Intelligent Citizens of the World

Hi, welcome back. In this lecture, 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? So. In a, in a sense,

right? 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 ad learners, Robert Hutchins

President of the University of Chicago. They had
this thing that they wrote called the

Synopticon which was a list, right, 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. And this comes from the Odyssey, you
know, this says the ship is going past the

sirens 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 tie.
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, you know,

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, right, so his men won't

you know 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 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,

they, you know, have, you know, 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's 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 are 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, where they may put all the
adjectives. At the end of the sentence

that looks very different than let's say
English. All right. Even the law. This is

a graph from one of my graduate students,
former graduate student. Now, he's a law

professor, Dan Katz. Where he's got sort
of a network model of which Supreme Court

justices, you know, who they appoint, so
who, 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. Behavior. It's the
study of strategic interactions between,

you know, 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, when you go

to, you know, college, you go to college,
you'll find that there's game theory

models of just about anything. Right? So
it's actually a field based entirely just

on models. Right? Why, right? [laugh] Why
all these models, right? Why does

everything from linguistics, to economics
to, you know, 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 book. And in this graph, he, what
he's showing is, he's showing the accuracy

of, some different, let me pull up a pin
here. 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 is it, in terms
of how particular, how fine of predictions

is it 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 discriminatoriness,
discrimination, and this axis is how

accurate. So, what you see here, down
here, are hedgehogs. 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. 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. So

different authors, right, use different
sequences of words. Different 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 they make us humble is

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, in the six-,
seventeenth century, when there's a, you

know, this 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 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. All right.
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

an 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 lotta 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, 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, right? That's

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's

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.