Application of Bayes’ Theorem in the Business of Social Games and Casino
In the article “The Business of Social Games and Casino,” Lloyd Melnick discusses how to succeed in mobile game space by applying Bayes’ Theorem. In the game space, the green light decision is when a company decides whether or not to fund fully a project and put it into production by using a highly defined process or intuition. Rather than struggling between those two tactics, Melnick contends that the Bayes’ Theorem shows that the best information for green light decision making is making use of the data from all previous game releases rather than focusing on the game product itself. From the information given in the previous game releases, there are four areas to look at: mechanic, theme, platform, and business platform. The mechanic is the type of game a company creates. By comparing the results of games of the same or similar mechanic category, companies can calculate the probability of success. The theme of the game reveals the players’ preferences, which can provide a higher likelihood of success. The platform at which the project is to be launched is also a determinant of success. For example, a company’s game may look great on Windows but if only 10 percent of social games are successful on Windows in comparison to 85 percent profitability on Android, the company should launch its game on Android. Lastly, the business model such as a game that is a paid download or free may affect success. By leveraging all of the information given through these categories, companies can make the best green light decision.
This article relates to the application of Bayes’ Theorem in creating general models for information cascade. This lesson is similar to the article in terms that it not only uses Bayes’ Theorem to calculate probabilities but also piece together existing incomplete information to form a logical conclusion. Information cascades occur when people copy decisions of others, not what they see or knew, because they convey information and update their own beliefs. The ubiquitous marble guessing game is an example of information cascade. In the marble guessing game, there are two bags: majority blue or majority red. People draw a marble from a bag, note the color (red or blue), put the marble back into the bag, and make a public guess about whether the bag is majority blue or red. At the end of the game, people who guess correctly receive money. The results of this game are a cascade of people who choose blue altogether or red, based on the prior guesses of the first three people. In the same notion as the article, the Bayes’ Theorem reveals mathematical reasoning under uncertain circumstances of information cascades – interpreting evidence in the context of previous experience or knowledge and transforms that probability into useful conjecture.