# 程序代写代做代考 decision tree Bayesian Decision Trees

Decision Trees

Mark Broom

August 19, 2016

These notes relate to the four equivalent chapters in “Decision Analysis

for Management Judgment” by Goodwin and Wright.

1

1 Introduction

Complex decisions

Management decisions are often complex because they involve:

1. Risk and uncertainty: you may have to choose between various

options, with various levels of risk. Stay in your current job, move to

a new job with a different company, set up your own business.

2. Multiple objectives: you may want to maximise your immediate or

long term earning potential, increase the amount of control you have

over your work, increase the amount of time you can spend with your

family.

3. A complex structure: if you decide to set up your own business,

there are many steps to take and it can be done in a number of ways.

It would be hard to summarise everything simply and clearly.

4. Multiple stakeholders: what is best for you may or may not be best

for your partner and/ or your children, for example.

People have problems coping with this complexity. The human mind

has limited information processing capacity and memory, and it is

difficult to take all factors into account. To cope with this complexity

we naturally tend to simplify problems, so that we can find a solution.

This can lead to inconsistency and biases if not done in a scientific

way.

2

The Role of Decision Analysis

How can Decision Analysis help us? The key lies in the word “analy-

sis”, which refers to breaking something down into constituent parts.

By doing this, we can solve simpler problems as components of a more

complex whole.

When analysing a problem, you will need to make assumptions about

the real problem which might be greatly simplifying. This can raise

consciousness about key issues, and also potential dangers due to un-

certainty.

The process of analysing a problem will also generate a clear rationale

for a decision. Even if some of the assumptions you have to make can

be questioned, the reasoning for the decision is clear.

This process may also make clear that there is important information

that you lack to make an effective decision. This helps set priorities on

what information needs to be obtained (and also what is not needed).

Note that the main purpose of decision analysis is to yield insights

and understanding about the decision problem rather than to impose

an optimal solution.

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Rationality

The basic assumption underlying Decision Analysis is that of ratio-

nality. In any analysis we will be able to quantify the outcomes in a

way to say some are more and less preferable, and it is assumed that

we should always choose the “best”.

Our assumptions may lead us to a single optimal solution. If the deci-

sion maker fully accepts the assumptions of the model as (effectively)

true, then the decision indicated by the analysis is the one that should

be implemented.

Often, assumptions will be more questionable. What if there is a con-

flict between the results of the analysis and the intuition of a decision

maker?

It may be that the analysis failed to capture some important aspect

of problem, for example inadequately accounting for risk or potential

reputational damage.

Alternatively, perhaps intuitive preferences were only partly formed or

were inconsistent, or were based on past experience which no longer

applies to changed circumstances.

Exploring this conflict can lead to deeper insights and understand-

ing about the decision problem. This may lead to a new and more

sophisticated analysis.

4

Applications of Decision Analysis

Decision Analysis has a wide range of applications. Anywhere where

there are complex decisions to be made, a logical analysis using such

methods is an important component in coming to a sound judgement.

An interesting set of examples are given in Goodwin and Wright.

These include:

1) Strategic decision making at the Du Pont chemical company

2) Strucural decision problems in response to the Chernobyl disaster

3) Selecting research and development projects in pharmaceutical com-

panies

4) Systems acquisition for the US military

5) Supporting top level political decisions in Finland

6) Automating advice giving in building societies

7) Optimal fund allocation in a shampoo company

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2 Decision-making under uncertainty

The maximin criterion

Suppose that a food manufacturer must choose the number of batches

of a perishable product to make each day.

Each batch produced costs $800 to make, and yields a revenue of $1000

if it is sold. If it is not sold, it has to be thrown away.

Assume that the demand on any given day may be one or two batches,

and so either one or two should be produced. This is summarised in

the following table, including the profit made under each outcome.

Action/Demand (no. of batches) 1 2

Produce one batch $200 $200

Produce two batches -$600 $400

The Maximin criterion says to find the worst outcome for each choice

you might make, and then pick the largest profit in this case. It

assumes the worst case scenario.

The small number in row 1 is 200 which is larger that the smallest in

row 2, -600. Thus we choose the option associated with row 1, i.e. to

produce one batch.

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The Expected Monetary Value (EMV) criterion

In the previous example, we had no measure of how likely the two

possible levels of demand were. Suppose we have a good idea of how

likely each is, through past experience.

