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Homework due next Tuesday, September 22

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. Chapter 5: Decision-making Concepts. Quantitative Decision Making with Spreadsheet Applications 7 th ed.

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Homework due next Tuesday, September 22

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  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/groebner6ed/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) Probabilities Payoffs

  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) EV=200(.3)+50(.5)+(-120)(.2)=61 200 Large factory Stable Economy (.5) 50 Weak Economy (.2) -120 (.3) Strong Economy EV=90(.3)+120(.5)+(-30)(.2)=81 90 Average factory (.5) Stable Economy 120 (.2) Weak Economy -30 (.3) Strong Economy EV=40(.3)+30(.5)+20(.2)=31 40 Small factory (.5) Stable Economy 30 (.2) Weak Economy 20

  11. Make the Decision Strong Economy (.3) EV=61 200 Large factory Stable Economy (.5) 50 Weak Economy (.2) -120 (.3) Strong Economy 90 EV=81 Maximum EV=81 Average factory (.5) Stable Economy 120 (.2) Weak Economy -30 (.3) Strong Economy EV=31 40 Small factory (.5) Stable Economy 30 (.2) Weak Economy 20

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