Module 15 July 28, 2014
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1 Module 15 July 28, 2014
2 General Approach to Decision Making Many Uses: Capacity Planning Product/Service Design Equipment Selection Location Planning Others
3 Typically Used for Decisions Characterized by the Following: Set of Possible Future Conditions that Will Have a Bearing on the Results of the Decision List of Alternatives to Choose From Known Payoff for Each Alternative Under Each Possible Future Condition
4 Steps: Identify Possible Future Conditions Example: Demand is Low, Medium, or High Referred to as States of Nature Develop List of Possible Alternatives Often Featuring the Do Nothing Alternative
5 Steps: Determine Payoff Associated with Each Alternative for Each Possible Future Condition If Possible, Estimate the Likelihood of Future Condition Evaluate Alternatives by Some Criterion and Select Best
6 Shows Expected Payoffs for Various States of Nature Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both * Payoffs in Predicted Profit Per Month
7 Certainty All Parameters Known Risk Certain Parameters Probabilistic Uncertainty Impossible to Assess Likelihood of Possible Future Events
8 Under Certainty We Know Which Future Condition(s) Will Occur Choose Alternative with Best Payoff Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both * Payoffs in Predicted Profit Per Month
9 Under Uncertainty Four Decision Criteria Maximin Find Worst Possible Payoff for Each Alternative, Choose Alternative with Best Worst Pessimistic Gives Guaranteed Minimum Maximax Determine Best Possible Payoff, Choose Alternative with Best Payoff Optimistic
10 Under Uncertainty Laplace Determine Average Payoff for Each Alternative, Choose Alternative with Best Average Assumes States of Nature Equally Likely Minimax Regret Determine Worst Regret, Choose Alternative with the Best Worst Seeks to Minimize Difference Between Actual Payoff and Best Payoff for Each State of Nature
11 Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both * Payoffs in Predicted Profit Per Month Maximin Indoor 500 Drive-In 700 Both Choose Best of Worst Drive-In
12 Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both Maximax * Payoffs in Predicted Profit Per Month Indoor 2000 Drive-In 1500 Both 3000 Choose Best of Best Both
13 Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both Laplace * Payoffs in Predicted Profit Per Month Indoor ( )/3 = Drive-In ( )/3 = 1150 Both ( )/3 = 0 Choose Indoor
14 Minimax Regret Criterion for Decision Making Under Uncertainty Develop a Table of Regrets (Opportunity Losses) Subtract EVERY Payoff in Each Column from the BEST Payoff in that Column
15 Type of Parlor Demand for Ice Cream Low Moderate High Indoor Drive-In Both Type of Parlor Indoor Drive-In Both Demand for Ice Cream Low Moderate High
16 Identify Worst Regret for Each Alternative Indoor 1000 Drive-In 1750 Both 2700 Choose LOWEST of These Regrets Choose Indoor Alternative
17 Under Risk We Know the Probability of Occurrence for Each State of Nature Common Approach is the Expected Monetary Value (EMV) Criterion Determine Expected Payoff for Each Alternative Choose Alternative with Best Expected Payoff
18 Under Risk Type of Parlor Expected Payoffs: Indoor: (0.3)(500) + (0.5)(1200) + (0.2)(2000) = 1150 Drive-In: (0.3)(700) + (0.5)(1500) + (0.2)(1250) = 1210 Both: (0.3)(-2000) + (0.5)(-1000) + (0.2)(3000) = -500 Select Drive-In Demand Probabilities Low Moderate High Indoor Drive-In Both
19 In Some Situations, it May Be Possible to Delay Making a Decision Until it is Clear Which State of Nature Will Occur in the Future EVPI is the Difference Between the Expected Payoff with Perfect Information and the Expected Payoff Under Risk
20 Steps: Compute Payoff Under Certainty Compute Payoff Under Risk EVPI is Difference Example Payoff Under Certainty (0.3)(700) + (0.5)(1500) + (0.2)(3000) = 1560 Payoff Under Risk = 1210 EVPI = = 350
21 NOTE: EVPI is the UPPER LIMIT on the Amount That the Decision Maker Should Be Willing to Pay to Obtain Perfect Information
22 Visual tool to represent a decision model Squares: Circles: Probability) - Decisions - Uncertain Events (Subject to Branches: Potential Actions or Results Some Branches are Terminal (End) Terminal Branches Have Payoffs (Payoffs Should Reflect All Costs/Revenues)
23 Simple Example No $0 Terminal Branches Buy Raffle Ticket? Win (0.01) $49 Decision Yes ($1 to Buy) Lose (0.99) -$1 Uncertain Event
24 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
25 Simple Example No $0 Buy Raffle Ticket? Win (0.01) $49 Yes ($1 to Buy) Lose (0.99) -$1 Expected Value = (0.01)(49) + (0.99)(-1) = = -0.5
26 Simple Example No $0 Buy Raffle Ticket? Yes -$0.5 Should You Buy a Raffle Ticket? NO! Your Expected Value is Negative
27 More Complex Example
28 Settle Now ($?) Settle? Wait
29 Settle Now ($?) Settle? Opp Wins (0.5) Wait Opp Loses (0.5) $0
30 Settle Now ($?) Opp Sues (0.8) Settle? Opp Wins (0.5) No Suit(0.2) Wait Opp Loses (0.5) $0
31 Settle ($8) Settle Now ($?) Opp Sues (0.8) Contest Contest Settlement Settle? Opp Wins (0.5) No Suit(0.2) Wait $0 Opp Loses (0.5) $0
32 Settle ($8) We Win (0.3) ($0) Settle Now ($?) Opp Sues (0.8) Contest We Lose (0.7) ($10) Settle? Opp Wins (0.5) Contest Settlement Wait No Suit(0.2) $0 Low (0.4) ($5) Opp Loses (0.5) $0 Win (0.6) ($15)
33 Settle ($8) We Win (0.3) ($0) Settle Now ($?) Opp Sues (0.8) Contest We Lose (0.7) ($10) Settle? Opp Wins (0.5) Contest Settlement EV = (0.3)(0) + (0.7)(10) = 7 Wait No Suit(0.2) $0 Low (0.4) ($5) Opp Loses (0.5) $0 Win (0.6) ($15) EV = (0.4)(5) + (0.6)(15) = 11
34 Settle ($8) Settle Now ($?) Opp Sues (0.8) Contest $7 Settle? Opp Wins (0.5) No Suit(0.2) Contest Settlement Wait $0 Opp Loses (0.5) $0 $11
35 Settle Now ($?) Opp Sues (0.8) $7 Settle? Opp Wins (0.5) No Suit(0.2) Wait $0 Opp Loses (0.5) $0
36 Settle Now ($?) Opp Sues (0.8) $7 Settle? Opp Wins (0.5) No Suit(0.2) $0 Wait EV = (0.8)(7) + (0.2)(0) = 5.6 Opp Loses (0.5) $0
37 Settle Now ($?) Settle? Opp Wins (0.5) $5.6 Wait Opp Loses (0.5) $0
38 Settle Now ($?) Settle? Opp Wins (0.5) $5.6 Wait Opp Loses (0.5) $0 EV = (0.5)(5.6) + (0.5)(0) = 2.8
39 Settle Now ($?) Settle? Wait $2.8
40 Uses of decision analysis Decision environment Uncertainty approaches Risk approaches EVPI Decision Trees
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