Quantitative business analysis by i lindner and j r van den brink

Quantitative Business Analysis
(QBA)
I. Lindner and J.R. van den Brink


Q tit ti Business
Quantitative
B i
Analysis
A l i
Dates lectures:

October 31, November 7, 14, 21, 28,
December 5
Time: 11:00-12:45,
Ti
11 00 12 45
Location: WN-KC 137

Quantitative Methods (QBA)

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Q tit ti Business
Quantitative
B i
Analysis
A l i
• T
Tutorials:

torials: Tuesdays
T esda s and Wednesdays
Wednesda s
• Prepare exercises beforehand!
• Program for the first two weeks:
Exercises from chapter “Topic E1 –
Exercises Decision Analysis”:
week 1:
1.1, 1.2, 1.3, 1.5, 1.8
week 2:
1.4, 1.6, 1.7, 1.9
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Quantitative Business Analysis
Contents of the Course

Week 1-2 (Ines Lindner)
1 Decision Analysis using Decision Trees
1.
Weekk 3-6
W
3 6 (René
(R é van den
d Brink)
B i k)
2. Strategic Thinking - Noncooperative Games

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Quantitative Business Analysis
• Book “Quantitative Business Analyses”
by C. van Montfort and J.R. van den Brink:
chapter T1,
T1 T2,
T2 E1 and E2;
• Book is available at Aureus
• Sheets of lectures (on Blackboard)
• Relevant sections for decision theory:
8.1-8.3,, 8.5 (except
(
p “Usingg Excel...”),
), 8.6,, 8.88.10
• We don’t discuss software applications in this
course!
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Quantitative Business Analysis

Two efficient strategies to pass
the exam!

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Quantitative Business Analysis
Facts:
• You have to read the text material in
order to pass the exam.
• Rule of thumb: A lecture is only fun if
you already know 50 percent.
Conclusion: Read text material before
lecture and take lecture as revision.
revision
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Quantitative Business Analysis
Facts:
• It is very tempting to just sit passively in
th tutorials
the
t t i l andd watch
t h discussion
di
i off
exercises.
• Problem: Passive understanding is not

enough for exam.
Conclusion: Try to do the exercises yourself
and take tutorial as a feedback on your
performance.
8


Quantitative Business Analysis
→ Answers to exercises decision theory will
be available at the end of week 2.
2

9


Decision Theory
Central question: What is the best decision to take?
Assumptions:
• We have some information.
• We are able to compute with perfect accuracy.
• We are fully rational.

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What kinds of decisions need a
theory?
Optimization – examples

• How can we pproduce at lowest costs?
• What is the best product mix?
p
wayy to spend
p
myy moneyy
• What is an optimal
(intertemporal choice)?

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What kinds of decisions need a
theory?
Choice under risk – examples
y
• Should I pplayy the lottery?
• What kind of insurrence should I buy?
y
• How should I invest myy money?

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What kinds of decisions need a
theory?

Interacting decision makers (game theory) –
examples
• The telephone conversation broke down – shall I
wait or call back myself (depends on what other
person does)?
d )
• As a firm, shall we enter a new market (depends
on competing
ti firms)?
fi
)?
• What is the best strategy to get promoted
(d
(depends
d on your bboss)?
)?
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What kinds of decisions need a
theory?
Game theory:
y
• Additional difficulty: the need to take into
account how other people in the situation will act.
• Presence of several “players” (strategically acting
agents).
• Requires strategic analysis (week 3-6).

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Decision Theory
Central question: What is the best decision to take?
• Absence of strategic considerations.
• Can be seen as a one-player game.

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Decision Analysis
using Decision Trees
Dilemma: organize party indoors or in
ggarden? What if it rains?
Events and Results

Choices

Rain

Sunshine

In Garden

Disaster

Real comfort

Indoors

Mild discomfort Regrets
but content

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Decision Analysis
using Decision Trees
Dilemma: organize party indoors or in
ggarden? What if it rains?
Events and Results

Choices

Rain

Sunshine

In Garden

Disaster

Real comfort

Indoors

Mild discomfort Regrets
but content

Quantitative Methods (QBA)

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Decision Analysis
using Decision Trees
Dilemma: organize party indoors or in
ggarden? What if it rains?
Events and Results

Choices

Rain

Sunshine

In Garden

Disaster

Real comfort

Indoors

Mild discomfort Regrets
but content

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Decision Tree Components
Decision 1

Decision node
or Decision fork
f k

Decision 2

Event 1

Event node or
Uncertainty fork
Event 2

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Decision Tree
Rain
Ruined refreshments
Damp Guests
Unhappines

Party Outdoors

No Rain
Very pleasant party
Distinct comfort

Decision
1
Rain

Crowded but dry
Happy
Proper feeling of
g sensible
being

Party Indoors

No Rain
Crowded, hot
Regrets about what
might have been

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Decision Tree
Rain
Ruined refreshments
Damp Guests
Unhappines

Party Outdoors

No Rain
Very pleasant party
Distinct comfort

Decision
1

P ff ?
Payoffs
Rain
Crowded but dry
Happy
Proper feeling of
g sensible
being

Party Indoors

No Rain
Crowded, hot
Regrets about what
might have been

Quantitative Methods (QBA)

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Decision Tree
Rain
Ruined refreshments
Damp Guests
Unhappines

Party Outdoors

No Rain
Very pleasant party
Distinct comfort

Decision
1
Rain

Crowded but dry
Happy
Proper feeling of
g sensible

being

Party Indoors

Highest
Hi
h
payoff

No Rain
Crowded, hot
Regrets about what
might have been

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Decision Tree
Rain
Ruined refreshments
Damp Guests
Unhappines

Party Outdoors

No Rain
Very pleasant party
Distinct comfort

Decision
1
Rain

Crowded but dry
Happy
Proper feeling of
g sensible
being

Party Indoors

Lowest
L
payoff

No Rain
Crowded, hot
Regrets about what
might have been

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Decision Tree
Rain
Ruined refreshments

Damp Guests
Unhappines

Party Outdoors

-100

No Rain
Very pleasant party
Distinct comfort

Decision

100

1
Rain
Crowded but dry
Happy
Proper feeling of
g sensible
being

Party Indoors

50

No Rain
Crowded, hot
Regrets about what

might have been

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-50

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Possible Interpretation
p
of Payoffs:
y
How much is this outcome worth to me?
Rain
Ruined refreshments
Damp Guests
Unhappines

Party Outdoors

-100

No Rain
Very pleasant party
Distinct comfort

Decision

100

1
Rain
Crowded but dry
Happy
Proper feeling of
g sensible
being

Party Indoors

50

No Rain
Crowded, hot
Regrets about what
might have been

Quantitative Methods (QBA)

-50

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