David A. Price does an "Interview" with Matthew Jackson, with the subheading "On human networks, the friendship paradox, and the information economics of protest movements" (Econ Focus: Federal Reserve Bank of Richmond, 2021, Q1, pp. 16-20). Here are a few snippets of the conversation, suggestive of the bigger themes.Homophily[O]ne key network phenomenon is known among sociologists and economists as homophily. It's the fact that friendships are overwhelmingly composed of people who are similar to each other. This is a natural phenomenon, but it's one that tends to fragment our society. When you put this together with other facts about social networks — for instance, their importance in finding jobs — it means many people end up in the same professions as their friends and most people end
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[O]ne key network phenomenon is known among sociologists and economists as homophily. It's the fact that friendships are overwhelmingly composed of people who are similar to each other. This is a natural phenomenon, but it's one that tends to fragment our society. When you put this together with other facts about social networks — for instance, their importance in finding jobs — it means many people end up in the same professions as their friends and most people end up in the communities they grew up in.The Friendship Paradox
From an economic perspective, this is very important, because it not only leads to inequality, where getting into certain professions means you almost have to be born into that part of society, it also means that then there's immobility, because this transfers from one generation to another. It also leads to missed opportunities, so people's talents aren't best matched to jobs.
This concerns another network phenomenon, which is known as the friendship paradox. It refers to the fact that a person's friends are more popular, on average, than that person. That's because the people in a network who have the most friends are seen by more people than the people with the fewest friends.Causality in Networks
On one level, this is obvious, but it's something that people tend to overlook. We often think of our friends as sort of a representative sample from the population, but we're oversampling the people who are really well connected and undersampling the people who are poorly connected. And the more popular people are not necessarily representative of the rest of the population.
So in middle school, for example, people who have more friends tend to have tried alcohol and drugs at higher rates and at earlier ages. And this distorted image is amplified by social media, because students don't see pictures of other students in the library but do tend to see pictures of friends partying. This distorts their assessment of normal behavior.
There have been instances where universities have been more successful in combating alcohol abuse by simply educating the students on what the actual consumption rates are at the university rather than trying to get them to realize the dangers of alcohol abuse. It's powerful to tell them, "Look, this is what normal behavior is, and your perceptions are actually distorted. You perceive more of a behavior than is actually going on."
Establishing causality is extremely hard in a lot of the social sciences when you're dealing with people who have discretion over with whom they interact. If we're trying to understand your friend's influence on you, we have to know whether you chose your friend because they behave like you or whether you're behaving like them because they influenced you. So to study causation, we often rely on chance things like who's assigned to be a roommate with whom in college, or to which Army company a new soldier is assigned, or where people are moved under a government program that's randomly assigning them to cities. When we have these natural experiments that we can take advantage of, we can then begin to understand some of the causal mechanisms inside the network.Live Protests vs. Social Media
[I]t's cheap to post something; it's another thing to actually show up and take action. Getting millions of people to show up at a march is a lot harder than getting them to sign an online petition. That means having large marches and protests can be much more informative about the depth of people's convictions and how many people feel deeply about a cause.If you would like more Jackson, one starting point is his essay in the Fall 2014 issue of the Journal of Economic Perspectives, "Networks in the Understanding of Economic Behaviors." The abstract reads:
And it's informative not only to governments and businesses, but also to the rest of the population who might then be more likely to join along. There are reasons we remember Gandhi's Salt March against British rule in 1930 or the March on Washington for Jobs and Freedom in 1963. This is not to discount the effects that social media postings and petitions can have, but large human gatherings are incredible signals and can be transformative in unique ways because everybody sees them at the same time together with this strong message that they convey.
As economists endeavor to build better models of human behavior, they cannot ignore that humans are fundamentally a social species with interaction patterns that shape their behaviors. People's opinions, which products they buy, whether they invest in education, become criminals, and so forth, are all influenced by friends and acquaintances. Ultimately, the full network of relationships—how dense it is, whether some groups are segregated, who sits in central positions—affects how information spreads and how people behave. Increased availability of data coupled with increased computing power allows us to analyze networks in economic settings in ways not previously possible. In this paper, I describe some of the ways in which networks are helping economists to model and understand behavior. I begin with an example that demonstrates the sorts of things that researchers can miss if they do not account for network patterns of interaction. Next I discuss a taxonomy of network properties and how they impact behaviors. Finally, I discuss the problem of developing tractable models of network formation.