Chapter 5 Supplement Decision Theory

Decision Theory Elements A set of possible future conditions exists that will have a bearing on the results of the decision A list of alternatives for the manager to choose from A known payoff for each alternative under each possible future condition

Decision Theory Process Identify possible future conditions called states of nature Develop a list of possible alternatives, one of which may be to do nothing Determine the payoff associated with each alternative for every future condition

Decision Theory Process (Cont’d) If possible, determine the likelihood of each possible future condition Evaluate alternatives according to some decision criterion and select the best alternative

Causes of Poor Decisions Bounded Rationality The limitations on decision making caused by costs, human abilities, time, technology, and availability of information

Causes of Poor Decisions (Cont’d) Suboptimization The result of different departments each attempting to reach a solution that is optimum for that department

Decision Environments Certainty - Environment in which relevant parameters have known values Risk - Environment in which certain future events have probable outcomes Uncertainty - Environment in which it is impossible to assess the likelihood of various future events

product and service design Decision Theory Decision Theory represents a general approach to decision making which is suitable for a wide range of operations management decisions, including: capacity planning product and service design equipment selection location planning

Possible future demand* Payoff Table Possible future demand* Present value in $ millions

Decision Making under Uncertainty Maximin - Choose the alternative with the best of the worst possible payoffs Maximax - Choose the alternative with the best possible payoff Laplace - Choose the alternative with the best average payoff of any of the alternatives Minimax Regret - Choose the alternative that has the least of the worst regrets

Format of a Decision Tree State of nature 1 B Payoff 1 State of nature 2 Payoff 2 Payoff 3 2 Choose A’1 Choose A’2 Payoff 6 Payoff 4 Payoff 5 Choose A’3 Choose A’4 Choose A’ 1 Decision Point Chance Event

Expected Value of Perfect Information Expected value of perfect information the difference between the expected payoff under certainty and the expected payoff under risk Expected value of Expected payoff Expected payoff perfect information under certainty under risk _ =

Sensitivity Analysis Example 8 16 14 12 10 8 6 4 2 A B C A best C best B best #1 Payoff #2 Payoff Sensitivity analysis: determine the range of probability for which an alternative has the best expected payoff Example 8

Solved Problem 5