Decoding Friendship: Paradoxes and Biases in Your Social World
Explore the intriguing phenomenon known as the 'Friendship Paradox' and its impact on various aspects of life, from social networks to dating, sports, and beyond. Uncover how this paradox unfolds in different scenarios, such as classroom dynamics, online networks, and even transportation systems.
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It’s an actual phenomenon called the ‘Friendship Paradox.’ It’s a paradox that doesn’t only apply to friendships but also to other areas of your life, such as dating, social media, sports, and pretty much most things that can be described as a social network. We’re going to be looking at how this paradox affects your life and why it’s good to be aware of it. Let’s try to understand: How does this paradox work? How can it even be possible? If our friends have more friends than us, then the paradox can’t possibly be true for them as well, right?
Let’s imagine a classroom with 9 students. Let’s assume everyone has 2 friends in this scenario; everyone’s friends also have 2 friends each. Therefore, the Friendship Paradox is not true for anyone right now. Everyone's friends also have 2 friends each. However, in almost every social network, there are people with more connections than others. Let’s add a tenth person who’s a bit more popular than his colleagues. He's friends with 6 people. Let's see what that does to the network. Let's start with the people on the sides who are not connected to him. The people on the edges continue to have 2 friends, but now those friends have 3 friends each since they are now connected to the popular guy.
The Friendship Paradox is now true for this case. The people in the middle now have 3 friends, but those friends have an average of 3.7 friends. That’s because they’re now friends with the popular guy who skews the average up. The Friendship Paradox is also true for the people in the middle. The only one who is not affected by the Friendship Paradox is the popular guy, who has way more friends than his friends. In this scenario, the Friendship Paradox is true for 90% of the classroom. It’s actually a simple phenomenon if you think about it: Popular people are friends with many people.
Therefore, they are more likely to be your friends, and they skew the average up. Social networks often have a small group of people with many connections. This phenomenon is actually quite common in other real-world networks as well. Networks are often concentrated around certain points, or nodes, the technical term, that have more connections than the remaining nodes. For example, this affects online networks. There's a Facebook dataset collected by Stanford with data from 4000 volunteers. If we look at this data, we see that 87% of those people have fewer Facebook friends than their Facebook friends. The Friendship Paradox is also visible in transportation networks.
If we look at the Eurail train network, we see that each city is connected to 2.7 cities on average, whereas their neighbors are connected to 3.8 cities. There's a group of central stations which have more connections than their neighbors and are responsible for most of the traffic. Notice how most of them are European capitals. This phenomenon is also relevant to understanding how diseases spread. People with more social connections are more likely to catch and spread infections. So, how does this affect your life? In dating, it’s more likely that your partners have had more partners than you. In this simulation, 78% of users who got matches matched with people that had more matches than them.
In this case, they have more friends than the typical person. In statistics, this is called sampling bias: When our sample of data points is biased in a way that does not represent the entire population. That’s why we should be careful when we make generalizations from the things we see. We often understand reality through social networks. We follow newspapers, influencers, and friends. We might think they give us an accurate image of the world, but they can also suffer from sampling bias. Today's sponsor tries to remove the sampling bias from your news.
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