1. The 6 steps of the decision process are:
1. Clearly define the problem and the factors that influence it. 2. Develop specific and measurable objectives.
3. Develop a model.
4. Evaluate each alternative solution.
5. Select the best alternative.
6. Implement the solution.
2. The purpose of this question is to make students use a personal experience to distinguish between good and bad decisions. A “good” decision is one that is based on logic and all available information. A “bad” decision is one that is not based on logic and all avail- able information. It is possible for an unfortunate or undesired out- come to result from a “good” decision (witness a patient expiring after open-heart surgery). It is also possible to have a favorable or desirable outcome result from a “bad” decision (you win at Blackjack, even though you drew a card when you already held an “18”). 3. The equally likely model selects the alternative with the highest average value; it assumes each state of nature is equally likely to occur. 4. The basic difference between decision making under certainty, risk, or uncertainty is based on the nature and amount of chance or risk that is involved in making the decision. Decision making under certainty assumes that we know with complete confidence the outcomes that result from our choice of each alternative. Decision making under risk implies that we do not know the specific outcome that will result from our choice of a particular alternative, but that we do know the set of possible outcomes, and that we are able to objectively measure or estimate the probability of occurrence of each of the outcomes in the set. Decision making under uncertainty implies that we do not know the specific outcome that will result from our choice of a particular alternative; we know only the set of possible outcomes and are unable to
objectively measure or estimate the probability of occurrence of any of the outcomes in the set. 5. A decision tree is a graphic display of the decision process that indicates decision alternatives, states of nature and their respective probabilities, and payoffs for each combination of alternative and states of nature. 6. Decision trees can be used to aid decision making in such areas as capacity planning (Supplement 7), new product analysis (Chapter 5), location analysis (Chapter 8), scheduling (Chapter 15), and maintenance (Chapter 17). 7. EVPI is the difference between payoff under certainty and maximum EMV under risk. 8. Expected value with perfect information is the expected return if we have perfect information about the states of nature before a decision has to be made. 9. Decision tree steps:
1. Define the problem
2. Structure or draw the decision tree
3. Assign probabilities to the states of nature
4. Estimate payoffs for each possible combination of alternatives and states of nature 5. Solve the problem by computing the EMV for each state of nature node. 10. Maximax considers only the best outcomes, while maximin considers only worst-case scenarios. 11. Expected values is useful for repeated decisions because it is an averaging process. However, it averages out the extreme outcomes. A rational decision maker is concerned with these extreme outcomes and will incorporate them into the decision-making process. 12. Decision trees are most useful for sequences of decisions under risk. End-of-Module Problems
A.3 (a) EMV (large stock) = 0.3(22) + 0.5(12) + 0.2(–2) = 12.2 EMV (average stock) ’ 0.3(14) + 0.5(10) + 0.2(6) ’ 10.4 EMV (small stock) ’ 0.3(9) + 0.5(8) + 0.2(4) ’ 7.5 Maximum EMV is large inventory ’ $12,200
(b) EVPI ’ $13,800 – 12,200 ’ $1,600
where: $13,800 ’ 0.3(22) + 0.5(12) + 0.2(6)
A.4 Note: In the text, the states of nature appeared in the left column and the decision alternative across the top row. This is to let students know...