Suppose that the probability of the demand being one batch is 0.3, and

so the probabilities of it being two batches is 0.7. This is summarised

as follows:

Action/Demand (no. of batches) 1 (prob 0.3) 2 (prob 0.7)

Produce 1 batch $200 $200

Produce 2 batches -$600 $400

We can calculating expected profits as follows:

Produce one batch:

Expected daily profit = (0.3× $200) + (0.7× $200) = $200.

Produce two batches:

Expected daily profit = (0.3×−$600) + (0.7× $400) = $100.

Thus the optimal choice is to produce one batch.

7

Sensitivity analysis: the above decision relies on the probabilities being

as we assumed. What if they are different?

We shall draw a simple figure (see associated slides from Goodwin and

Wright Chapter 1) to illustrate this.

Sensitivity analysis and Limitations of the EMV criterion

Often a problem is not simply to maximise expected income. For

instance you may not take an option that has maximum expected

return if it had a small change of catastrophe.

For example, should you insure your house against structural damage,

fire, flood etc. The insurance company that you deal with will be

hoping to make a profit from a policy it sells you. If it is on average

making a profit, you are making a loss.

It makes sense for you to take out this type of insurance (but consider

the above logic for other types of insurance), because if you do not

and your house burns down you will be ruined. The damage done by

a big loss is more than the benefit from an equivalent large gain.

The amount of money involved is still small for the insurance company,

so they can disregard this, and so think in terms of expected reward

and sell you the policy.

The key thing to consider is your attitude to risk.

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Utility functions

The attitude to risk of a decision maker can be incorporated into a

utility function. For each outcome there is a single value allocated to

it.

The simplest case is if our outcomes can be expressed in terms of

monetary values. We will use utility functions if we are not simply

interested in expected value, for instance if a large loss would be dis-

astrous.

We consider the following conference organising problem.

A conference organiser can choose between two venues, the Luxuria

Hotel, and the Maxima Centre.

If there is high attendance then the expected profit at the Luxuria

Hotel is $30000, if low attendance $11000. The equivalent figures for

the Maxima Centre are $60000 for high attendance, and an $10000

loss for low attendance.

If the Luxuria Hotel is chosen, the probability of high attendance is

0.6, if the Maxima Centre, then this is 0.5.

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We can calculating expected profits as follows:

Choose Luxuria Hotel:

Expected profit = (0.6× $30000) + (0.4× $11000) = $22400.

Choose Maxima Centre:

Expected profit = (0.5× $60000) + (0.5×−$10000) = $25000.

Thus the Maxima Centre is best if using the Expected Monetary Value

criterion.

A utility function typically allocates a value 1 to the largest possible

outcome, and 0 to the smallest. In this case the largest is $60000 and

the smallest −$10000. What is the value of $30000?

We ask our organiser, whether she prefers a guaranteed $30000 or

$60000 with a probability p, and $ − 10000 with probability 1 − p.

She will have her preferences for various p (e.g. p = 0.6 would give a

slightly higher expected value for the lottery, but the consequences of

the loss might mean she still prefers the safe $30000).

When we find a p which she is indifferent between the choices, that is

the utility of $30000.

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Suppose that she chooses the following utilities:

Money Utility

$60000 1.0

$30000 0.85

$11000 0.60

−$10000 0.0

We can find the expected utilities as follows:

Choose Luxuria Hotel:

Expected profit = (0.6× 0.85) + (0.4× 0.6) = 0.75.

Choose Maxima Centre:

Expected profit = (0.5× 1.0) + (0.5× 0.0) = 0.5.

Thus the Luxuria Hotel is best if using this utility function.

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Interpreting utility functions

The utility function described in the previous example is “risk-averse”.

If you plot the utilities on the y-axis of a graph against monetary value

on the x-axis, you will get a slope that is decreasing as the monetary

value increases.

Our organiser’s utility function meant that she was neutral between a

guaranteed $30000 or $60000 with a probability 0.85, and $ − 10000

with probability 0.15.

Similarly she was neutral between a guaranteed $11000 or $60000 with

a probability 0.6, and −$10000 with probability 0.4.

Most people are “risk-averse” like this.

