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Prisoner’s Dilemma in the Virtual world

                In class, the prisoner’s dilemma was brought up to get an idea of how dominant strategy works. The prisoner’s dilemma has been shown to pop up in the real world, for example in arm races between countries and the overfishing problem; it’s easy to see why even though there is an optimal solution for both parties, the net result is one where both parties do worse. However, this issue is not just isolated in reality and I realized that similar social dilemma often arises in video games, specifically in multiplayer games.

                The main reasons why many people play video games is to have fun, for the competition, or a little bit of both. However, even if you enjoy playing a game for fun rather than for competition, more often than not you will find winning much more enjoyable than losing. In many games, the most fun way to play will not be the most reliable to obtain a victory. This leads players to a dilemma in which they have to decide which strategy to pick that will counter the strategy picked by the opposing team. While competitive strategy can still be just as enjoyable as non-competitive strategies, there are times in games where the best strategy does not result in a fun time.

                Online multiplayer game developers have the job of having to constantly patch a game even if there are no visible bugs. This is due to the fact that a multiplayer game that stays consistent and unchanged will grow stale and slowly lose its player base. To remedy this problem, developers will add new content to the game and make small adjustments that can change the meta in small or big ways. Unfortunately, sometimes the changes in the meta of the game are big and result in a less fun meta whether or not the developer intended for this. For example, “in the early days of StarCraft, a strategy called “Zerg rushing” emerged where at the beginning of the match players would quickly build lots of cheap Zerg units to overwhelm opponents before defenses could be constructed” (Madigan 2010). Before developer patches, this was the dominant and most used strategy of the game, even if it was not fun to play as or to play against. The prisoner dilemma perfectly explains why players kept using this strategy even though it was not every enjoyable.

Example of a Zerg rush
Zerg rush pay off matrix

From the matrix above, one can see why Zerg rushing became so common. The dominant strategy for both sides is to Zerg rush and is a strategy that is strictly better than all other options, regardless of what other players do. While a game where neither player Zerg rushes would be ideal, if one player chooses not to Zerg rush, the other player will have more incentive to Zerg rush since they would have more enjoyment dominating the game than they would in a normal match. As a result, both players Zerg rush and the games are unsatisfying to play.

                Another issue that comes from developers patching a game and adding new content is the inevitable bugs that come along with that content. Sometimes these bugs and glitches will be small and not usually have much impact on the game, but there are times when exploiting these bugs is a legitimate strategy that results in a more likely victory. For instance, “some players of the online first-person shooter Modern Warfare 2 discovered what became known as “the javelin glitch.” Someone, somewhere, somehow figured out that through a bizarre sequence of button presses you could glitch the game so that when you died in multiplayer you would self destruct and murder everyone within 30 feet, often resulting in a net gain in points” (Madigan 2010). Modern Warfare players end up in a similar dynamic as the Zerg rush problem where they have to decide which strategy will result in a more positive outcome.

Example of Javelin Glitch
Javelin Glitch pay off matrix

                Once again, even though not exploiting the glitch would result in fair play that is optimal for both parties, instead the more common route was mayhem where all players exploited this glitch. This was so common in fact that Infinity Ward had to rush out a patch to stop it from being exploited any further. Using the same logic as the prisoner’s dilemma we can see that the dominant strategy for all players would be to glitch. The players would rather have a broken match than be dominated by opposing players.

                In conclusion, the prisoner’s dilemma and game theory allow for a better understanding of social dilemmas in not just the real world, but also the virtual world. I believe that game developers at the very least can use this information to prevent players from having to be put in future dilemmas, such as by banning players that exploit bugs so that the pay off matrix  will result in a dominant strategy that is fun for all players.

Sources:

Madigan, J., Says. (2013, July 30). The Glitcher’s Dilemma: Social Dilemmas in Games. Retrieved November 18, 2020, from https://www.psychologyofgames.com/2010/03/279/

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Using Graphs to Recommend Champions (League of Legends)

In class, we learned about the various uses for graphs and Dijkstra’s algorithm. This made me consider how graphs are used in video games, considering the clear and visible variables that they use, and how much easier it would be to collect data from a game than it would be in real life. Graphs can help find relationships with variables and Dijkstra’s algorithm can be used to find the maximum distance between those variables. One use of these tools relating to video games would be for recommendations. Specifically, I will discuss about champion recommendation in the video game League of Legends.

League of Legends is Multiplayer Online Battle Arena (MOBA) game where two teams of players battle each other with characters called Champions. Players select Champions based on the roles they prefer to take to aid their team in the fight. There is a large inventory of Champions players can choose from. To ensure players try a variety of Champions, the developer of the game may wish to recommend other Champions that may suit the play style of the player. A naïve way to recommend Champions would be to compare the key attributes between the player’s most used Champion and other Champions in the Champion roster, finding one that shares the most features. However, using graph theory, it is possible to generate a network to find recommendations using the player base instead of the Champions themselves.

Suppose a player, who enjoys playing with Champion A, also enjoys playing with Champion B. Then it is very likely other players who enjoy playing Champion A may enjoy playing with Champion B. However, to generate a graph that strongly supports these predictions would require a lot of player data. Luckily, the game has a large player base, reaching 100 million unique monthly players at one point in 2017 (Spezzy 2020).

Typical attribute-based recommendation

By having every Champion in the game be represented as a node and the weighted connection between two Champions be represented by the edges of the graph, we can find a much better representation of champion picks in general. These connections can be found by using the most played champions for every player in a given sample. If two champions appear in the same top five most played champions for a player, it can be concluded that there is a connection between them.

Example of connections per champion (Williams 2020)

While a graph like this would seem useful at first glance, it has its issues stemming from the mechanics of the game. Recommendations from this graph will most likely recommend champions who are in the same role as other champions since players tend to dedicate themselves to playing certain positions. A quick fix to this problem is to find each player’s top three Champions per role instead. We can then use Dijkstra’s algorithm to find champions with minimal distance from each other to give out much better recommendations than we would have with an attribute-based recommendation system.

Recommendation graph that skewed towards champions of the same position (Williams 2020)

In conclusion, graphs and Dijkstra’s algorithm allow for much better champion recommendations by taking advantage of the large amount of data from video games. I believe that this idea could also be extended for other recommendations services by checking what items or choices are chosen by users in common and recommending them to other users.

Sources:

Williams, J. J. (2020, September 20). Graph Networks for Champion Recommendation (League of Legends). Retrieved October 15, 2020, from https://towardsdatascience.com/graph-networks-for-champion-recommendation-league-of-legends-189c8d55f2b

S. (2020, August 03). Did you know? Total League of Legends Player Count – UPDATED 2020. Retrieved October 15, 2020, from https://leaguefeed.net/did-you-know-total-league-of-legends-player-count-updated-2020/