Decision Analysis

How to make a difficult decision?

 Uncertainty regarding the future  Conflicting values or objectives  Goal of Decision Analysis: – Help people make good decisions

Good Decisions vs Good Outcomes

 Good decisions don’t always result in good outcomes  Not necessarily the fault of the decision maker  A structured approach to decision making should lead to more good outcomes than if decisions were made haphazardly

Characteristics

 Decisions  Alternatives  States of nature

Decisions

 Decision must involve at least 2 alternatives – Alternative: course of action intended to solve a problem

Alternatives

 Alternatives are evaluated on the basis of the value added to one or more decision criteria – Criteria: various factors that are important to the decision maker and influenced by the alternatives

States of Nature

 States of nature correspond to future events that are not under the decision maker’s control

Magnolia Inns Example

 Decision Alternatives – Buy at A – Buy at B – Buy at A & B – Buy nothing  States of Nature – Airport built at A – Airport built at B

Magnolia Inns Cash Flow

Current purchase price Present value of future cash flows if hotel and airport are built at this location Parcel of Land Near Location A $18 B $12 $31 $23 Present value of future sales price of parcel if the airport is not built at this location $6 $4 (Note: All values are in millions of dollars.)

Payoff Matrix

Land Purchased Airport is Built at Location at Location(s) A B A B A&B None $13 ($8) $5 $0 ($12) $11 ($1) $0

Maximax Decision Rule

 Alternative with largest payoff

Land Purchased Airport is Built at Location at Location(s) A B A B A&B None $13 ($8) $5 $0 ($12) $11 ($1) $0

Maximin

 Alternative with largest minimum payoff – Hedge against worst possible outcome

Land Purchased Airport is Built at Location at Location(s) A B A B A&B None $13 ($8) $5 $0 ($12) $11 ($1) $0

Expected Monetary Value (EMV)

 Decision alternative with largest EMV

Land Purchased Airport is Built at Location at Location(s) A B A B A&B None $13 ($8) $5 $0 ($12) $11 ($1) $0 EMV ($2) $3.4

$1.4

$0

 Example:

40% chance A, 60% chance B

Sensitivity Analysis

Prob. Of location A

0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

EMV A

-$2.0

-$12.0

-$9.5

-$7.0

-$4.5

-$2.0

$0.5

$3.0

$5.5

$8.0

$10.5

$13.0

EMV B

$3.4

$11.0

$9.1

$7.2

$5.3

$3.4

$1.5

-$0.4

-$2.3

-$4.2

-$6.1

-$8.0

EMV A&B EMV None

$1.4

$0.0

-$1.0

-$0.4

$0.0

$0.0

$0.2

$0.8

$1.4

$2.0

$2.6

$3.2

$3.8

$4.4

$5.0

$0.0

$0.0

$0.0

$0.0

$0.0

$0.0

$0.0

$0.0

$0.0

Expected Value of Perfect Info

 EV of PI = EV with PI – max EMV

Decision Trees

 Decision nodes  Event nodes  Terminal nodes  Magnolia Inns Example (page 731)

TreePlan

 Excel add-in from Prof. Mike Middleton