Decision Analysis
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Decision Analysis OPS 370
description
Decision Analysis. OPS 370. Decision Theory. A. B. a. b. c. d. e. Decision Theory. A. a. b. c. . Decision Theory. Steps: 1. A. B. 2. A. 3. 4. 5. Payoff Table. Shows Expected Payoffs for Various States of Nature. Decision Environments. A. a. B. a. C. - PowerPoint PPT Presentation
Transcript of Decision Analysis
Decision Analysis
OPS 370
Decision Theory
• A. • B.
– a.– b.– c.– d.– e.
Decision Theory
• A.
– a.
– b. – c.
Decision Theory• Steps:
– 1. • A. • B.
– 2. • A.
– 3.
– 4.
– 5.
Payoff Table
• Shows Expected Payoffs for Various States of Nature
Low Moderate HighIndoor 500 1200 2000
Drive-In 700 1500 1250Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
* Payoffs in Predicted Profit Per Month
Decision Environments
• A. – a.
• B.– a.
• C. – a.
Decision Making
• A. – a. – b.
Low Moderate HighIndoor 500 1200 2000
Drive-In 700 1500 1250Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
* Payoffs in Predicted Profit Per Month
Decision Making
• Under Uncertainty– Four Decision Criteria– 1.
• A.
• B. – 2.
• A.
• B.
Decision Making
• Under Uncertainty– 3.
• A.
• B. – 4.
• A.
• B.
Example
• Maximin– Indoor – Drive-In – Both – Choose “Best of Worst”
Low Moderate HighIndoor 500 1200 2000
Drive-In 700 1500 1250Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
* Payoffs in Predicted Profit Per Month
Example
• Maximax– Indoor – Drive-In – Both – Choose “Best of Best”
Low Moderate HighIndoor 500 1200 2000
Drive-In 700 1500 1250Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
* Payoffs in Predicted Profit Per Month
Example
• Laplace– Indoor – Drive-In – Both – Choose
Low Moderate HighIndoor 500 1200 2000
Drive-In 700 1500 1250Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
* Payoffs in Predicted Profit Per Month
Decision Making
• Minimax Regret– Criterion for Decision Making Under Uncertainty
• A.
• B.
Decision MakingLow Moderate High
Indoor 500 1200 2000Drive-In 700 1500 1250
Both -2000 -1000 3000
Demand for Ice Cream
Type of Parlor
Low Moderate HighIndoor
Drive-InBoth
Demand for Ice Cream
Type of Parlor
Decision Making
• A.– Indoor – Drive-In – Both
• B. – a.
Decision Making
• Under Risk– A.
– B.
• a. • b.
Decision Making
• Under Risk
– Expected Payoffs:• Indoor:• Drive-In: (0.3)(700) + (0.5)(1500) + (0.2)(1250) = • Both: (0.3)(-2000) + (0.5)(-1000) + (0.2)(3000) =
– Select
0.3 0.5 0.2Low Moderate High
Indoor 500 1200 2000Drive-In 700 1500 1250
Both -2000 -1000 3000
Type of Parlor
Demand Probabilities
Expected Value of Perfect Info
• A.
• B.
Expected Value of Perfect Info
• Steps:– 1. – 2. – 3.
• Example– A.
• a. – B. – C.
Expected Value of Perfect Info
• NOTE:– A.
Decision Trees
• Visual tool to represent a decision model• Squares: - Decisions• Circles: - Uncertain Events (Subject to
Probability)• Branches: Potential Actions or Results• Some Branches are Terminal (End) Terminal
Branches Have Payoffs (Payoffs Should Reflect All Costs/Revenues)
Decision Trees
• Simple Example
No
Yes($1 to Buy)
Buy Raffle Ticket? Win (0.01)
Lose (0.99)
Terminal Branches
Decision
Uncertain Event
$0
$49
-$1
Decision Trees
• Build Decision Trees from LEFT to RIGHT• Solve Decision Trees from RIGHT to LEFT• Determine Expected Values at Chance Nodes• Choose “Best” Expected Value at Decision
Nodes• Identify “Best” Path of Decisions
Decision Trees
• Simple Example
No
Yes($1 to Buy)
Buy Raffle Ticket? Win (0.01)
Lose (0.99)
$0
$49
-$1
Expected Value = (0.01)(49) + (0.99)(-1) = 0.49 – 0.99 = -0.5
Decision Trees
• Simple Example
• Should You Buy a Raffle Ticket?• NO! Your Expected Value is Negative
No
Yes
Buy Raffle Ticket?
$0
-$0.5
Decision Trees
• More Complex Example
Decision Trees
Settle?
Settle Now ($?)
Wait
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
$0
Settle ($8)
Contest
ContestSettlement
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
$0
Settle ($8)
Contest
ContestSettlement
We Win (0.3) ($0)
We Lose (0.7) ($10)
Low (0.4) ($5)
Win (0.6) ($15)
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
$0
Settle ($8)
Contest
ContestSettlement
We Win (0.3) ($0)
We Lose (0.7) ($10)
Low (0.4) ($5)
Win (0.6) ($15)
EV = (0.3)(0) + (0.7)(10) = 7
EV = (0.4)(5) + (0.6)(15) = 11
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
$0
Settle ($8)
Contest
ContestSettlement
$7
$11
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)
$0
$7
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
Opp Sues (0.8)
No Suit(0.2)$0
$7
EV = (0.8)(7) + (0.2)(0) = 5.6
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)
$0
$5.6
Decision Trees
Settle?
Settle Now ($?)
Wait
Opp Wins (0.5)
Opp Loses (0.5)$0
$5.6
EV = (0.5)(5.6) + (0.5)(0) = 2.8
Decision Trees
Settle?
Settle Now ($?)
Wait$2.8
