Introduction to Decision Analysis
• Decision analysis studies the process of
making difficult decisions
• The objective is to update, model, and
document the intuition of managers
• A structured approach to decision making
is especially critical in group decision
making
• Key importance in the case of resource
decisions (operations strategy)
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Why are decisions difficult?
• Complexity
– Simply keeping all the issues in mind at
one time is nearly imposible
• Uncertainty
– Making a decision is especially difficult
when you are not sure about one decision
variable’s state
• Multiple objectives
• Different perspectives lead to different
conclusions
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Example
• Winter of 1985:
– The Oregon Department of Agriculture
(ODA) faced an infestation of gypsy moth
in Lane County in Western Oregon
– Forest industry representative call for an
aggressive eradication campaign
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Alternatives
• Use BT, a bacterial insecticide
– target specific
– ecologically safe
– reasonably effective
• Use Orthene, a chemical spray
– registered as acceptable for home garden use
– questions about its ultimate ecological effects
– possible danger to humans
• Possible to use both
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Opinions
• Forestry officials:
– Argue Orthene is more potent and is necessary to
ensure eradication
• Environmentalists:
– Orthene potentially too dangerous
• Others
– Already too late anyway
• Others...
– Still time but decision/implementation has to be
made now
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Subjective Judgments
• In decisional contexts such as the one faced by
ODA
– objective data is lacking
– a procedure determining an « optimal » decision
derived from objective data is of little use...
– personal statements about uncertainty and value
become important inputs (no choice...)
– Discovering and finalizing these judgments is a key
issue in decision analysis
– Instead of criticizing them, we will learn how to
better assess and use them
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The Decision Analysis Process
• Identify the decision situation and understand
objectives
• Identify alternatives
• Decompose and model the problem
– model of problem structure
– model of uncertainty
– model of preferences
•
•
•
•
Choose the best alternative
Sensitivity analysis
Is further analysis needed? yes/no?
Implement the chosen alternative
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Requisite Decision Models
• A model can be considered requisite only
when no new intuitions emerge about the
problem
• or when it contains everything that is
essential for solving the problem
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Elements of a Decision Problem
•
•
•
•
Values and objectives
Decisions to make
Uncertain events
Consequences
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Values and Objectives
• Value: used in its general sense « things
that matter to you »
• Objective: Specific thing that you want to
achieve
• The set of objectives taken up together
make up the values
• Values are the reason for making the
decision in the first place
• They define the decision context
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Objectives for Boeing’s
Supercomputer
Supercomputer
Objectives
Cost
Performance
Five-year costs
Cost of
improved
performance
Speed
Throughput
Memory Size
Disk Size
On-site performance
User
Needs
Installation date
Roll in/Roll out
Ease of Use
Software
compatibility
Mean time
between failures
Operational
Needs
Square footage
Water cooling
Operator tools
Telecommuni-cations
Vendor support
Management
Issue
Vendor Health
US Ownership
Commitment
to supercomputer
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Decision to Make
• Given a decisional context, one (or
several) decision(s) has to be made
• In some cases, several decisions may
have to be made in a sequence
Time
Decision 1
Decision 2
Decision 3
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Uncertain Events
– Uncertain events are either linked to
chance or are linked to a probability
distribution
– Uncertain events have outcome
– It is important to position uncertain events
appropriately between decisions
Decision 1
Decision 2
Time
Decision 3
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Consequences
• After the last decision has been made
and the last uncertain event has been
resolved, the decision maker’s fate is
finally determined
Time
Decision 1
Decision 2
Decision 3
Consequence
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Example
Weather
for
Cleanup
Weather
Accident
Cost
Location
Environmental
Damage
Cause
Policy
Decision
Time
Accident
Management
Decisions
Consequence:
Cost $
Environmental
damage
PR damage
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Making Choices
• Decision Trees
• Example: Texaco vs. Pennzoil
• Decision trees and expected value
– certainty equivalent
