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RICHARD DE NEUFVILLE:
Decision analysis

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provide a structured,
efficient way

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to help people recognize
this complexity,

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to recognize the probabilities,
and to deal with it.

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So what are the general
features of this method?

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First of all, it offers a simple
way of defining the choices.

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Indeed, it assumes
that everything

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is a discrete choice
that is either yes, no,

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or yes, somewhat, a
lot, maybe, not at all.

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But it has discrete--

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it forces you to think in
terms of discrete choices, not

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a continuum.

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It also enables you, but
generally compels you,

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to look over several periods.

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That is, it's not just what
you do in the first period,

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but then what you do.

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Just like going to
a course at MIT,

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you have your choice of
first semester courses.

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And the choices you make then
enable you to do certain things

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or prevent you from doing other
things because you didn't take

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the prerequisite course and so
forth so that you want to think

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about it over several periods.

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The third element, which is
key to the whole reason we

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even discussing it, is because
it deals with uncertainties.

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And it is also a
standardized method.

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So there are tools
available to do this,

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to do decision analysis.

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And so if you are into
decision analysis,

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there are a lot of other
people who do it the same way,

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they do it in a standard way.

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So it's a structured process.

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It's like Excel.

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It provides a
widespread language

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to think about certain things.

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And finally, which I
think is relevant here,

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is that it can include not just
a numerical result in terms

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of the income or
the present value,

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but also can deal with risk
aversions, your consumer

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satisfaction, some kind
of notion of utility.

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We'll talk more about
that in a later session

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in the half semester course.

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But for the moment, it allows
you to say, well, yeah.

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We could have on average
our outcome is good,

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but the bad outcome is--

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I really don't like it at all.

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If, for example, if I'm
saving for my pension,

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I don't just think on average
I might have $1 million

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to retire on, but it makes a
great deal of difference for me

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if that million is--

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for an extreme, it's either
$2 million or nothing.

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That would be an
average of a million.

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But if I had nothing, I'd
be feeling very unhappy

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about my retirement and
so that I would weight

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against that possibility.

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So it allows you to think
about utility assessment.

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Now, all right.

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The central element of
the decision analysis

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is the decision tree.

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It shows the choices
over several period.

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And it has basically four
outcomes, four elements.

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It has the choices,
the structure, the way

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you draw it--

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it has the choices and
the possible outcomes.

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I'll show you those in a moment.

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And secondly, it
has data, the data

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both about uncertainties
that may occur

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and the value of the outcomes.

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So four elements-- choices of
the structure and the data.

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So here is the structure.

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So basically, it's a disciplined
way of present alternatives.

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And it has two elements.

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It has the decision points.

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So over on the left-hand
side here, I'm starting,

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and I have a choice to make.

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And in this, I'm going to
have a very simple case just

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to illustrate it.

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I can either do choice
one or choice two.

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I have these two branches.

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Of course, I could have
five or six, but or--

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I have a point.

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Then, having done something,
say I decided to take--

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whatever, I decide to
build a plant somewhere,

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then I have a result, a chance
outcome of it performed well,

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not so well, it was a
disaster, whatever it might be.

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I have possible outcomes.

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And for each of the choices,
typically known as a branch,

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you will have a
possible outcomes.

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And they will lead you to
another place where you say,

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all right, I build this plant.

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It was a success.

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Now, do I build up
some more or stay put?

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Or I build this plant,
it was a failure.

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I can now decide to sell it
or renovate it, whatever.

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So we got sequences-- decision,
chance, decision, chance

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is the basic structure of this.

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Now, the data, then, has--

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all right.

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So I have elements
about the outcomes.

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So given all this,
I've made a decision.

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I observe what happened.

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I made another decision.

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I observe what happens.

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And I have an outcome
one, outcome two.

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And in this
particular case where

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I have two choices, two
outcomes, at each stage,

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I have, in this very
simple two-stage process,

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I have 16 outcomes.

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Now, you might ask, well,
where is the decision tree?

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Well, basically,
you've got to think

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that this tree is
lying on its side

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and that this is the root
here, and the branches go

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up, and leaves and so forth.

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And what I invite you to
consider, first of all here,

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is when does this decision
tree become a messy bush?

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Because realize that I've
only had two choices,

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two decisions here.

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I could have had five.

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And I could have five outcomes.

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And now, I'm going to have 25.

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And I repeat this, and I'm
going to have 25 squared.

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And I have 600-some
outcomes, et cetera.

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It can really easily
get very complicated

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when I haven't looked at a
particularly difficult problem.

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So for example, if I were trying
to represent a game of chess

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here and I could move
basically something like eight,

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10 moves at the
beginning with my pawns,

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and my castle, and my rooks, I
would have 10 possible choices

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or 12 possible choices
here, and then I

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could have what
my opponent does.

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And a realistic
problem can explode on.

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So one of the issues here,
again, back to my emphasis

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at the beginning or my
remark at the beginning

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is that decision analysis
is good for simple problems.

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For complicated problems,
it just gets too messy.

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