Chapter 3 Structuring Decisions
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Transcript of Chapter 3 Structuring Decisions
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Chapter 3
Structuring Decisions
Dr. Greg ParnellDepartment of Mathematical Sciences
Virginia Commonwealth University
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Overview
• Problem structuring
• Decision basis
• Structuring Objectives– Value Hierarchy– Means-Objectives Network
• Influence Diagram
• Decision Tree
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Decision Analysis Is a Systematic Process
Questions:
Deliverables:
What do we want?What do we know?What can we do?
ValuesValue HierarchyInformationAlternativesInfluence DiagramDecision Tree
Value ModelSensitivity AnalysisCritical Uncertainties
What are the relationships?What is important?
What are the possible outcomes?What are the probabilities of those outcomes?How much could we gain/lose?
Probability DistributionsDominated AlternativesRisk Profiles
Are we ready to decide OR how much more information would we be willing to pay for?
Value of InformationValue of Control
Iteration
ProblemStructure
DeterministicAnalysis
Probabilistic Analysis
Evaluation
DecisionInitial
Situation
Problem Structuring
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Decision Basis
ValuesWhat do we want?
InformationWhat do we know?
AlternativesWhat can we do?
• Problem structuring focuses on the values, alternatives, and information.• We start with values. (We will use single value, usually NPV, until Chapter 15)
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Structuring Objectives
• Identify objectives– Develop a wish list– Identify alternatives– Consider problems and shortcomings– Predict consequences– Identify, goals, constraints, and guidelines– Consider different perspectives– Determine strategic objectives– Determine generic objectives
• Sort or organize objectives into logical groups
Keeney, R.L., (1994) "Creativity in Decision Making with Value-Focused Thinking," Sloan Management Review, Summer, 33-41.
First we identify, then we group the objectives.
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Definitions
• fundamental objective(s): the decision-makers ultimate objective(s)
• objectives: the essential reasons for our interest in the decision situation
• objectives (value) hierarchy: a hierarchy that identifies what aspects of the higher level objective are important (Keeney/Clemen call this a fundamental-objectives hierarchy)
• means: specific approach to achieve our objectives• means-objectives network: network whose
purpose is to help generate alternatives by identifying the means to obtain our objectives
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Example: Virginia Science Museum• Experiencing queuing problems at the major exhibits
– Why?• Long lines, people leaving
– What?• Getting patrons into the museum
– How?• Cashiers with computer hardware and software
– Who?• Patrons, cashiers, managers
– When? • During the most popular exhibits
– Where? • Entrance to the museum
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Objectives HierarchyScience Museum of Virginia
M in im ize t im ein q u eu e
M in im ize p rocess in gtim e
M in im ize p a tron w a it in gan d p roces in g t im e
M u seu mE m p loyees
H ard w areC os ts
S o ftw areC os ts
V is ito rg ood w ill
M in im ize p a tronp rocess in g cos t
Im p rove p a tron p rocess in g a tth e m u seu m Fundamental Objective
Objectives
Subobjectives
The objectives define the fundamental objective & subobjectives define the objectives.
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Means-Objectives Network
M in im ize t im ein q u eu e
M in im ize p rocess in gtim e
M in im ize p a tron w a it in gan d p roces in g t im e
M u seu mE m p loyees
H ard w areC os ts
S o ftw areC os ts
V is ito rg ood w ill
M in im ize p a tronp rocess in g cos t
Im p rove p a tron p rocess in g a tth e m u seu m
Provide incentives to arrive at non-peak times
Improved software
Improved hardware
Cashiertraining
Provideentertainment
Separate processing for members
Recruitmembers
• Add more means• Connect the means to the subobjectives
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Influence Diagrams - Node Types
• ID captures the DM’s state of information– Technique for decision structuring
– Algorithms also exist to solve IDs
– IDs have no cycles [IDs are not flow diagrams]
• Arrows are used for two purposes– Relevance: knowledge of the outcome of a predecessor node is useful to
determine the outcome of a successor node– Sequence: the outcome of a predecessor node is known before the outcome of a
successor node
Chance
Deterministic
ValueDecision
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Venture Capitalist's Decision
Questions:1. What does the arc from Invest to Return on Investment mean?2. What does the arc from Venture to Return on Investment mean?3. Why is there no arc from Invest to Venture?4. Why is there no arc from Venture to Invest?5. How could the DM obtain additional information about the Venture?
Return onInvestment
Venture Suceeds or Fails
Invest?
