Walt Disney Word’s Disney Genie+: The Payoff Matrix and Perfect Matching
Walt Disney Word’s Disney Genie+: The Payoff Matrix and Perfect Matching
After having worked at Walt Disney World this past summer, I really grew to appreciate the intricate process and immense detail that goes into each attraction and experience. Additionally, after having taken Networks, I noticed some similarities between the concepts learned in class and how Disney implements techniques in their parks. With hundreds of thousands of guests visiting the park each day, techniques such as perfect matching and the payoff matrix can be applied to create the most positive experience for the guests visiting the parks.
Recently, Walt Disney World Resort has implemented a new feature within their mobile app, the Disney Genie+. This is a feature that guests can purchase which has multiple aspects to help improve their experience and plan their day.
One feature of the Disney Genie+ is the “My Disney Genie Day” feature. Especially in parks like the Magic Kingdom, there is so much to do that it may be difficult for each guest to realistically get to each of their favorite attractions and eat at their preferred restaurants. This “My Disney Genie Day” allows the guests to be able to select certain attractions they would like to ride, shows they would like to watch, and characters that they would like to meet, as well as the times that they would like to do them. This feature can be beneficial, as it uses other user’s input in order to decide when the wait times will be highest and lowest at certain times of the day.
This concept, how the app can choose between the attractions, can be closely related to the payoff matrix. For each attraction in the park that the guest prefers, we can imagine there being a certain algorithm of the App, which knows each user’s input. We can think about the varying user input via a payoff matrix. In the payoff matrix below, there are two guests that the app is comparing, David and Yian, who have certain preferences of what they would like to do at 4 p.m. This matrix can represent the payoffs that are best maximized when they choose certain attractions (which are in the place of “strategies” in this example). With example numbers below, there is a Nash Equilibrium suggested of David riding “Jungle Cruise”, and Yian riding “Space Mountain”, which are both the best responses to the other guests in the park, to maximize their payoffs (minimize their wait times so they can best allocate their time throughout the day).
In addition to the “My Disney Genie Day”, one of the most crucial features, and one that provides a significant portion of revenue within the parks, is the Disney Genie+’s Lightning Lane. This feature costs the guests around $20 a day, varying with peak crowds during the holiday seasons and by park demand. This feature allows the guest to join a separate line that supposedly reduces their wait time. In order to keep people buying this experience, it is crucial that each ride is loaded efficiently and with maximum capacity.
For example, working in Attractions, Disney tries to maintain a guest flow with a certain number of guests riding each hour. This helps to keep wait times down as much as possible amidst the high crowds. In thinking about how to load these ride vehicles most efficiently, it may help to think about the scenario with regard to perfect matching, especially when there are certain height limits and preferences for each guest. For example, on Disney’s “Slinky Dog Dash”, the loading of the vehicles depends on a few factors. This can be demonstrated via the perfect matching diagram below:
In this scenario of loading the vehicle, perfect matching does not exist. When deciding who to place in the vehicle, there are certain ratios that must be used in order to make the Genie+ Lightning lane proportionately faster than the Standby. Additionally, each vehicle has a special seat, known as the TAV, which is wheelchair accessible. Only those with wheelchair access can sit in the TAV, and no other seat has wheelchair access. Therefore, in this scenario, it would make sense to fill the TAV, even if they are on standby. However, with this amount of people let through the waiting queue, there is 1 remaining seat, but 2 adults still waiting. Filling this last seat for perfect matching would depend on whether or not the party would like to separate.
Filling the ride vehicle is an almost perfect example of perfect matching. Each node (guest) would like preferences for specific slots (N(S) = Seats). Constricted sets may include certain parties that have particular preferences in which they don’t want to separate, or have children under 6 years old (who cannot ride alone). Therefore, to maximize effectiveness, it is important to fill the queue preceding this stage of the ride according to the various demographics of the guests in line. Being able to have a full vehicle for each round of loading demonstrates the importance of perfect matching knowledge within the context of Walt Disney World’s theme parks. The same concept can be applied to Disney’s other attractions, shows, restaurants, and hotels.
Overall, this blog post illustrates the real-world application of concepts from Networks, such as perfect-matching and the payoff matrix. Increased experience with these concepts can be beneficial to large hospitality and entertainment companies such as the Walt Disney World parks, which aims to both maximize efficiency and guest experience each day, on each attraction, and with each experience.
Additional Information Used:
https://www.travelweekly.com/Travel-News/Hotel-News/Disney-earnings-fiscal-Q1-2022