Why thinking in models makes us a more intelligent citizen?

       I want to talk a little bit more about how using models can help make you a more intelligent citizen of the world. And so, we're gonna break this down into a bunch of set of sub-reasons about why models make you better able to engage in all the things that are going on in this modern, complex world in which we live.
      Okay, so. When we think about models, they're simplifications. They're abstractions. So in a sense, there's a sense in which they're wrong. There's a famous quote by George Box, where he says, "All models are wrong." And that's true, right? They are. "But some are useful." And that's gonna be a mantra that comes up throughout this course. These models are gonna be abstractions, they're gonna be simplifications, but they're be useful to us. They're gonna help us do things in better ways. 'K? And this is a, a big thing in this course. Models are the new lingua franca. They're the language of not only the academy, you know, which I talked about some in the last lecture, but they're the language of business. They're the language of politics. They're the language of the nonprofit world.
       Wherever you go, where there's people are trying to do good, make money, cure disease, whatever it is that they wanna do, right? You're gonna find that people are using models to enable them to be better at what their purpose is. Okay? That's why they've really become the new lingua franca. So, if you think back. Remember, I talked about this in the first lecture. The whole idea of having a great books movement was that there was these... set of ideas that any person should know. So within the hundred and so great books, there were thousands of ideas. And one of our learners, Robert Hutchins President of the University of Chicago，they had this thing that they wrote called the Synopticon which was a list, as they put this together. This was kind of list of sort of all the ideas that someone should know, that an intelligent person should know.
      So what are those ideas? So one of those ideas was to tie yourself to the mast桅杆. And this comes from the Odyssey长途冒险, this says the ship is going past the sirens汽笛 and he wants to hear the sirens beautiful love song. So what he does is he has his crew tie him to the mast. He ties himself to the mast so he can listen to them but pre-commit to not driving his boat over to hear the sirens, at the same time he puts wax in the ears of his crew so they also won't be encouraged to sort of drive the boat over there. Well this is an idea that recurs in history when we think about. Cortez burning his ships, so his men won't retreat, they'll continue to advance. So this idea to tie yourself to math, is a real worthwhile thing.
       But here's the problem. One of, one of my favorite websites is a website called Office of Proverbs. So on this websites, it says things like he who hesitates is lost, a stitch in time saves nine, or two heads are better than one, too many cooks spoil the broth. So you've got to hear this really good advice, something that probably made it in the Synopticon, but then you get something that says the exact opposite. Well, how do they adjudicate裁判 between those two things? The way we adjudicate between those two things is by constructing models because models give us the conditions under which he who hesitates is lost, and then there's the conditions under which a stitch in time saves nine. So when we talk about the wide diversity and prediction, we'll see why it's the case that two heads is better than one, and we'll see why it's the case that too many cooks spoil the broth. So, ironically, what models do is they tie us to a mast, they tie us to a mast of logic and by tying us to a mast of logic, we figure out which ways of thinking, which ideas in this Are useful to us.'K? So, if you look at almost any discipline, whether its economics, and here what you see in this diagram, is you see a description of sort of, this is a, a utility function for an agent. And what that agent is doing trying to maximize their pay-off, right? So, economists use models all the time. Biologists use models, as well. They have models of the brain, where they have little axons and dendrites going between the neurons. They have models of gene regulatory networks. They have models species, right?
      Things like that. Sociology, we have models, as well, right? So, there're models of, sort of. How your identity effects your actions, and your behaviors and things like that. Okay, in political science, we have models. Political science these days, this is a picture of a spatial voting model. So they might say candidates are a little more conservative on certain dimensions and voters are a little more conservative and you say that, well, you're more likely to vote for a candidate who takes positions similar to yourself.
      So my work at the University of Michigan we have something called the National Election Studies that's run out of there where we sort of gather all this data about where politicians are and where voters are, and that allows us to make sense of who votes for whom and why. Okay? So models help us understand the decisions people make. Linguistics, right? Here's another area, right? So you might think, how can you use models in linguistics? Well, this little model here, you see things where it says you see v's and n and p's in here, if you look closely. Well, v stands for verb, n stands for noun, and well you gotta. And S stands for, you know, subject, let's say, right? So you can do this: you can ask "What is the structure of a language?" You can ask, formally and mathematically, what the structure of a language is, and whether some languages are more like other languages or not, depending on how people, you know, set up their sentences.
      So in German, they may put all the adjectives at the end of the sentence that looks very different from English. Even the law. This is a graph from one of my graduate students, former graduate student. Now, he's a law professor, Dan Katz. He's got sort of a network model of which Supreme Court justices who they appoint, if someone appoints judges from some other judge. By putting that data that's out there in this sort of model-based form, we can begin to understand how conservative and how liberal certain judges are. All right? So, there's lots of ways to use models, and there's even whole disciplines now, that have evolved, that are based entirely on models. So, game theory, which is what I was really trained in as a graduate student, is all about strategic behavior. It's the study of strategic interactions between, individuals, companies, nations.
