# Question: Now reconsider the expanded version of the Silicon Dynamics problem

Now reconsider the expanded version of the Silicon Dynamics problem described in Probs.16.3-2 and 16.4-2.

(a) Use ASPE to construct and solve the decision tree for this problem.

(b) Perform sensitivity analysis systematically by generating a data table that shows the optimal policy and the expected payoff (when using Bayes’ decision rule) when the prior probability of selling 10,000 computers is 0, 0.1, 0.2, . , 1.

(c) Assume now that the prior probabilities of the two levels of service are both 0.5. However, there is some uncertainty in the financial data ($15 million, $6 million, and $600) stated in Prob. 16.2.2. Each could vary from its base value by as much as 10 percent. For each one, perform sensitivity analysis to find what would happen if its value were at either end of this range of variability (without any change in the other two pieces of data) by adjusting the values in the data cells accordingly. Then do the same for the eight cases where all these pieces of data are at one end or the other of their ranges of variability.

(a) Use ASPE to construct and solve the decision tree for this problem.

(b) Perform sensitivity analysis systematically by generating a data table that shows the optimal policy and the expected payoff (when using Bayes’ decision rule) when the prior probability of selling 10,000 computers is 0, 0.1, 0.2, . , 1.

(c) Assume now that the prior probabilities of the two levels of service are both 0.5. However, there is some uncertainty in the financial data ($15 million, $6 million, and $600) stated in Prob. 16.2.2. Each could vary from its base value by as much as 10 percent. For each one, perform sensitivity analysis to find what would happen if its value were at either end of this range of variability (without any change in the other two pieces of data) by adjusting the values in the data cells accordingly. Then do the same for the eight cases where all these pieces of data are at one end or the other of their ranges of variability.

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