Decision trees are used to model possible outcomes given a set of conditions, which makes them a great tool for determining which decision will yield the best results. They get more complicated when we do not have perfect information, as is the case with most decisions we have to make. For the case study, a restaurant owner knows there is a rezoning petition being voted on and needs to decide whether or not to hire a lobbyist to gauge the committee's likelihood of passing the rezoning. This decision tree is essentially determining the worth of the lobbyist's knowledge and the predicted financial outcome of either course of action. The company knows there is a 70% chance that the rezoning will go through if they lobby. Even with a favorable report from the lobbyist, they still need to decide which size property to buy, and after that there is still a chance event in what the board officially decides on the rezoning proposal. If the report is favorable, there is an 80% chance that the rezoning will go through. After inputting the probabilities and dollar values (measured in thousands of USD), the decision tree shows that lobbying is the best course of action in green, which in this case is lobbying. This outcome has an expected value of $39,300 which is greater than the $35,500 original expected value. The difference in these two expected values ($3,800) is the value of the lobbyist's report.