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The management of Red Cat Computers was considering launching a new product called the Tiger 100. The problem was that if a certain competitor had developed a new technology, as had been rumored, they would quickly blow the Tiger out of the water resulting in a loss of $700,000. The board of directors assessed the chance that the competitor had this technology at 60%. If the competitor did not have this technology the board anticipated profits of $1,500,000. Bill Doors, the chairman of the board and company founder, was not happy making a launch/no launch decision with such a high degree of uncertainty and asked “how can we get information about whether they have this technology or not?” Duey Cheetum, his slimeball assistant said, “There are a few ways we can go about it. As you know, we have used one of their ex-employees, Tom Bayes, to assist us in making assessments before. I think he’ll have a 75% chance of getting it right if we ask him. Let’s incorporate that possibility in our decision tree. He’ll probably want about $6,000 for supplying his expert opinion. I wonder if it’s worth it because even after we get his view there will still be some uncertainty. You know, I’m sure that for $60,000 we could hire someone inside their company to tell us for sure.” Draw the tree for Red Cat’s decision problem and determine the optimum decision based on expected values. Include the possibility of launching or not: 1) without information, 2) with Tom’s input and 3) using the industrial spy. Repeat using an exponential utility function with R=$1,500,000.