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Influence Diagram Modeling
QUESTIONS 1. Describe how the two arrows model sequence and relevance?2. What determines the number of possible consequences? 3. If we had three decision alternatives and four Market Activity outcomes, how many consequences would we have?
• This approach would be very cumbersome for large problems, fortunately, in many cases, we can use functions to simplify modeling.
Payoff
MarketActivity
InvestmentChoice
ALTERNATIVES:Savings account
Mutual fund
MARKET OUTCOMES:Market up
Market down
ALTERNATIVE MARKET PAYOFFSavings Account Up 100Savings Account Down 100Mutual Fund Up 400Mutual Fund Down -100
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Imperfect Information - Very Common
QUESTIONS1. Would you expect the Market Survey to be perfect or imperfect information? Why?2. What is the effect of number of outcomes of the Market Survey have on the number of Payoff outcomes? Why?3. Describe how the arrows model sequence and relevance?4. Why do we draw the arrow from Actual Market to Market Survey versus the other direction?5. What would an arrow from New Product to Actual Market mean?
Payoff
Market Survey
ActualMarket
New ProductDecision?
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Wildcat Oil ID
ProfitsRevenues
SeismicStructure
ExpSeismic
Test
Amountof Oil
DrillingCosts
TestDrill
WILDCAT OIL DRILLING PROBLEM
Interpret this ID
• Some Common Influence Diagram Mistakes
- IDs are not flow charts- NO CYCLES! Sequential decisions
• DPL Note: Read DPL Users Guide, pp. 244-247
- Color of the arrows is the key!!!!
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Probabilistic Modeling with IDs
Time to Shipment
ManualPrinting
Time
PackagingPrinting
Time
ManualCompletion
Time
PackagingCompletion
Time
SoftwareCompletion
Time
DesignSoftware
Beta-testSoftware
ProgramSoftware
WriteManual
ReviseManual
Design Packaging
What is missing from this ID?
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Decision Trees• IDs are good for problem structuring since they
suppress detail• Decision trees - identify the sequence of
decisions/events and have a branch for each decision alternative and each uncertain event outcome
• Decision tree must identify all paths• Each outcome space must be ME & CE !
Low
Return_on_Investment Nominal
Return_on_Investment High
Return_on_Investment
Yes
Venture Suceeds or Fails
No
Invest? Develop the decision tree for each of the IDs we have developed
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New Product Decision
Yes
Payoff No
Payoff
Low
Nominal
High
New ProductDecision?
Low
Nominal
High
ActualMarket
Market Survey
How many outcomes (at the end of the DT) are there?How many Payoffs need to be calculated?
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Decision Tree
Dry
Revenues Wet
Revenues Soaking
Revenues
Low
Drilling_Costs Med
Drilling_Costs High
Drilling_Costs
Amountof Oil
Yes
DrillingCosts
No None
a
Drill
No
Open
Closed
a Core Sample
Test
SeismicStructure
No
Open
Closed
a Exp Seismic
Test
ExpSeismic
Test
Test
WILDCAT OIL DRILLING PROBLEM
How many outcomes (at the end of the DT) are there?How many Payoffs need to be calculated?
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Probability Tree
Fancy
Time_to_Shipment Simple
Time_to_Shipment
Major changes
Minor changes
Design Packaging
Easy
Hard
ReviseManual
Smooth
Buggy
WriteManual
Easy
Hard
Beta-testSoftware
Major Changes
Minor Changes
ProgramSoftware
DesignSoftware
What node type is missing?How many outcomes (at the end of the DT) are there?How many Payoffs need to be calculated?
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Decision Trees Versus Influence Diagrams
• Influence diagrams• Good for problem structuring
• Good for communicating with management- suppress details
• Decision trees• Show details
- better for asymmetric problems
• Complementary- DPL uses both representations
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Clarity Test• Elements of a decision must be clearly defined
• DM, DM's staff, decision analyst
• Clairvoyant = access to all future information
• Clarity Test (Howard, 1988)Your model passes the clarity test
if a clairvoyant would be able to unequivocally tell you the outcome of any event in the ID/decision tree
EXAMPLE: Does the following uncertain variable pass the clarity test?
Low
Nominal
High
Saturn (SC)Sales in
2000
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Summary• Problem structuring• Decision basis• Structuring objectives
– Value hierarchy– Means-objectives network
• Influence diagram– Types of nodes
• Decision tree– Types of nodes
• Comparison– Advantages of each problem structuring method
• Clarity test