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]]>It is obvious that the network of friendships in a society influences its ability to collectively act, for example rebel against a regime. Almost everyone would agree that a society with few friendship connections has a more difficult time rebelling than a society with a rich network.

To make more specific predictions than this, one must use math and model networks explicitly. Many people have done this. For example, many people think of rebellion as spreading like a disease, and your probability of getting a disease is a function of how many of your friends have the disease. Another approach is to let your action (say your degree of rebellion) be a linear function of the actions of your friends (as in Roger Gould’s 1993 paper). These models are not game-theoretic, i.e. they do not model people’s preferences and how they make choices.

However, in a political rebellion, each person cares deeply about how many other people also participate. You are much more likely to participate if you know others will also. The standard way of modeling this is to give each person a “threshold”—a person will rebel if she knows that the total number of people also rebelling is at least her threshold. A person with a high threshold is more wary, and a person with a low threshold is more militant.

In my 1999 paper, I make a game-theoretic model of a political rebellion in which each person learns the thresholds of her friends in the network. It turns out that one interesting result is that your decision to rebel depends not only on what you know about your friends’ thresholds but also whether your friends know each other.

In some sense, this is obvious. If I have many militant friends but they are scattered about, and don’t know each other, then I might not revolt because I don’t know if they have other militant friends. However, if I have militant friends and I know that they all know each other, I am secure that I will not be alone when I rebel.

This argument has very specific implications about which networks are best for inciting rebellion. If you have a disease-like model, then networks which are very “non-local” and scatter widely are best. In a game-theoretic model, more “ingrown” networks, in which a friend of a friend is likely to be a friend, are advantageous. (This corresponds to the distinction between “weak” and “strong” networks made by Mark Granovetter in 1973.)

This argument could be right or wrong when applied to the real world, but it did not exist before game theory. In previous non-game-theoretic models (which could also be right or wrong), whether a person’s friends are friends with each other has no immediate impact on whether a person rebels.

Anyhow, I’m going on too long! I’m sure that other game theorists can chime in about how their own work is relevant.

]]>Hi Ariel—thanks. It’s true, one does not need game theory to come up with this explanation. Ex post, the explanation is in some sense obvious. But that’s a good thing, in my opinion.

Again, I can only speak for myself. I came at the question of Super Bowl advertising (and TV advertising in general) because I was motivated by a purely theoretical question—whether there are any real-world applications to the concept of “common knowledge” (arbitrarily many levels of metaknowledge). Without game theory, I would never have even thought that the price of ads on the Super Bowl was an interesting question. The larger point of the book “Rational Ritual” is that an important aspect of rituals (like the Super Bowl) is how they create common knowledge. People have been studying rituals and ceremonies ever since the ancient Greeks, and this point, in some sense obvious, could have been made long ago by people who are much smarter than I am. But (as far as I know) it wasn’t. I got to stand on the shoulders of game theory.

This argument is pretty specific for social science standards (!) but I can talk about other stuff. Most game theory papers will not be able to help people quantify the benefits of their actions, so that level of specificity might be tough.

]]>Thanks for reading my book, by the way! Michael

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