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Chapter 18 Introduction to Decision Analysis Business Statistics:  A Decision-Making Approach 6 th  Edition
Chapter Goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Making Overview Decision Making Certainty Nonprobabilistic Uncertainty Probabilistic Decision Environment Decision Criteria
The Decision Environment Certainty Uncertainty Decision Environment Certainty:   The results of decision alternatives are known Example:   Must print 10,000 color brochures Offset press A:   $2,000 fixed cost    + $.24 per page Offset press B:   $3,000 fixed cost    + $.12 per page *
The Decision Environment Uncertainty Certainty Decision Environment Uncertainty:   The outcome that will occur after a choice is unknown Example:   You must decide to buy an item now or wait.  If you buy now the price is $2,000.  If you wait the price may drop to $1,500 or rise to $2,200.  There also may be a new model available later with better features.  * (continued)
Decision Criteria Nonprobabilistic Probabilistic Decision Criteria Nonprobabilistic Decision Criteria:   Decision rules that can be applied if the probabilities of uncertain events are not known. * ,[object Object],[object Object],[object Object]
Decision Criteria Nonprobabilistic Probabilistic Decision Criteria * Probabilistic Decision Criteria:   Consider the probabilities of uncertain events and select an alternative to maximize the expected payoff of minimize the expected loss ,[object Object],[object Object],(continued)
A Payoff Table ,[object Object],Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Maximax Solution 1. Maximum Profit 200 120 40 ,[object Object],[object Object],Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Maximax Solution 1. Maximum Profit 200 120 40 ,[object Object],[object Object],[object Object],2. Greatest maximum is to choose  Large factory   (continued) Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Maximin Solution 1. Minimum Profit -120 -30 20 ,[object Object],[object Object],Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Maximin Solution 1. Minimum Profit -120 -30 20 ,[object Object],[object Object],[object Object],2. Greatest minimum is to choose  Small factory   (continued) Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Opportunity Loss The choice “Average factory” has payoff 90 for “Strong Economy”.  Given “Strong Economy”,  the choice of “Large factory” would have given a payoff of 200, or 110 higher .  Opportunity loss = 110 for this cell. Opportunity loss is the difference between an  actual  payoff for a decision and the  optimal  payoff for that state of nature Payoff Table Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Opportunity Loss (continued) Payoff Table Opportunity Loss Table Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20 Investment Choice (Alternatives) Opportunity Loss in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 0 110 160 70 0 90 140 50 0
Minimax Regret Solution Opportunity Loss Table ,[object Object],[object Object],1. Maximum Op. Loss 140 110 160 Investment Choice (Alternatives) Opportunity Loss in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 0 110 160 70 0 90 140 50 0
Minimax Regret Solution Opportunity Loss Table ,[object Object],[object Object],[object Object],1. Maximum Op. Loss 140 110 160 2. Smallest maximum loss is to choose  Average factory   (continued) Investment Choice (Alternatives) Opportunity Loss in $1,000’s   (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 0 110 160 70 0 90 140 50 0
Expected Value Solution ,[object Object],Suppose these probabilities have been assessed for these states of nature Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy (.3) Stable Economy (.5) Weak Economy (.2) Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Expected Value Solution ,[object Object],[object Object],Expected Values 61 81 31 Maximize expected value by choosing  Average factory   (continued) Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy (.3) Stable Economy (.5) Weak Economy (.2) Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Expected Opportunity Loss Solution ,[object Object],[object Object],Expected Op. Loss (EOL) 63 43 93 Minimize expected op. loss by choosing  Average factory   Opportunity Loss Table Investment Choice (Alternatives) Opportunity Loss in $1,000’s   (States of Nature) Strong Economy (.3) Stable Economy (.5) Weak Economy (.2) Large factory Average factory Small factory 0 110 160 70 0 90 140 50 0