A “risk-neutral” utility function would just be a straight line. Someone

using the Expected Monetary Value criterion is effectively using a risk-

neutral utility function. Any “risk-averse” function will lie above this

straight line.

A “risk-prone” utility function has a continuously increasing slope,

and will lie below the risk-neutral line.

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Utility functions for non-monetary attributes

Sometimes we face a problem where the outcomes are not described in

monetary terms. We can still use the idea of utility to decide between

options in this case, as long as it is clear what is a good outcome and

what is a bad outcome.

For instance if we wish to develop a new product, and the outcome is

the time it takes to develop it, then a short time is good and a long

time is bad.

We can use the same lottery argument as before. When given a number

of options (e.g. 1 year, 2 years or 8 years) we allocate utility 1.0 to

the best option (1 year) and 0.0 to the worst option (8 years).

To find the utility of values between these two (e.g. 2 years), we

consider obtaining that outcome with certainty against a lottery of the

best outcome with probability p and the worst one with probability

1− p.

The value of p where we would be indifferent between the two options

is the utility of the imbetween value that we are considering.

For example if this value is 0.95 in the above case, we are saying that

u(2 years)=0.95 u(1 year)+0.05 u(8 years)=0.95(1.0)+0.05(0.0)=0.95.

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Allais’s paradox

We shall consider the following example, which illustrates that people

do not always operate according to a logical idea of utility.

Consider the following decision. You can choose one of the following:

Option A: $1000000 with probability 1.

Option B: $1000000 with probability 0.89, $5000000 with probability

0.1 and $0 with probability 0.01.

Most people choose option A.

Now consider the alternative decision. You can choose one of the

following:

Option X: $5000000 with probability 0.1, $0 with probability 0.9.

Option Y: $1000000 with probability 0.11, $0 with probability 0.89.

Most people choose option X.

Are these choices logically consistent?

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Setting this up in terms of utility theory, we have three ordered sums

of money. $5000000 is the largest and so must have utility 1.0, $0 is

the smallest and so has utility 0.0. What could the utility of $1000000,

which we shall denote by u(1M), be?

Option A has value u(1M).

Option B has value 0.89u(1M) + 0.1× 1.0 + 0.01× 0.0

= 0.1 + 0.89(u(1M).

Option A was preferred to option B, so that

u(1M) > 0.1 + 0.89u(1M)⇒ u(1M) > 0.1/0.11 = 10/11.

Option X has value 0.1+0=0.1.

Option Y has value 0.11 u(1M)+0.

