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Essential Steps in Decision-Making: Theory and Practice

This chapter introduces the key principles and methods in decision analysis, helping students learn the systematic approach to problem-solving. It outlines the six essential steps of decision-making, the different decision-making environments (certainty, risk, and uncertainty), and how to effectively make decisions under these conditions. Students will explore utility theory, Bayesian analysis, and learn to calculate expected monetary values (EMV) and expected value of perfect information (EVPI) to enhance their decision-making skills.

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Essential Steps in Decision-Making: Theory and Practice

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  1. Chapter 3 Decision Analysis Prepared by Lee Revere and John Large 3-1

  2. Learning Objectives Students will be able to: • List the steps of the decision-making process. • Describe the types of decision-making environments. • Make decisions under uncertainty. • Use probability values to make decisions under risk. • Understand the importance and use of utility theory in decision theory. 3-2

  3. Chapter Outline 3.1Introduction 3.2 The Six Steps in Decision Theory 3.3 Types of Decision-Making Environments 3.4 Decision Making under Uncertainty 3.5 Decision Making under Risk 3.7 How Probability Values Are Estimated by Bayesian Analysis 3.8 Utility Theory 3-3

  4. Introduction • Decision theory is an analytical and systematic way to tackle problems. • A good decision is based on logic. 3-4

  5. The Six Steps in Decision Theory • Clearly define the problem at hand. • List the possible alternatives. • Identify the possible outcomes. • List the payoff or profit of each combination of alternatives and outcomes. • Select one of the mathematical decision theory models. • Apply the model and make your decision. 3-5

  6. John Thompson’s Backyard Storage Sheds 3-6

  7. Decision Table for Thompson Lumber 3-7

  8. Types of Decision-Making Environments • Type 1: Decision making under certainty. • Decision makerknows with certaintythe consequences of every alternative or decision choice. • Type 2: Decision making under risk. • The decision makerdoes knowthe probabilities of the various outcomes. • Decision making under uncertainty. • The decision makerdoes not knowthe probabilities of the various outcomes. 3-8

  9. Decision Making under Uncertainty • Maximax • Maximin • Equally likely (Laplace) • Criterion of realism • Minimax 3-9

  10. Decision Table for Thompson Lumber • Maximax: Optimistic Approach • Find the alternative that maximizes the maximum outcome for every alternative. 3-10

  11. Thompson Lumber: Maximax Solution 3-11

  12. Decision Table for Thompson Lumber • Maximin: Pessimistic Approach • Choose the alternative with maximum minimum output. 3-12

  13. Thompson Lumber: Maximin Solution 3-13

  14. Thompson Lumber: Hurwicz • Criterion of Realism (Hurwicz) • Decision maker uses a weighted average based on optimism of the future. 3-14

  15. Thompson Lumber: Hurwicz Solution CR = α*(row max)+(1- α)*(row min) 3-15

  16. Decision Making under Uncertainty • Equally likely (Laplace) • Assume all states of nature to be equally likely, choose maximum Average. 3-16

  17. Decision Making under Uncertainty 3-17

  18. Thompson Lumber;Minimax Regret • Minimax Regret: • Choose the alternative that minimizes the maximum opportunity loss . 3-18

  19. Thompson Lumber:Opportunity Loss Table 3-19

  20. Thompson Lumber:Minimax Regret Solution 3-20

  21. Decision Making under Risk Expected Monetary Value: In other words: EMVAlternative n = Payoff 1 * PAlt. 1 + Payoff 2 * PAlt. 2 + … + Payoff n * PAlt. N EMV= payoff of state of nature* probability of state of nature 3-21

  22. Thompson Lumber:EMV 3-22

  23. Thompson Lumber: EV|PI and EMV Solution 3-23

  24. Expected Value of Perfect Information (EVPI) • EVPI places an upper bound on what one would pay for additional information. • EVPI is the expected value with perfect information minus the maximum EMV. 3-24

  25. Expected Value with Perfect Information (EV|PI) In other words EV׀PI = Best Outcome of Alt 1 * PAlt. 1 + Best Outcome of Alt 2 * PAlt. 2 +… + Best Outcome of Alt n * PAlt. n 3-25

  26. Expected Value of Perfect Information Expected value with no additional information Expected value with perfect information EVPI = EV|PI - maximum EMV 3-26

  27. Thompson Lumber:EVPI Solution EVPI = expected value with perfect information - max(EMV) = $200,000*0.50 + 0*0.50 - $40,000 = $60,000 • It means that if the cost of information less that 60000 we’ll accept to pay for getting information • Otherwise refuse. From previous slide 3-27

  28. In-Class Example 2 Let’s practice what we’ve learned. Using the table below compute EMV, EV׀PI, and EVPI. 3-28

  29. In-Class Example 2: EMV and EV׀PI Solution 3-29

  30. In-Class Example 2:EVPI Solution EVPI = expected value with perfect information - max(EMV) = $100,000*0.25 + 35,000*0.50 +0*0.25 = $ 42,500 - 27,500 = $ 15,000 3-30

  31. Expected Opportunity Loss • EOL is the cost of not picking the best solution.EOL = Expected Regret 3-31

  32. Thompson Lumber: EOLThe Opportunity Loss Table 3-32

  33. Thompson Lumber: EOL Table 3-33

  34. Thompson Lumber: EOL Solution 3-34

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