Cupid Tinder: The Risky Business of Modern Romance

 

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The Anxious Wait for your Date… 

You anxiously sit on the edge of your seat, nervously waiting for your Tinder match to arrive for your first date. You peer through the scores of people walking into the restaurant, recalling her facial features, so you can wear your charming smile to welcome her to the table. “5’3, brunette, brown eyes”, you murmur in eager anticipation for her arrival. She walks in, 5’3, brunette, brown eyes, indeed but you notice something off about her.

This is how they start, the horror tales of lonely singles, desperately looking to fairy godmother Tinder, to find their perfect match in a modern marketplace where, much like a flawed market, distortions turn hope into heartbreak. Their desperate search for Prince Charmings and Cinderellas is often marked by exaggerated descriptions, unexpected twists and deceitful portrayals of their “perfect” match. Since this is a blind date and you’ve never met before, you put your trust in the all-knowing matchmaker, to match you up with the perfect gal. But what you don’t know is that even the fairy godmother doesn’t know it all, as your date deceitfully holds information from you. This is known as asymmetric information from economic theory, where one party in a (romantic) transaction withholds crucial information and knows more than the other party, leading to market failure and maybe in this case, a romantic disaster. Studies even show that 42% of Tinder users already have a partner, highlighting how widespread this imbalance of information can be (Sirisha, 2023).

You arch your brows in scepticism as she cheerfully approaches your table, vividly remembering her pearly white teeth and her radiant glow from her Tinder profile. So now you sit confused in your chair, wondering why her teeth have a rusty brown stain or why her face is dented with piercings you can’t seem to count the full number of. “Hi, I’m Catherine from Tinder! Edgar, right?” she grunts. A wave of shock sweeps over you as you think to yourself, “What have I gotten myself into?” And to think you used Tinder premium. 

The digital matchmaking market exemplifies the adverse selection problem, where users present carefully curated versions of themselves to appear more desirable. Users, trusting the sophisticated match-making algorithms to do all the work for them, don’t realise that crafty foxes can infiltrate the system and give a deceitful portrayal of who they really are, curating their profiles to sound picture perfect, claiming job titles they don’t carry and boasting cars they probably can’t even afford. Recent figures reveal that 40% of men admit to lying about their finances, and 20% of women misrepresent their age (LegalJobs, 2024). Although these may be small falsehoods, in a macro setting, they swing the balance of trust in a high-risk marketplace and skew the outcomes in the market.

I mean, come on - why would a Bentley owner be on Tinder? 

The Truth Behind the Profiles 

As mentioned before, asymmetric information, in economic theory, comes into play when one withholds critical information so as to not sound any less than perfect. A recent report shows that 53% of online dating profiles contain some kind of false information, and about 10% of people you swipe on aren’t who they say they are at all (RTÉ, 2024). I guess the fear of rejection can make you do crazy things. Behaviour as risky as insulating truthful info about who we really are on dating apps like Tinder leads to a moral hazard, where bad apples misrepresent their qualities because the immediate cost of being dishonest is minimised. This leads to poor souls like Edgar making misinformed dating choices because of the poor signals given by bad actors such as Catherine. 

Can Tinder Premium Help? 

As economic theory states, signalling is meant to solve the problems of asymmetric information, where the knowledgeable party alerts the other about their unobservable characteristics. This solidifies trust in the knowledgeable party and in the trustworthiness of the romantic transaction. Tinder’s Premium subscription feature acts as a signalling feature to prove the authenticity of the potential date. Features such as prioritised likes, where your profile gets shown to potential matches sooner, and rewind, where you can undo your last swipe so you don't mistakenly swipe right on someone you don’t fancy, can help enhance a candidate’s commitment and authenticity, making them a very attractive candidate. Moreover, Tinder’s age and identity verification features seem to prove the legitimacy of someone’s profile. So how did Catherine evade the system? 

Are Dating Apps Really Helping You? 

Does it fall on the dating app to mitigate these circumstances? Or maybe they want to keep you swiping. Sure, this one failed date with Catherine wasn’t fun, but perhaps you can try again next time. After all, the entire app's purpose is to bring people together. But if we apply the principal-agent problem, when an agent (dating apps) has different incentives than the principal (you, the user) - we start to see a different picture.

You, the principal, want to find a meaningful connection as quickly and efficiently as possible. But dating apps, the agent, thrives when you don’t find one or at least not too soon. Their revenue comes from keeping you engaged, whether through premium subscriptions, paid boosts, or super likes. The longer you swipe, the more they profit. Dating app revenues have increased every year since 2015, reaching $6.18 billion in 2024 (Curry, 2024). If dating apps were too effective at matching people instantly, their business model would collapse. 

This creates an incentive misalignment: while you’re looking for an exit (a happy relationship), dating apps are subtly designed to keep you in the game. Algorithms prioritise keeping you hooked rather than necessarily showing you the best possible match. Features like limited daily likes, blurred profile visibility, and paywalled perks encourage users to spend more time, and sometimes more money chasing the perfect match that always seems just out of reach. So maybe it isn’t really your fault that you’re still single.

References

Bapna, R., Ramaprasad, J., Shmueli, G., & Umyarov, A. (2023). One-way mirrors and weak-signaling in online dating: A randomized field experiment. Management Science. https://doi.org/10.1287/mnsc.2023.4951  

Curry, D. (2024). Dating App Revenue and Usage Statistics (2024). [online] Business of Apps. Available at: https://www.businessofapps.com/data/dating-app-market/.

Shen, H., Dang, C. (I.), & Zhang, X. (M.). (2024). Mr. Right or Mr. Best: The role of information under preference mismatch in online dating. Information Systems Research, 35(4), 2013–2029. https://doi.org/10.1287/isre.2022.0233 

Sirisha (2023). How Many People Get Catfished? [Catfishing Statistics 2023]. [online] legaljobs.io. Available at: https://legaljobs.io/blog/catfishing-statistics.