Homework due next Tuesday, September 22

1
Homework due next Tuesday, September 22

• p. 156 5-7, 5-8, 5-9
• Please use complete sentences to answer any
  questions and make. Include any tables you are
  asked to make.

2
Chapter 5 Decision-making Concepts

• Quantitative Decision Making with Spreadsheet
  Applications 7th ed.
• By Lapin and Whisler
• Section 5-7 Decision Tree Analysis
• Some slides are from Business Statistics
• A Decision-Making Approach
• 6th Edition found at www.clt.astate.edu/asyamil/gr
  oebner6ed/ppt/ch18ppln.ppt

3
The Bayes Decision Rule

• Takes into account all the information about the
  chances for various payoffs.

4
Other Decision Criteria

• Maximin Payoff Criterion choose the best of the
  worst outcomes.
• Maximum Likelihood Criterion focus on the most
  likely event to the exclusion of all others.
• The Criterion of Insufficient Reason every
  event has the same probability.

5
Table vs. Tree

• Payoff table simple decisions
• Decisions made at different points in time with
  uncertain events occurring between decisions.
• Tree gives more flexibility.
• Tree shows every possible course of action and
  all possible outcomes.

6
Decision Tree

• A decision tree is a picture of all the possible
  courses of action and the consequent possible
  outcomes.
• A box is used to indicate the point at which a
  decision must be made,
• The branches going out from the box indicate the
  alternatives under consideration
• A circle represents an event (usually has a
  probability)
• The branches going out from the circle represent
  outcomes of the event.

7
Sample Decision Tree
Strong Economy
Large factory
Stable Economy
Weak Economy
Strong Economy
Average factory
Stable Economy
Weak Economy
Strong Economy
Small factory
Stable Economy
Weak Economy
8
Add Probabilities and Payoffs
(continued)
Strong Economy
(.3)
200
Large factory
Stable Economy
(.5)
50
Weak Economy
(.2)
-120
(.3)
Strong Economy
90
Average factory
(.5)
Stable Economy
120
(.2)
Weak Economy
-30
Decision
(.3)
Strong Economy
40
Small factory
(.5)
Stable Economy
30
(.2)
Weak Economy
20
Uncertain Events (States of Nature)
Payoffs
Probabilities
9
Decision Tree Analysis

• Each node is evaluated in terms of its expected
  payoff.
• Event forks expected payoffs are computed.
• Act forks the greatest value is brought back.
• The decision tree is folded back by maximizing
  expected payoff.
• Inferior acts are pruned from the tree.
• The pruned tree indicates the best course of
  action, the one maximizing expected payoff.
• The process works backward in time.

10
Fold Back the Tree
Strong Economy
(.3)
200
EV200(.3)50(.5)(-120)(.2)61
Large factory
Stable Economy
(.5)
50
Weak Economy
(.2)
-120
(.3)
Strong Economy
90
EV90(.3)120(.5)(-30)(.2)81
Average factory
(.5)
Stable Economy
120
(.2)
Weak Economy
-30
(.3)
Strong Economy
40
EV40(.3)30(.5)20(.2)31
Small factory
(.5)
Stable Economy
30
(.2)
Weak Economy
20
11
Make the Decision
Strong Economy
(.3)
200
EV61
Large factory
Stable Economy
(.5)
50
Weak Economy
(.2)
-120
(.3)
Strong Economy
90
EV81
Maximum EV81
Average factory
(.5)
Stable Economy
120
(.2)
Weak Economy
-30
(.3)
Strong Economy
40
EV31
Small factory
(.5)
Stable Economy
30
(.2)
Weak Economy
20