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« on: March 03, 2025, 09:34:58 pm »
nstalling a dating app, two pals give it a shot. The man makes an effort to create the ideal profile. A lovely frontal image. A group photo to give the impression that he has buddies. A photo taken in Paris gives the impression that he is cultured. And an image of him scaling mountains gives the impression that he is daring. After much effort, the profile is complete. The woman decides to use the first image she finds because she doesn't feel comfortable disclosing much personal information. They begin to swipe and pray for the best. When the woman checks her phone at the end of the day, her counter is complete.
Almost every profile she likes is a match right away. She soon finds her inbox overflowing with matches and messages. It's a different tale for the male. He hasn't had any matches yet and has only gotten a few likes. He loses patience with the app and begins to doubt his self-worth. After all, he spent a lot of time creating a great profile. How come he can't find any matches? We must comprehend the statistics underlying dating applications to respond to that query.
To determine why males receive so few matches on dating apps, I created a simulation using 1000 dummies. The absolute dating scene may reflect something other than what is seen on dating apps. Some research suggests that dating apps can lower self-esteem, with men being more affected than women. And women frequently need to develop coping mechanisms for men's intrusive behavior on these apps. Because so little information is provided, it can be challenging to comprehend exactly what is happening within these apps. Nevertheless, we can infer conclusions from the scant data we have.
I'll start with a perfect, fanciful scenario, and then I'll start introducing real-world elements to watch how rapidly things change. We're discussing dating people of different genders as an aside. The mechanics of same-gender relationships on dating apps are completely different, so this video is not about them. Let's say there are an equal number of men as well as women accessing the app because we're aiming to create the best-case scenario. Let's assume that everyone views 100 profiles daily and that the algorithm treats each profile equally. I suppose that users only like one of every four profiles they see. This indicates that every time a user's profile is displayed, there is a 25% chance that they will be liked. It's okay that some of these constraints aren't realistic. Starting this way, we'll gradually make it more plausible. So, at the end of the day, how many likes as well as matches will everyone receive? Run the simulation now. Men and women receive 25 likes and 6 matches daily on average. This seems different from actual dating applications, but why?
Let's begin with the first justification: Male users outnumber female users. For two of the most well-known dating applications in the world, Tinder and Bumble, I was able to locate user gender information. In both apps, there were noticeably more men than women users. I will keep things simple in our simulation and suppose that there are 2 males for every woman, a ratio that falls between Bumble's and Tinder's. The differences were significant when I reran the simulation. You'll have an opportunity to pause the movie if you want to make an educated prediction about the number of likes and matches users receive. The data are now showing the first hints of a gender gap. Because there are twice as many men as women, ladies receive twice as many likes, while males only receive half as many. And intriguing things happen when we look at the number of matches. Women received 50 likes on average. You may anticipate they would receive an average of 12 or 13 matches since they like 1 out of every 4 profiles. But they only receive 6. This is because there are so many male users right now that women barely have an opportunity to see half of the individuals that liked them. There are simply too many males waiting since they can only view 100 profiles daily. It is understandable at this point for ladies to begin to feel a little overwhelmed by the number of likes they are receiving.
Additionally, girls are forced to consider carefully who they give likes to because they frequently experience men's intrusive behavior. At the same time, men are beginning to show signs of desperation. They know they can't be too selective because they don't receive many likes, so they start distributing likes more freely to increase their chances of finding matches. And that brings us to reason two: Men like more than women. Men are almost three times more likely than women to like a profile on Tinder, according to this 2014 New York Times story. So let's apply those figures. In the simulation I'm updating, women and men offer likes in 14% and 46% of cases. What impact do you believe this will have on the outcomes? The gender gap has now grown much more comprehensive. Men only get 7 likes compared to women's average of 92. Men like 46% of the users they see; therefore, this results in an average of 3.2 matches from these 7 likes. Women receive 6.4 matches per day on average, two more than males. For the typical male user, things might become even more challenging. Although subjective, there is evidence that more people will find some profiles attractive than others. This takes us to the third reason: A small percentage of individuals receive most of the likes. One of the Hinge developers provided some information regarding this issue in a Q&A post in 2017 on the company's website. He emphasized how some individuals receive astronomically more attention than others: According to his findings, 25% of women received likes from men, while only 15% of men received likes from women. This indicates that a small group of users receives a significant portion of the total likes, particularly among men. Try to incorporate that into our simulation, please. I'm assigning each user a score, ranging from 0 to 100%, based on how attractive other users find them to be. Until now, attractiveness has had no bearing on the likelihood of receiving likes. This means that regardless of how appealing a profile was judged, there was a 46 and 14% chance that it would be liked each time it was displayed for a particular gender.
I'm currently seeking a new distribution that ensures the top users receive significantly more likes while maintaining the same average like percentages. I used the most straightforward formulas I could think of. I expected that people with a score of 0 would have a 0% probability of receiving likes and users with a score of 100 would have a 100% chance of doing so. Although this is oversimplified, I'm doing my best to keep it that way because I couldn't find any data.
According to these curves, 50% of male likes to go to the top 27% of female users, and 50% of female likes to go to the top 10% of male users, which is quite similar to the data provided by Hinge. So, this ought to be relatively accurate. Let's start the last simulation. You can now try to predict the outcomes.
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