George Case
Above is the decision tree that was created for this decision based on the information given. Now, the different branches of the decision tree are based on the Georges assumptions of what could happen. At this juncture, there are basic variables that George cannot control: how many people show up at the event and how many of those people will end up buying one of his shirts. Now, while George does not have any control over these variables, we will provide the data on which he will base his decision.
First off, there is the variable of how many people will show up for the event. George believes there are three likely scenarios; he believes that there is a 50% chance that 50,000 people will attend the event, which leave 50% by which he believes can equally split between an attendance of 20,000 and 80,000 (Bodily, 481). The other variable focuses on the percentage of patrons that will buy his T-shirts. He believes that there is a 60% chance that 10% will buy one, a 30% chance that 5% will buy a shirt, and a 10% chance that 15% will buy his merchandise (Bodily, 481). This creates a dynamic, which can be seen in the decision tree, that in which there are nine different possibilities of which George believes could happen. He could have a low attendance rate, but a high T-shirt buyer rate. On the other hand, he could have a high attendance rate, but a low T-shirt buyer rate. I could also be one of the many alternatives in between.
To complicate things even further, Georges decision, includes the variable that he does control: buying the T-shirts, must be taken into account. He has the option of buying shirts in 5,000, 7,500, or 10,000 batches at $17,750, $25,250, and $32,125, respectively. If this decision were to be added to decision tree it would have to go at the very top of the decision tree and the whole decision tree seen above would have to be replicated under each different options resulting in the 27 options mentioned earlier.