Hello, in this set of lectures, what we're gonna do is we're going to look at some
decision models. So these are models of how people do make decisions and how they
should make decisions and we're gonna do a bunch of types. We're gonna do multiple
criterion making decisions models, we'll do some spatial models and we'll do some
decision theory under uncertainty. Okay, so that's sort of the outline. Now when we
think about decision theory models, we're really doing these things for two reasons.
First is normatively. Remember how we talked about how models can make us be
better thinkers? Well, these particular models are going to help us make better
choices. Remember Talk about how they can serve as a crutch. They can help us where
we're sort of, because of our sort of limited ability to hold information,
models can help us. That's clearly gonna be the case here. So, normatively these
models will be useful because they'll help us make better choices. Also, There's a
positive dimension. So, social scientists use these sort of models a lot to try and
predict the choices that people make. Whether they're policy makers or business
people, right, or even entire governments, you can use these sort of models to figure
out, you know, why did some actor make the choice that he or she made? Okay, so
that's sort of what this is all about and you can bring these models to date as
we'll see as well. So, what do I mean by normatively using [inaudible]? Well think
about it, right? So you've got all these choices to make: whether to go to school,
what investments to make, what job to take, even whether you should like drive
or fly somewhere, right? So a whole bunch of choices you have to make and these can
be difficult. There gonna be lots of dimensions to them and they can be made
under uncertainty. So these models will help you make Better choices, which how
should you buy? Should you have your wedding inside or outside, right? All
these sets of choices, you can use these models to help you make better ones. Now
positively, right, I was talking about that, you can help predict what's going,
again, you've got some politician who makes a policy choice. Maybe they make a
nomination to the Supreme Court. You want to understand why did they choose that
particular person. A candidate chooses a platform. You can use these models to
understand, why did they choose that platform? Business makes an investment,
these models will explain, you know, why they made the investment or maybe tell you
something about what they perceive the value of that investment to be. So, these
models right can be used in two ways: one, to help you make choices and two, to
understand the choices of others. Now we're gonna do two broad classes of
models. The first are multi-criterion choice models. So that's where there's
lots of dimensions and you're trying to weigh one alternative versus another. The
second type are going to be probabilistic where there's some uncertainty out there
in the world, you're not sure how it's going to unfold and so what you have to do
is decide, okay, how do I balance out the risks versus the reward. Let me explain
these a little bit and then we'll get started. So suppose you're buying a car.
You could be looking at the new Ford Fusion, which is a great car, or the Chevy
Volt. Also, a great car. And you've got to think about, okay, how do I, measure these
to, how do I decide which of these two that I want to buy. Right? Well, one way
you could do it is you could say, okay, here's a bunch of criteria. Maybe there's,
you know, how comfortable the seats are. And so Ford wins on that one and maybe
this one is miles per gallon and maybe the Chevy gets better miles per gallon. So
what you can do is you have all these criteria and you could choose the car
based on these criteria. Now alternatively you could have a spatial model. Now in a
spatial model you could have a, you could have an idea point. So there could
[inaudible] be a couple dimensions, one of them might be, sort of, how fast the car
goes, speed of the car. And another might be, how comfortable it is. [sound], so
let's get straight. Comfort. Here, [sound]. So I could want cut it so sort of
moderately comfortable. Maybe I don't want be too comfortable because I sort of fall
asleep in the car. And maybe I don't want it to be too fast because my sons are
going to be driving soon and I wanna car that isn't quite as speedy. So this is my
ideal point right here and I can think about how far are these cars from my ideal
point? So this is distance one and distance two and I buy the car that's
closer to my ideal point. So that's another way we can think about making
decisions. Spatially, in terms of distance between you know how close one product or
one policy is closest to my ideal preferences. Okay? Those sort of choices
though are you know, under certainty, if we know all the attributes of the car.
Often times we have to make choices under uncertainty. Where we don't know whether,
you know, how the fu-, what the future's gonna hold. So for example, I teach at the
University of Michigan. Great school, a lot of people want to go there. But people
always ask me, should my son or daughter apply to Michigan/ And you can think about
it as a choice under uncertainty. Right, cuz you can apply or you can not apply.
But if you apply then there's some probability P that you'll get accepted and
some probability one minus P that you won't get accepted. So this decision, do I
apply. Or do I don't apply, not apply, comes down to really what you think the
value of P is and also, how nice it would be to be accepted. How much do you really
want to go there. So you can write down on these decision trees and decide, is it
worth it? Does it make sense to fill out that application and pay the application
fee? In addition to doing that, once you've got these decision trees we're
gonna show how you can use them. Remember one reason we write models is because they
let us do other stuff. Once you've written down those decision trees we can actually
compute something called the value of information. What the value of information
tells us is if the uncertainty went away. So someone could tell us whether you're
gonna get into Michigan or tell us if it's gonna rain on our wedding day. Then we can
figure out exactly how much that information will be worth. So it's a
little trick we can do once we've written down our decision tree which is a pretty
cool thing. So that's the outline. Your [inaudible] multi criterion decision
making, spatial models into a quick aside on probability and then we'll move onto
decision theory and the value of information. Let's get started, thanks.