Option X was preferred to option Y, so that

0.1 > 0.11u(1M)⇒ u(1M) < 10/11.
Thus we have a contradiction.
This does not invalidate utility theory. Rather it shows that sometimes
the choices that people make are perhaps not strictly logical. Never-
theless, these choices are consistently made, and this issue should be
taken into account.
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Multi-attribute Utility
Often we have more that one important attribute to try to optimise,
e.g. cost and time.
Often making improvements in one comes at a cost to the other. For
example we can speed up work by paying overtime to the workforce
or hiring extra workers at some financial cost.
We thus need to decide in some way the relative importance of the
different attributes.
Suppose that a project manager has a project to complete with time
and cost to consider. Ideally he wants the project completed in 12
weeks, which was the timeframe specified by the customer.
Completing the project on time gains customer goodwill. He can
improve the chance of doing this by hiring extra workers and running
24 hour shifts.
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Assume that there are two options only:
1) Work normally, in which case with probability 0.1 the project is
completed on time at a cost of $50000, with probability 0.6 it is 3
weeks overdue at cost $60000 and with probability 0.3 it is 6 weeks
overdue at cost $80000.
2) Hire extra labour, in which case with probability 0.8 the project is
completed on time at a cost of $120000, and with probability 0.2 it is
1 week late with a cost of $140000.
How do we choose the best option?
Utility independence
Attribute A is utility independent of attribute B, if the decision makers
preferences for gambles involving different levels of A, but the same
level of B, do not depend on the level of attribute B.
This means that when comparing utilities we will be able to add sep-
arate utility functions for the different attributes; otherwise it is more
complicated.
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Utility functions for overrun time and project cost
We shall consider the following multi-attribute utility function
u(x1, x2) = k1u(x1) + k2u(x2) + k3u(x1)u(x2),
where k3 = 1− k1 − k2.
First we find a single attribute utility function for each measure in
turn, assuming independence.
For the overrun time, no overrun is best with utility of 1.0, and a 6
week overrun worst, with utility 0.0. If the project manager is indif-
ferent between a certain overrun of 3 weeks, and a gamble offering
no overrun with probability 0.6 and a 6 week overrun of 0.4, we get
u(3 weeks)=0.6. Similarly, we assume that the manager finds u(1
week)=0.9.
We repeat the process with the cost. Suppose that we arrive at the
following table for the project managers utilities for overrun and cost
Overrun Cost of
(weeks) Utility project ($) Utility
0 1.0 50 000 1.00
1 0.9 60 000 0.96
3 0.6 80 000 0.90
6 0.0 120 000 0.55
140 000 0.00
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We need now to find the values of the kis.
Suppose that we consider either an outcome with the best overrun time
(0 weeks) and the worst cost ($140000) against a lottery, between the
best cost and overrun times (0 weeks and $ 50000) with probability k1
and the worst of both (6 weeks and $140000) with probability 1− k1.
The bigger the value of k1, the more important that we consider the
overrun time. Suppose that k1 = 0.8.
We can find k2 similarly, by changing the certain outcome to the worst
overrun time but the best cost. Again, the larger k2, the more impor-
tant we think the cost is. Suppose that k2 = 0.6.
This means that relatively speaking, the project manager believes that
the overrun time is the more important factor.
This finally gives us that k3 = 1− k1 − k2 = −0.4.
We can now find the utilities for any combination of overrun time and
cost. For example
u(3 weeks, $ 60000)=0.8× 0.6 + 0.6× 0.96− 0.4× 0.6× 0.96 = 0.8256.
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3 Decision trees
Constructing a decision tree
The initial node in a decision tree is the first decision of the decision-
maker. It is represented by a square box. All nodes which represent
a decision point are represented by such a square.
Leading off from this node are several branches, each representing a
possible decision.
Typically, before the decision-maker has the chance to make a further
decision, chance will determine which of various different scenarios
that they face. Thus at the end of the first branches there will be
a vertex which represents a chance occurence (due to actions of the
environment, the market etc.). Such a vertex is represented by a circle.
All nodes representing a chance event are represented by such a circle.
Leading off from such a node are several branches, each represented a
possible occurence, accompanied by the probability that it occurs.
At the end of each of these branches, there might be an outcome
(which specifies the reward to the decision-maker) or another square
box, representing another decision to be made.
It is possible that there might be more than one square box or more
than one circle in succession, but generally these can be consolidated,
so we only consider alternating paths of squares and circles.
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Finding the optimal policy
A policy is a a plan of action which specifies the decision at all square
nodes that can be reached under the policy.
To find the optimal policy we work back from the end nodes of the
tree. These end nodes, or terminal nodes, will have a value which may
be a monetary value.
If the previous node on a branch is a circle, then its value is found
by summing the products of the values of subsequent nodes and the
probabilities of their occurence.
If the previous node is a square, the value here is just the largest of
the values of all of the subsequent nodes, since the decision-maker will
choose the one giving the largest value.
We shall consider an example to illustrate this (see the food processor
example in the Decision trees slides).
If the values in our problem are not monetary ones, but utilities, there
is absolutely no difference to how the process is carried out.
We shall consider the same example where monetary values are re-
placed by utilities.
