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Analyzing Film Release Times Using Game Theory

No matter how high quality a film is, if it is not marketed properly or released at a right time, it may get hit by low sales. Many things can influence the release date of a film. Genre of a film is one of the most important things that affects its release time as people are used to certain types of movies in certain seasons. That is why we usually get blockbuster action movies in Summer, critically acclaimed dramas in Fall, scary and horror movies closer to Halloween and comedy/holiday theme movies closer to new year holidays. However, genre of a film is not the only thing that greatly has an influence on the release date. Publishers also try their best to make sure they release their films farther apart from each other so that they do not have to compete with each other in box office revenues. That is the reason researchers from Shanghai University of Finance and Economics, decided to explore the idea of using Game Theory in order to see if they can maximize the revenues of multiple studios competing with each other for release time slots and whether or not there exists a pure Nash equilibrium for such scenarios.

In their model, they used n players (film publishers), who are selfish and always trying to maximize their revenue. They decide the release date of the film and only have one film to publish. They used a utility function u_i(a, delta) which defines the number of audience who watches player i’s film.

In the function above, a is the action profile. There are M available time slots and a_i is in M. delta_i is the popularity degree of the film i. C_j is the set of players who chose the time slot j. The demand of the audience in j-th time slot is denoted by d_j.

Using the function above, they are able to calculate the utility (revenue) a player earns in each time slot and are able to create a payoff matrix based on the actions each player chooses. After creating the payoff matrix, the researchers then had to prove that a pure Nash equilibrium exists in the matrix. In order to do that they created a set s that defines the strategy profiles of all the players. s_i is the strategy profile of player i, s_-i is the strategy profile of all players except i.

Proving the equation above means that, there is a strategy s^* that no player can increase its utility by changing its release time slot. Therefore, there exists a Nash equilibrium.

Using the utility function and the payoff matrix created by the utility function they were able to prove the above equation which led to this theorem:

“In the attraction competition game, there always exists a pure Nash equilibrium. If players choose the release time greedily in the decreasing order of the popularity degree, the reached schedule is a Nash equilibrium.”

As we saw in the example above, Game Theory can be used to analyze many real life decisions which can lead to better decision making and profit to parties that use it efficiently.

References:

https://arxiv.org/abs/1910.13095

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Using Network Analysis to Better Understand Virtual Communities in MMORPGs

MMORPGs are one the most popular and interesting genres in the video game industry. World of Warcraft, Black Desert Online and Destiny have been prominent examples of this genre for years. One the most unique features of MMORPGs are the virtual communities, most of the time referred to as Clans, that are created due to the nature of this genre, which promotes completing missions and quests with other players. One thing that has been interesting to me about these communities, are the characteristics and personalities of them and how they differ from each other. How do they recruit players and communicate with each other? Fortunately for us, there have been a research done by Markus Schatten and Bogdan Okreša Ðuri ́c from University of Zagreb about this exact subject by using analytical techniques and graph theories we are learning in CSCC46.

In this study, they observed and collected data from players, which were mostly students from three countries, for one month. After data collection, they used social network analysis (SNA) techniques to find patterns of organizational behavior among successful players.

The analysis of their data found multiple interesting player behaviors. For example, players usually describe their relationship with other players using characteristics like, Friend, Enemy and Blacklist. Another finding showed that communities are usually created among people who have the same interests. For example, players who try to complete the same mission, acquire the same items or have similar goals usually tend to group together. Analyzing chats and communications also showed that people who are on the same or similar real-life geographical locations, tend to use in game chat and whisper functionalities with each other more than others.

This study shows that analyzing player behaviors in games, specially player to player behaviors, can show many interesting facts which can be very useful to game developers to better optimize and design their games for their audience. It can also help scientists better understand how people communicate and interact with each other in virtual worlds.

Source:

https://bib.irb.hr/datoteka/796277.Proceedings_IEEE_9828a037.pdf