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Decision Trees
• Decision trees are a graphical
representation of a decision problem
Venture succeeds
Invest
Large return
Funds lost
Venture fails
Do not invest
Typical return earned
on less risky investment
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Decision Tree
Evacuate
Safety
Cost
Safe
High
Danger
Low
Safe
Low
Storm hits Miami
Forecast
Stay
Storm misses Miami
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Cash Flows and Probabilities
• To each branch of the tree, we can attach
– a probability
– and/or, a cash flow
– or any measure replacing monetary values
for a specific problem
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Case Study: Texaco vs. Pennzoil
• In early 1984, Pennzoil and Getty Oil
agreed to the terms of a merger
• Before the signature of the formal
agreement, Texaco offered Getty a
substantially better price , and Gordon
Getty (majority stockholder) defected on
Pennzoil and sold to Texaco
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Case Study: Texaco vs. Pennzoil
• Pennzoil felt this was unfair practice and filed a
lawsuit against Texaco, alleging that Texaco had
interfered illegally in the the Pennzoil-Getty
negotiations
• Pennzoil won the case in late 1985 and was
awarded $11.1 billion – the largest settlement in
the US at this point in time
• Texaco appealed and the settlement was
reduced by $2 billion – but interest and penalty
got the amount back to $10.3 billion
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Case Study: Texaco vs. Pennzoil
• Kinnear, Texaco’s CEO, announced that
Texaco would file for bankruptcy if
Pennzoil obtained court permission to
secure the judgment by filing liens against
Texaco’s assets
• Kinnear promised to fight the case all the
way to the Supreme Court
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Texaco’s Offer
• In April 1987, just before Pennzoil started
filing liens, Texaco offered to pay Pennzoil
$2billion to settle the entire case
• Liedtke, chairman of Pennzoil, announced
that his advisors estimated that a
settlement of 3-5 billions would be fair
What should Liedtke do?
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Decision Tree
Settlement
Amount
($billion)
Accepts $2 billion
2
Texaco accepts $5 billion
Counteroffer
$5 billion
Texaco
counteroffers
$3 billion
Texaco
refuses
counteroffer
Final Court Decision
Final Court Decision
Refuse
Accepts $3 billion
5
10.3
5
0
10.3
5
0
3
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Subjective Probabilities
• In the decision tree, we are missing
probability estimates of the each event
• For this lecture, we will take these
probability values for granted
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Decision Tree
Accepts $2 billion
Settlement
Amount
($billion)
2
Texaco accepts $5 billion
(0.17)
Counteroffer
$5 billion
(0.33)
Texaco
counteroffers
$3 billion
5
(0.2)
Texaco
refuses
counteroffer
(0.50)
10.3
5
(0.3)
0
(0.2)10.3
(0.5)
5
(0.3)
0
Final Court Decision (0.5)
Final Court Decision
Refuse
Accepts $3 billion
3
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Expected Monetary Value
• Computing an expected monetary value is
a way of selecting among risky alternative
• Computing expected values bring the
problem back to a « certainty equivalent »
• What is the expected value of the court
judgment?
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Expected Value of the Court
Judgment
• EV = 0.2 * 10.3 + 0.5 * 5 + 0.3 * 0
• EV = $ 4.56 billion
(0.2)
Final Court Decision
(0.5)
(0.3)
10.3
5
0
It is possible to reduce the tree with this certainty
equivalent
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Reduced Tree
Accepts $2 billion
Texaco accepts $5 billion
2
5
(0.17)
Counteroffer
$5 billion
(0.33)
Texaco
counteroffers
$3 billion
Eliminated
Texaco refuses counteroffer
4.56
(0.50)
Refuse
Accepts $3 billion
4.56
3
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Expected Monetary Value of the
Counteroffer
• What is the expected monetary value of
Pennzoil $5 billion counter offer:
• EV = P(Texaco accepts) * 5 + P(Texaco
refuse) * 4.56 + P(Texaco counteroffers) *
4.56
• EV = 4.63
Liedtke should not accept the $2 billion offer,
and should counter-offer $5 billion. If Texaco
refuses, then the matter should be taken to
court
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Reducing the Decision Tree
• In practice, we do not reduce the decision
tree but report expected values on the
nodes
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Resolved Decision Tree
Accepts $2 billion
2
Texaco accepts $5 billion
Counteroffer
$5 billion
4.63
(0.33)
Texaco
counteroffers
$3 billion
(0.17)
5
(0.2)
Texaco
4.56
refuses
counteroffer
(0.50)
4.56
Refuse
Final Court Decision (0.5)
(0.3)
(0.2)
Final Court Decision
Accepts $3 billion
(0.5)
(0.3)
10.3
5
0
10.3
5
0
3
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Suggested Homework
• Problem S2-10, p. 70
• Problem S2-13, p. 71
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