The Hong Kong University of Science and Technology

ISOM 390

Decision Tree Decision Making under Uncertainty: Recognize all possible states of the world, If possible, estimate the probability for each state Evaluate outcomes (in each state) of each action; Rank actions; Collect information only if it is worthy Example: Marketing a Movie Maximax: Best of the best = Maximum possible result  Maximize the most optimal result; Maximin: Best of the worst; Minimin: Minimum of the minimum; Laplace (Equal Likelihood): Best/ Maximum of the average Minimax Regret: Least of the worst regret = Loss because you didn’t choose the best option; Regret = The most can get – Actual Get The appropriate criterion depends on the “risk” attitude, personality, and philosophy of the decision maker Decision Making with Probabilities: Not probability in the sense of frequencies; Best guesses, or subjective probability E.g. The management assesses that the probability (P) for this new production to be a hit is 30%; ↓Decision Tree without Information↓ Event = Not your choice; But depends on the state of the world Decision Point = Make Choice; Need to tell Optimal Choice Expected Profit of “Action” = 0.3 x 25m + 0.7 x -10m = 0.5m Sensitivity Analysis (P is unknown) Expected-value: best of the weighted average Action is the best choice if 35p-10 > 20p-5 and 35p-10 > 5  p > 3/7 Comedy is the best choice if 20p-5 > 35p-10 and 20p-5 > 5 TV network is the best choice if 5 ≥ 35p-10 and 5 ≥ 20p-5  p < 3/7 Example: Surgery A patient is deciding whether and when to have a surgery. If she does not have a surgery, her payoff is 40. If she has a surgery now, her payoff is as follows: She can also postpone the decision by five years. By then possible advancements in technology may improve the surgery outcome. Her payoff of having a surgery in fiv