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Decision trees for continuous probability distributions
In the decision tree problems that we have seen so far, there have only
been a limited number of outcomes for any course of action, e.g. two
outcomes, high or low sales.
However, for some problems there may be an effectively infinite num-
ber of possibilities. Actual sales could be any value up to many thou-
sands or more. Thus we effectively have a continuous probability
distribution.
One possibility is to approximate this continuous distribution with a
discrete distribution, including only a small number of values. These
could be a high, medium and low value.
The high value could be that which has only a 5% chance of being
exceeded, the medium one that which has a 50% chance of being
exceeded, and the low one that which has a 95% chance of being
exceeded. This is the extended Pearson-Tukey approximation.
Probabilities must be allocated to the high, medium and low values.
A natural choice would be 0.1, 0.8 and 0.1 respectively. However this
significantly underestimates the variance. The actual probabilities
often used are 0.185, 0.63 and 0.185 respectively.
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Influence diagrams
Influence diagrams are a simple way of representing the interactions
between decisions and events due to outside factors/ chance.
Just as in decision trees, there are boxes and branches between the
boxes, where a square box represents a decision and a circle represents
an event.
There are four possible sequences of two boxes. The first and the
second can each be a circle or a square.
Two squares means a sequence of two decisions, where the first decision
is known before the second is made.
Two circles mean two chance events that follow each other, e.g. the
weather can influence the number of customers at a shop.
The other two orderings have either a decision made following a chance
event,
or chance affecting the outcome of a decision, after the decision has
been made.
Sometimes a decison-maker will construct an influence diagram to
understand how key aspects interact in a conceptual way, before then
using it to construct a decision tree.
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4 Revising judgements in the light of new information
Bayesian methods
Suppose that we believe that there is a probability of 0.3 that a virus
is present in the soil in a farm, and so a probability of 0.7 that it is
absent. This is our prior probability.
A laboratory has a test for the presence of the virus.
If the soil is infected, the test has a probability of 0.9 of indicating
that the virus is present.
If the soil is not infected, the test has a probability of 0.2 of indicating
that the virus is present.
This provides new information to update our probability estimates.
There are thus four posibilities with the following probabilities:
Virus is present and the test says it is present: 0.3× 0.9 = 0.27
Virus is present and the test says it is absent: 0.3× 0.1 = 0.03
Virus is absent and the test says it is present: 0.7× 0.2 = 0.14
Virus is absent and the test says it is absent: 0.7× 0.8 = 0.56
Bayes’ theorem says that the conditional probability of A given B,
P (A|B), satisfies
P (A|B) =
P (AandB)
P (B)
.
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Let A denote the event that the virus is present, and B denote the
event that the test indicates that the virus is present.
We further denote Ac as the complement of A, i.e. the event that the
virus is not present. Similarly we denote Bc as the event when the
test indicates that the virus is not present.
Suppose that the test indicates that the virus is present. The updated
probability of the virus being present is then P (A|B), where P(A and
B)=0.27 and P(B)=P(A and B)+P(Ac and B)=0.27+0.14=0.41.
Thus P (A|B)=0.27/(0.27+0.14)=0.66. This is the posterior probabil-
ity.
Similarly, if the test said that the virus was not present, we would
need to find P (A|Bc). This leads to the posterior probability of the
virus being present being 0.03/(0.03+0.56)=0.05.
Thus for a real problem we might be faced with the possibility of
acquiring more information at some cost (e.g. buying the test in the
above). This would lead to the figure in the accompanying slides.
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The value of new information
In the above we have considered the possibility of buying new informa-
tion that might help us make a decision. How much is that information
potentially worth?
Suppose that the profit from the crop would be $90000 if the virus
was not present, but there would be a loss of $20000 if it was present.
Further suppose that if the farmer decided not to plant the crop, then
a profit of $30000 could be made.
If there was no test available, the choice would be between
A - plant crops with expected return 0.7× $90000 + 0.3×−$20000 =
$57000.
B - plant alternative crop with return $30000.
Here option A is clearly the best.
You want to plant the crop if and only if there is no virus present. If
there was a perfectly accurate test avaliable then the return (without
the cost of the test) would be
C - 0.7× $90000 + 0.3× $30000 = $72000
Buying this test would increase profits by $72000− $57000 = $15000.
Thus you would be willing to pay up to this amount to have this test
available.
26
Suppose we only have the imperfect test previously described. Assum-
ing that this test is accurate enough that if it indicated the virus was
present you would not plant the crop, and if it indicated it was absent
you would plant it (and this assumption should be properly checked
using the decision tree, but the test would be useless if this was not
the case) then the following gives us the value of the test.
Recall that the probability that the test indicates the virus is present
is 0.41, and so it is 0.59 that it indicates it is absent.
Recall also that if the test says the virus is absent, then it is actually
present with probability ≈ 0.05 (actually 0.0508 to 4dp).
If the test says that the virus is present, then we obtain
0.9482× $90000 + 0.0508×−$20000 = $84411.
This means that the expected profit with the imperfect information
from the test is
D - 0.41× $30000 + 0.59× $84411 = $62102 ≈ $62100.
The expected value of the imperfect information (EVII) is thus
$62100− $57000 = $5100.
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