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Introduction to Decision Analysis

Introduction to Decision Analysis. Decision analysis studies the process of making difficult decisions The objective is to update, model, and document the intuition of managers A structured approach to decision making is especially critical in group decision making

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Introduction to Decision Analysis

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  1. Introduction to Decision Analysis • Decision analysis studies the process of making difficult decisions • The objective is to update, model, and document the intuition of managers • A structured approach to decision making is especially critical in group decision making • Key importance in the case of resource decisions (operations strategy)

  2. Why are decisions difficult? • Complexity • Simply keeping all the issues in mind at one time is nearly imposible • Uncertainty • Making a decision is especially difficult when you are not sure about one decision variable’s state • Multiple objectives • Different perspectives lead to different conclusions

  3. Example • Winter of 1985: • The Oregon Department of Agriculture (ODA) faced an infestation of gypsy moth in Lane County in Western Oregon • Forest industry representative call for an aggressive eradication campaign

  4. Alternatives • Use BT, a bacterial insecticide • target specific • ecologically safe • reasonably effective • Use Orthene, a chemical spray • registered as acceptable for home garden use • questions about its ultimate ecological effects • possible danger to humans • Possible to use both

  5. Opinions • Forestry officials: • Argue Orthene is more potent and is necessary to ensure eradication • Environmentalists: • Orthene potentially too dangerous • Others • Already too late anyway • Others... • Still time but decision/implementation has to be made now

  6. Subjective Judgments • In decisional contexts such as the one faced by ODA • objective data is lacking • a procedure determining an « optimal » decision derived from objective data is of little use... • personal statements about uncertainty and value become important inputs (no choice...) • Discovering and finalizing these judgments is a key issue in decision analysis • Instead of criticizing them, we will learn how to better assess and use them

  7. The Decision Analysis Process • Identify the decision situation and understand objectives • Identify alternatives • Decompose and model the problem • model of problem structure • model of uncertainty • model of preferences • Choose the best alternative • Sensitivity analysis • Is further analysis needed? yes/no? • Implement the chosen alternative

  8. Requisite Decision Models • A model can be considered requisite only when no new intuitions emerge about the problem • or when it contains everything that is essential for solving the problem

  9. Elements of a Decision Problem • Values and objectives • Decisions to make • Uncertain events • Consequences

  10. Values and Objectives • Value: used in its general sense « things that matter to you » • Objective: Specific thing that you want to achieve • The set of objectives taken up together make up the values • Values are the reason for making the decision in the first place • They define the decision context

  11. Objectives for Boeing’s Supercomputer Supercomputer Objectives Management Issue Vendor Health US Ownership Commitment to supercomputer Cost Five-year costs Cost of improved performance Performance Speed Throughput Memory Size Disk Size On-site performance User Needs Installation date Roll in/Roll out Ease of Use Software compatibility Mean time between failures Operational Needs Square footage Water cooling Operator tools Telecommuni- -cations Vendor support

  12. Decision to Make • Given a decisional context, one (or several) decision(s) has to be made • In some cases, several decisions may have to be made in a sequence Time Decision 1 Decision 2 Decision 3

  13. Uncertain Events • Uncertain events are either linked to chance or are linked to a probability distribution • Uncertain events have outcome • It is important to position uncertain events appropriately between decisions Decision 1 Decision 2 Decision 3 Time

  14. Consequences • After the last decision has been made and the last uncertain event has been resolved, the decision maker’s fate is finally determined Time Decision 1 Decision 2 Decision 3 Consequence

  15. Example Weather for Cleanup Weather Accident Cost Location Environmental Damage Cause Consequence: Cost $ Environmental damage PR damage Accident Management Decisions Policy Decision Time

  16. Making Choices • Decision Trees • Example: Texaco vs. Pennzoil • Decision trees and expected value • certainty equivalent

  17. Decision Trees • Decision trees are a graphical representation of a decision problem Large return Venture succeeds Invest Funds lost Venture fails Do not invest Typical return earned on less risky investment

  18. Decision Tree Safety Cost Evacuate Safe High Storm hits Miami Forecast Danger Low Stay Safe Low Storm misses Miami

  19. Cash Flows and Probabilities • To each branch of the tree, we can attach • a probability • and/or, a cash flow • or any measure replacing monetary values for a specific problem

