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TM 732 Engr. Economics for Managers

TM 732 Engr. Economics for Managers. Decision Analysis. GoferBroke. Prototype Ex. 2.

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TM 732 Engr. Economics for Managers

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  1. TM 732Engr. Economics for Managers Decision Analysis

  2. GoferBroke

  3. Prototype Ex. 2 Digger Construction is interested in purchasing 1 of 3 cranes. The cranes differ in capacity, age, and mechanical condition, but each is fully capable of performing the jobs expected. The firm anticipates a growing market and that there will be sufficient demand to justify each of the cranes. However, low, medium, and high growth estimates result in different cash flow profiles for each crane. Based on ATCF at 15%, the analyst estimates the following NPWs for each of the cranes for each of the growth market conditions.

  4. Digger Construction

  5. Decision Matrix EUAW

  6. Matrix Decision Model Aj = alternative strategy j under decision makers control Sk = a state or possible future that can occur given Aj pk = the probability state Sk will occur

  7. Matrix Decision Model V(jk) = the value of outcome jk (terms of $, time, distance, . . ) jk = the outcome of choosing Aj and having state Sk occur

  8. Decisions Under Certainty

  9. Decisions Under Certainty Investor wishes to invest $10,000 in one of five govt. securities. Effective yields are: A1 = 8.0% A2 = 7.3% A3 = 8.7% A4 = 6.0% A5 = 6.5% choose A3.

  10. Maximin Select Aj: maxjminkV(jk) e.g., Find the min payoff for each alternative.

  11. Maximin Select Aj: maxjminkV(jk) e.g., Find the min payoff for each alternative. Find the maximum of minimums Select Crane 1 Choose best alternative when comparing worst possible outcomes for each alternative.

  12. Maximin Select Aj: maxjminkV(jk) e.g., Find the min payoff for each alternative. Find the maximum of minimums Sell Land Choose best alternative when comparing worst possible outcomes for each alternative.

  13. MiniMax Select Aj: maxjminkV(jk) e.g., Find the max payoff for each alternative.

  14. MiniMax Select Aj: maxjminkV(jk) e.g., Find the max payoff for each alternative. Find the minimum of maximums Select Crane 1 Choose worst alternative when comparing best possible outcomes for each alternative.

  15. MiniMax Select Aj: maxjminkV(jk) e.g., Find the max payoff for each alternative. Find the minimum of maximums Sell Land Choose worst alternative when comparing best possible outcomes for each alternative.

  16. Class Problem Choose best alternative using a. Maximax criteria b. Minimin criteria

  17. Class Problem Choose best alternative using a. Maximax criteria (best of the best) maxj{15163, 16536, 18397} = 18,397 choose A3

  18. Class Problem Choose best alternative using a. Minimin criteria (worst of the worst) minj{11,962 10,934 10,840} = 10,840 choose A3

  19. Maximum Likelihood Assume S2 a certainty

  20. Maximum Likelihood Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  21. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  22. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  23. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  24. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  25. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  26. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  27. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  28. Most Probable Assume S2 a certainty max{PA1, PA2, PA3 | p2 =1.0} choose A1

  29. Maximun Likelihood Most Probable Assume S2 a certainty max{PA1, PA2| p2 =1.0} choose A2

  30. Bayes’ Decision Rule E[A1] > E[A2] > E[A3] choose A1

  31. Bayes’ Decision Rule E[A1] > E[A2] choose A1

  32. Expectation E[A1] > E[A2] > E[A3] choose A1

  33. Expectation E[A1] > E[A2] > E[A3] choose A1

  34. Expectation E[A1] > E[A2] > E[A3] choose A1

  35. Expectation E[A1] > E[A2] > E[A3] choose A1

  36. Expectation E[A1] > E[A2] > E[A3] choose A1

  37. Expectation E[A1] > E[A2] > E[A3] choose A1

  38. Laplace Principle If one can not assign probabilities, assume each state equally probable. Max E[PAi] choose A1

  39. Expectation-Variance If E[A1] = E[A2] = E[A3] choose Aj with min. variance

  40. Sensitivity Suppose probability of finding oil (p) is somewhere between 15 and 35 percent.

  41. Sensitivity Suppose probability of finding oil (p) is somewhere between 15 and 35 percent.

  42. Sensitivity Suppose probability of finding oil (p) is somewhere between 15 and 35 percent.

  43. Sensitivity

  44. Sensitivity Plot 200 150 Drill Expected Value 100 Sell 50 0 0 0.1 0.2 0.3 0.4 Prob. of Oil Sensitivity

  45. Sensitivity We know E[payoff] = 700(p) -100(1-p) = 800p - 100

  46. Sensitivity Plot 200 150 Drill Expected Value 100 Sell 50 0 0 0.1 0.2 0.3 0.4 Prob. of Oil Sensitivity

  47. Aspiration-Level Aspiration: max probability that payoff > 60,000 P{PA1 > 60,000} = 0.8 P{PA2 > 60,000} = 0.3 P{PA3 > 60,000} = 0.3 Choose A2 or A3

  48. Aspiration-Level Aspiration: max probability that payoff > 60,000 P{PA1 > 60,000} = 0.8 P{PA2 > 60,000} = 0.3 P{PA3 > 60,000} = 0.3 Choose A2 or A3

  49. Class Problem Determine alternative Aj if aspiration level is NPW > $14,000.

  50. Class Problem Determine alternative Aj if aspiration level is Payoff > $14,000.

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