Cost of Uncertainty ,[object Object],[object Object],[object Object],[object Object],[object Object]
Expected Value Under Certainty ,[object Object],[object Object],Example:  Best decision given “Strong Economy” is “Large factory” 200  120  20 Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy (.3) Stable Economy (.5) Weak Economy (.2) Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Expected Value Under Certainty ,[object Object],200  120  20 (continued) EVUC = 200 (.3) +120 (.5) +20 (.2)   = 124 Investment Choice (Alternatives) Profit in $1,000’s   (States of Nature) Strong Economy (.3) Stable Economy (.5) Weak Economy (.2) Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
Cost of Uncertainty Solution ,[object Object],[object Object],[object Object],so:  EVPI  =  EVUC – EV   =  124 – 81   =  43 Recall:  EVUC = 124   EV is maximized by choosing “Average factory”,   where EV = 81
Decision Tree Analysis ,[object Object],[object Object],[object Object]
Sample Decision Tree Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy
Add Probabilities and Payoffs Large factory Small factory Decision Average factory Uncertain Events (States of Nature) Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy (continued) Payoffs Probabilities 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2)
Fold Back the Tree Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2) EV=200(.3)+50(.5)+(-120)(.2)= 61 EV=90(.3)+120(.5)+(-30)(.2)= 81 EV=40(.3)+30(.5)+20(.2)= 31
Make the Decision Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2) EV= 61 EV= 81 EV= 31 Maximum  EV= 81

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Decision analysis ppt @ bec doms

  • 1. Chapter 18 Introduction to Decision Analysis Business Statistics: A Decision-Making Approach 6 th Edition
  • 2.
  • 3. Decision Making Overview Decision Making Certainty Nonprobabilistic Uncertainty Probabilistic Decision Environment Decision Criteria
  • 4. The Decision Environment Certainty Uncertainty Decision Environment Certainty: The results of decision alternatives are known Example: Must print 10,000 color brochures Offset press A: $2,000 fixed cost + $.24 per page Offset press B: $3,000 fixed cost + $.12 per page *
  • 5. The Decision Environment Uncertainty Certainty Decision Environment Uncertainty: The outcome that will occur after a choice is unknown Example: You must decide to buy an item now or wait. If you buy now the price is $2,000. If you wait the price may drop to $1,500 or rise to $2,200. There also may be a new model available later with better features. * (continued)
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  • 13. Opportunity Loss The choice “Average factory” has payoff 90 for “Strong Economy”. Given “Strong Economy”, the choice of “Large factory” would have given a payoff of 200, or 110 higher . Opportunity loss = 110 for this cell. Opportunity loss is the difference between an actual payoff for a decision and the optimal payoff for that state of nature Payoff Table Investment Choice (Alternatives) Profit in $1,000’s (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20
  • 14. Opportunity Loss (continued) Payoff Table Opportunity Loss Table Investment Choice (Alternatives) Profit in $1,000’s (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 200 90 40 50 120 30 -120 -30 20 Investment Choice (Alternatives) Opportunity Loss in $1,000’s (States of Nature) Strong Economy Stable Economy Weak Economy Large factory Average factory Small factory 0 110 160 70 0 90 140 50 0
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  • 25. Sample Decision Tree Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy
  • 26. Add Probabilities and Payoffs Large factory Small factory Decision Average factory Uncertain Events (States of Nature) Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy (continued) Payoffs Probabilities 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2)
  • 27. Fold Back the Tree Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2) EV=200(.3)+50(.5)+(-120)(.2)= 61 EV=90(.3)+120(.5)+(-30)(.2)= 81 EV=40(.3)+30(.5)+20(.2)= 31
  • 28. Make the Decision Large factory Small factory Average factory Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy Strong Economy Stable Economy Weak Economy 200 50 -120 40 30 20 90 120 -30 (.3) (.5) (.2) (.3) (.5) (.2) (.3) (.5) (.2) EV= 61 EV= 81 EV= 31 Maximum EV= 81