  20. Case Study: Texaco vs. Pennzoil • In early 1984, Pennzoil and Getty Oil agreed to the terms of a merger • Before the signature of the formal agreement, Texaco offered Getty a substantially better price , and Gordon Getty (majority stockholder) defected on Pennzoil and sold to Texaco

  21. Case Study: Texaco vs. Pennzoil • Pennzoil felt this was unfair practice and filed a lawsuit against Texaco, alleging that Texaco had interfered illegally in the the Pennzoil-Getty negotiations • Pennzoil won the case in late 1985 and was awarded $11.1 billion – the largest settlement in the US at this point in time • Texaco appealed and the settlement was reduced by $2 billion – but interest and penalty got the amount back to $10.3 billion

  22. Case Study: Texaco vs. Pennzoil • Kinnear, Texaco’s CEO, announced that Texaco would file for bankruptcy if Pennzoil obtained court permission to secure the judgment by filing liens against Texaco’s assets • Kinnear promised to fight the case all the way to the Supreme Court

  23. Texaco’s Offer • In April 1987, just before Pennzoil started filing liens, Texaco offered to pay Pennzoil $2billion to settle the entire case • Liedtke, chairman of Pennzoil, announced that his advisors estimated that a settlement of 3-5 billions would be fair What should Liedtke do?

  24. Decision Tree Settlement Amount ($billion) Accepts $2 billion 2 Texaco accepts $5 billion 5 Counteroffer $5 billion 10.3 Texaco refuses counteroffer Final Court Decision 5 0 10.3 Final Court Decision Texaco counteroffers $3 billion 5 Refuse 0 Accepts $3 billion 3

  25. Subjective Probabilities • In the decision tree, we are missing probability estimates of the each event • For this lecture, we will take these probability values for granted

  26. Decision Tree Settlement Amount ($billion) Accepts $2 billion 2 Texaco accepts $5 billion 5 (0.17) (0.2) 10.3 Counteroffer $5 billion Texaco refuses counteroffer Final Court Decision (0.5) 5 (0.3) 0 (0.50) (0.2) 10.3 (0.33) Final Court Decision (0.5) Texaco counteroffers $3 billion 5 Refuse (0.3) 0 Accepts $3 billion 3

  27. Expected Monetary Value • Computing an expected monetary value is a way of selecting among risky alternative • Computing expected values bring the problem back to a « certainty equivalent » • What is the expected value of the court judgment?

  28. Expected Value of the Court Judgment • EV = 0.2 * 10.3 + 0.5 * 5 + 0.3 * 0 • EV = $ 4.56 billion (0.2) 10.3 Final Court Decision (0.5) 5 (0.3) 0 It is possible to reduce the tree with this certainty equivalent

  29. Reduced Tree Accepts $2 billion 2 Texaco accepts $5 billion 5 (0.17) Counteroffer $5 billion Texaco refuses counteroffer 4.56 (0.50) (0.33) 4.56 Texaco counteroffers $3 billion Refuse Accepts $3 billion Eliminated 3

  30. Expected Monetary Value of the Counteroffer • What is the expected monetary value of Pennzoil $5 billion counter offer: • EV = P(Texaco accepts) * 5 + P(Texaco refuse) * 4.56 + P(Texaco counteroffers) * 4.56 • EV = 4.63 Liedtke should not accept the $2 billion offer, and should counter-offer $5 billion. If Texaco refuses, then the matter should be taken to court

  31. Reducing the Decision Tree • In practice, we do not reduce the decision tree but report expected values on the nodes

  32. Resolved Decision Tree Accepts $2 billion 2 Texaco accepts $5 billion 5 (0.17) Counteroffer $5 billion (0.2) 10.3 Texaco refuses counteroffer Final Court Decision (0.5) 4.56 5 (0.3) 4.63 0 (0.50) (0.2) 10.3 (0.33) Final Court Decision (0.5) Texaco counteroffers $3 billion 4.56 5 Refuse (0.3) 0 Accepts $3 billion 3

  33. Suggested Homework • Problem S2-10, p. 70 • Problem S2-13, p. 71

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