they can sue for unlawful discrimination.

All of these policies would be bad! The regulatory and suppressive powers of the state should not be applied to romance, or to much else. But the role of the state is not just to regulate and suppress, but to act as a space for coordinating collective action where the market has failed. A platform for effective long-term matches is a public good, more romance, more dates, more children, are under-provided by a market that has converged to Rose Jail. We should also make our cities denser, our cafe permits more rapid. We should also build community ourselves and host the in-person events indispensable to a healthy social world. But if the state seeks to ensure a flourishing romantic life for all, providing a public option for the way that most couples currently meet seems like a useful intervention on the margin.