      Right? And game theory can also be applied to biology, right? So there's all sorts of stuff, right? When you go to college, you'll find that there's game theory models of just about anything. So it's actually a field based entirely just on models. Why, right? [laugh] Why all these models, right? Why does anything from linguistics, to economics to political science use models? Well, cuz, they're better, right? They're just better than we are. So, let me show you a graph, here. This is a graph from a book by Phil Tetlock. It's a fabulous传说的 book. And in this graph, he, what he's showing is, he's showing the accuracy of, some different ways of predicting. So, what you see on this axis, this calibration axis, right here. This is asking, sort of how, showing you how accurate a model is. And this axis is saying how discriminating it is, in terms of how particular, how fine of predictions it is making.
        So, instead of saying is it hot or cold, it might be saying it's gonna be 90 degrees, or 80 degrees, or 70 degrees. So this axis here, this up and down axis, is discrimination, and how this axis is accurate. So, what you see here, down here, are hedgehogs刺猬. So, these are people who use a single model. Hedgehogs are not very good at predicting. Right? They're terrible at predicting. Up here are people he calls foxes狐狸. Now, foxes are people who use lots of models. They have sort of lots of loose models in their head. And, they do much better at, you know, sort of at calibration, a little bit better at discrimination, than individuals. But way up here, [laugh] better than anybody, are formal models. Formal models just do better than either foxes or hedgehogs. Now [inaudible] how much data is this? Tetlock actually had tens of thousands of predictions. So, over a 20-year period, he gathered predictions by people. And compared how those people did to models. And the answer is models do much, much better. Okay. All right, so. What about people, then, who actually make predictions for a living? So, this is a picture of Bruce Bueno de Mesquita, who makes predictions about what's gonna happen in international relationships, and he's very good at it. He's so good at it that they put his picture on the cover of magazines, right? He's at, Stanford and NYU, Chair of the department at NYU used to be, anyway. So, Bruce, uses models. He's got a very elaborate model that helps him figure out, based on sort of bargaining position and interest, what different countries are gonna do. But,just like George Box said at the beginning, he doesn't base his decision entirely on that model. What the model does is gives him guidance as to what he then thinks. So, it's a blending of what the formal model tells him, and. Experience tells them so smart people who use models but the models don't tell them what to do. Okay. Another reason models have taken yeah they are better but they're also very fertile. So once you learn a model. It's, you know, for one domain, you can apply to a whole bunch of other domains, which is fascinating. So we're gonna learn something called mark-off processes, which are models about dynamic processes. So they can be used to model things like disease spread and stuff like that, right? We're gonna finally learn though that you can also use them, this is sorta surprising, to figure out who wrote a book. >> [laugh] And they say, how does that happen? Well that happens because you can think of words, writing a sentence, as an anemic process. Different authors use different sequences of words and patterns. So therefore we can use this mathematical model that wasn't developed in any way for this purpose to figure out who wrote what book, okay? Totally cool. All right.
       Another big reason, models really make us humble. The reason why they make us humble is that We just have to lay out sort of all the logic and then we realize holy cow. I had no idea that this was going to happen, right. So often when we construct the model, we're going to get very different predictions than what we thought before, right. So if you look at things, here's a picture of a, the tulip graph, right, from When there's a big spike in tulip prices. You can imagine that people thought that prices were gonna continue to go up and up and up. Well, if you had a simple linear model, you might have invested heavily in tulips, and lost a lot of money. So, one reason that models make us humble is, never go back to the George Box code. All models are wrong, right? So, a model is going to be wrong. But the models are humbling to us, because they sort of make us see the full dimensionality广延 of a problem.
       So, once we try and write down a model of any sort of system, it's a very humbling exercise, because we realize how much we've gotta leave out to try and understand what's going on. Here's another example, right? This is the Case-Shiller Home Price Index, and what you see is, you see prices going up and up and up, right? And then you see this, let me put a pin up here, precipitous crash right here, right? A lot of people had models that just said, look, things are gonna continue this way. There were a few people that had models that said things go down. These people, the ones whose models went down, they made a lot of money. These people thought it was gonna go up, didn't. So, we're always gonna see a lot of diversity in models, and you're really not gonna know, often until after the fact, which one is right. And so, one thing that's gonna be really important is to have many models. So, let's go back to that fox-hedgehog graphite that we, I showed you before. The foxes, the people with lots of models, did much better than the hedgehogs, the people with no models. And former models did better than The foxes. Well, what would do better than formal models? Well, people with lots of formal models. Right? So if we really want to make sense of the world what we want to do is have lots of formal models in our disclosures. So what we're going to do in this class is almost like, remember the old, like, sixteen, 32 box of Crayolas? That's sort of what we're doing here.Right? We're just going to pick up a whole bunch of models. And we're going to have them, right, they're fertile. We're going to plot across a bunch of settings. So when we're confronted with something what we can do is pull out our models. Ask which ones are appropriate, and in doing so, right, be better at what we do. So the essence of Tetlocks's book is where that graph came from with the foxes and hedgehogs is that, the only people who are really even better than what he. He has a way of classifying what a random choice would be. The only people who are better than random at predicting what gonna happen are people who use multiple models. And that's the kind of people that we wanna be. Okay, so thats, sort of the big, intelligent citizen of the world logic, right. There is, models, are incredibly fertile, they make us humble,they help, you know really clarify the logic, and they're just better. Okay? So if you wanna be out there, you know, helping to change the world in useful ways, it's really, really helpful to have some understanding of models. Thank you very much.