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A small start-up company must make additional investments to maximize its firm value. But the company owner will not make this investment unless she can renegotiate outstanding debt claims. Solving this “debt overhang” problem through negotiation is the focus of the case. In this context, students are exposed to a variety of issues: the nature of financial claims, bargaining and negotiation fundamentals, and agency costs of debt.

Two recently graduated MBA students are tasked with developing an ad-serving learning algorithm for a mobile ad-serving company. The case illustrates the way in which hypotheses can be tested in an A/B format or “horse race” in order to establish customer preferences and superior profitability. The case was written for a course elective covering hypothesis testing.

Set in 1999, this case allows students to put themselves in the positions of both Airbus and Boeing as Boeing considered how to respond to Airbus's decision to announce its plans to proceed or not with the $10 billion development of the world's first commercial superjumbo jet, the Airbus A3XX. Boeing was considering a development effort to “stretch” its 747 jumbo jet into a larger superjumbo version, the 747-X. At the time, the two companies’ widely available 20-year forecasts for jumbo- and superjumbo-jet demand were particularly divergent. In light of this very public “agreement to disagree,” Boeing could pursue several alternatives, all of which were related to Airbus's decision about whether or not to develop the A3XX. This case presents an opportunity for students to make a real downstream decision. It was prepared as a final exam for an introductory decision analysis course involving subjective probability assessment, decision tree modeling, simulation, real options, and game theory. In the analysis of this case, a student is expected to utilize ideas from all five of these areas.

The case evolves around the Powerball lottery and the rule changes implemented in 2015, which, among other things, changed the chances of winning the jackpot from 1 in 175 million to 1 in 292 million. What is the impact of such rules on lottery revenues? The expected value rule is unable to explain why people play in the first place and fails to give the appropriate weight to the factors that explain the attractiveness of a lottery. This case is ideal to introduce the notion of decision weights as put forward by Kahneman and Tversky's prospect theory. By calculating decision weights, we obtain a reasonable prediction for the willingness to pay for the lottery as a function of different jackpot amounts. Using past data, we can correlate lottery revenues with predicted willingness to pay for a ticket. Quantitative-inclined audiences can then develop a simulation model of how likely it is that the jackpot grows, which, coupled with the prediction of revenues as a function of the jackpot, would give the evolution of the revenues under the new rule. The accompanying spreadsheet provides data for students to work out various scenarios to narrow objectives and maximize revenue from Powerball tickets.