Critical Evaluation Of Pokemon Go Game: Game Theories, Implications, And Recommendations

Introduction to Pokemon Go Game

Discuss about the Game Evaluation Essay on Pokemon Go.

Pokemon Go is one of the most popular augmented reality or AR games that was developed by Niantic for the Android and iOS devices. It was initially released in few selected countries in July 2016. This game is the collaboration between Nintendo and Niantic (Serino et al. 2016). Pokemon Go game utilizes the GPS of mobile devices for the purpose of locating, capturing, battling and finally training various virtual creatures, known as Pokemon. This eventually appears like they are in the player’s real world location. This game is completely free to play and it utilizes a specific business model for supporting the in-app purchases in the additional in-game items.

The following essay outlines a brief discussion and the critical evaluation of Pokemon Go game. This critical evaluation is done with the significant intention to identify the various aspects, which would be working perfectly. Proper recommendations will also be provided for better improvement of the game. This type of improvement should be done significantly to increase the popularity of this game. The game design literature will be researched properly for evaluating the game significantly. Proper game theories would be considered for evaluation of this particular game.

Brief Description of the Game

The game of Pokemon Go is the augmented reality technology based game, which takes up around 20 million regular active users. This particular game eventually combines the geo spatial elements with the practices of gaming for incentivizing the movement of the user within the physical world (McCartney 2016). These incentives could comprise of the potential externalities for businesses that are associated with these. It is considered as one of the most popular and significant augmented reality technology based game that helps the user in capturing the Pokemon in various locations as per the GPS of the device of the user playing the game. Within the game, the Pokemons are widespread and has the automated procedure of searching them where they could be found so that they can easily appear to the user (Clark and Clark 2016). Almost 151 different types of Pokemons are present are in the game and all of them are to be found by the user. These varieties could be found in the Pokemon Gyms. These collected Pokemon has to fight within the Gyms. This is the entire scenario of the game of Pokemon Go.

Game Theories in Pokemon Go Game

Figure 1: Screenshot of Pokemon Go

(Source: Civicioglu 2013)

Game Design Literature for Locating Game Theories

There are some of the major game theories that are relevant to this particular game of Pokemon Go (LeBlanc and Chaput 2017). These game theories help the game in proper execution. The two significant game theories of Pokemon Go are given below:

Back tracking: The first and the foremost game theory that has been applied in the game of Pokemon Go is back tracking (Yang and Liu 2017). It is the most significant algorithm hat helps to find or search some of the major solutions for various computational problems; mainly constraint satisfaction problems, which incrementally build several candidates to the solutions and thus abandoning or back tracking the candidate. This candidate cannot complete the valid solution and this is determined perfectly. Back tracking helps in obtaining proper results by means of getting better and perfect solutions (Civicioglu 2013). Back tracking can be applied to the game of Pokemon Go since this particular game theory is applicable for various problems that admit the entire concept of the partial candidate solution and the relatively quicker testing of whether it provides valid solution or not. For an example, it is absolutely useless for locating the given value within an unordered table. When this is applicable, back tracking is much faster than the brute force enumeration of every complete candidate as it could eliminate the larger number of candidates with any one particular test (Civicioglu 2013). For the game of Pokemon Go, the candidates are the Pokemons. The constraint satisfaction problems like verbal arithmetic and crosswords could be easily solved with the help of back tracking. Hence, the game theory of backtracking is the most important and significant for the game of Pokemon Go.

Figure 2: Screenshot of User Finding Pokemon in Pokemon Go

(Source: Dorward et al. 2017)

Decision Tree: The second significant game theory for the game of Pokemon Go is decision tree (Dorward et al. 2017). This particular tree is the decision support tool, which eventually utilizes the tree like graph as well as model of decisions with their probable consequences that includes resource costs, utility and chance event outcomes. This decision tree is the most effective way of displaying the algorithm, which eventually comprises of conditional control statement. These decision trees are commonly utilized in the operations research, especially in the decision analysis for helping to identify a particular strategy to reach the goals and are the most popular tool within machine learning (Althoff, White and Horvitz 2016). The decision tree is the flowchart like structure, where every internal node subsequently represents the test on the attribute. Every branch thus represents the result of this test and each of the leaf nodes represents a significant class label. There are three types of nodes present within a specific decision tree. They are the decision nodes, chance nodes and end nodes (Rasche, Schlomann and Mertens 2017). For the game of Pokemon Go, the game theory of decision trees is extremely important and thus this would help them in attaining popularity and perfectness.

