Game Theory of (straight) Tinder
App-based dating has recently become incredibly prevalent in this day and age. One particularly popular app, Tinder, has over 75 million users. In a way, dating platforms like Tinder can be seen as a game, with the players being the individual users (this case is presuming a heterosexual couple so a man and a woman), the strategy being the kind of swiping they will employ (selectively swiping or spamming), and the payoffs being the chance of a good match leading to a romantic connection.
As detailed in the attached article, the best strategy and Nash equilibrium with the highest outcome for the man and the woman would be if both of them are selective, since that means that the match would consist of two interested parties. The worst outcome would be if the man and woman both spam for matches since they would end up with a bunch of matches they’re not interested in. If one person is selective and the other person spams, the payoff for the selective person is high since they get validation but the payoff for the person doing the spamming is low since they don’t end up with many matches.
However, this gets more complicated as, on Tinder, the match rate for women is much higher than for men. Women match 10.5% of their right swipes whereas men only match 0.6% on average. Therefore, the payoff for a man being selective on Tinder is much lower when the woman is also being selective since his chance of getting a match at all is that much lower. The result of this is a different Nash equilibrium, where the woman is selective and the man spams.
This is in no way an exact representation of what occurs, and there are many individual and subjective factors that need to be taken into consideration for the payoffs of being selective or spamming to be more accurate (such as the man/woman’s popularity since people who are more popular on Tinder would likely have a higher payoff for being selective). Overall, I thought this was a fun application of the Game Theory concepts we learned in class, and it is interesting how the reassessment of payoffs for the players can result in a drastic shift in the placement of the Nash equilibrium. This has me contemplating how payoffs are calculated in real-world strategy applications of game theory.
https://adelaideeconomicsclub.medium.com/a-game-theoretical-analysis-of-a-straight-side-of-tinder-9a18cf1fa782#:~:text=Game%20theory%20moderates%20strategic%20interactions,matches%20or%20spam%20right%20swipes.