Implications of Game Theories in Pokemon Go Game

Figure 3: Screenshot of In Game Map

(Source: Tabacchi et al. 2017)

The game theories of back tracking as well as decision tree have several significant implications on the game of Pokemon Go (Zach and Tussyadiah 2017). For the game theory of back tracking, the major implications are given below:

The game theory of back tracking eventually enumerates a set of partial candidates, which could be completed in several methods for giving various solutions to the provided problem. This completion is done exponentially, by the sequence of candidate extension steps (Civicioglu 2013). For the game of Pokemon Go, the candidates are the Pokemons that are to be found out by the user. All of these candidates are then represented as the several nodes of the tree structure. This is the potential search tree.

The backtracking algorithm within the game of Pokemon Go, traverses the search tree recursively from root down in depth first order (Tabacchi et al. 2017). At every node, this algorithm will be checking whether the node could be completed to the valid solution or not.

For the game theory of decision tree, the major implications in Pokemon Go are given below:

It helps to identify the strategy in reaching out to the goal, mainly in machine learning. In the game of Pokemon Go, the user can be easily influenced with the decision analysis (Kari 2016). The diagrams are utilized as the visual as well as analytical decision support technique, where all the expected values of the competing options are evaluated. For this reason, the users are able to compete or participate in the game.

The various relevant and significant recommendations and suggestions for the improvements of this Pokemon Go game are as follows:

The first and the foremost recommendation for improving the game of Pokemon Go is that the graphical user interface or GUI should be improved for better result. This could be done with the usability tests.

The second recommendation for the improvement of the game of Pokemon Go is that the draining of battery should be stopped within the game. For the battery draining, this particular application often becomes an extremely important problem for the mobile phones. This should be checked for the longer excursions of hunting.

The third recommendation for the improvement of the game of Pokemon Go is the presence of social network of some kind. This social network presence can make the game more attractive and popular for all the users of this game and thus could be termed as the most effective of all.

Recommendations for Improving Pokemon Go Game

Conclusion

Therefore, from above discussion, conclusion can be drawn that Pokemon Go game is the application that eventually enables its users in creating as well as naming a character and thus partaking within an augmented mobile version of this game. This game was made popular with the help of Nintendo. When this application is installed within the phone, it utilizes the Geo location for identifying the location of the users. A particular map is provided that shows the nearby Pokemon from the users. This map then helps the user in finding their Pokemon and collecting points. The main premise of this game is to walk or ride to the Pokemon for capturing it and increasing the game status. The above essay has properly outlined the entire concept of the Pokemon Go game with a detailed critical evaluation. This particular game is extremely popular for every user and especially for the youth generation. The game evaluation is done on the basis of the various current game theories. These game theories properly explain the game with relevant and significant game theories and thus these theories are termed as the most relevant of all. Moreover, proper recommendations are also provided within the essay to improve the game and make more users friendly.

References

Althoff, T., White, R.W. and Horvitz, E., 2016. Influence of Pokémon Go on physical activity: study and implications. Journal of medical Internet research, 18(12).

Civicioglu, P., 2013. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), pp.8121-8144.

Clark, A.M. and Clark, M.T., 2016. Pokemon Go and research: qualitative, mixed methods research, and the supercomplexity of interventions.

Dorward, L.J., Mittermeier, J.C., Sandbrook, C. and Spooner, F., 2017. Pokémon Go: Benefits, costs, and lessons for the conservation movement. Conservation Letters, 10(1), pp.160-165.

Kari, T., 2016. Pokémon GO 2016: exploring situational contexts of critical incidents in augmented reality. Journal of Virtual Worlds Research, 9.

LeBlanc, A.G. and Chaput, J.P., 2017. Pokémon Go: A game changer for the physical inactivity crisis?. Preventive medicine, 101, pp.235-237.

McCartney, M., 2016. Margaret McCartney: game on for Pokémon GO. BMJ, 354, p.i4306.

Rasche, P., Schlomann, A. and Mertens, A., 2017. Who is still playing pokemon Go? a Web-based survey. JMIR serious games, 5(2).

Serino, M., Cordrey, K., McLaughlin, L. and Milanaik, R.L., 2016. Pokémon Go and augmented virtual reality games: a cautionary commentary for parents and pediatricians. Current opinion in pediatrics, 28(5), pp.673-677.

Tabacchi, M.E., Caci, B., Cardaci, M. and Perticone, V., 2017. Early usage of Pokémon Go and its personality correlates. Computers in Human Behavior, 72, pp.163-169.

Yang, C.C. and Liu, D., 2017. Motives matter: motives for playing Pokémon Go and implications for well-being. Cyberpsychology, Behavior, and Social Networking, 20(1), pp.52-57.

Zach, F.J. and Tussyadiah, I.P., 2017. To catch them all—the (un) intended consequences of Pokémon GO on mobility, consumption, and wellbeing. In Information and communication technologies in tourism 2017 (pp. 217-227). Springer, Cham.