Injustice and Balance in Pervasive Video Games

• Main Author: João Álvaro Cardoso Soares Ferreira
• Publication Date: 2021
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/10216/135533
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

Being an art medium closely tied with the advancement of technology, some of the most interesting video games get made by expanding the technical limits of play in many new ways. Just like the jump from 2D graphics to 3D, how the internet shaped multiplayer games and inter-connectivity between players, and the way that now Virtual Reality and Augmented Reality are changing what is possible in a virtual world, another game genre has grown in popularity with the advent of new, exciting technology - pervasive games. The genre of pervasive games encompasses games that merge the game's virtual world and the real world together by taking advantage of the player's location data and contextual information, using the new leaps in technology regarding mobile internet. The phenomenon of Pokémon GO spearheaded the genre's break into the mainstream, allowing many other similar games to thrive and carving for itself a really large audience worldwide. Unlike augmented reality games - that expect you to see the world through a camera, that it then applies elements of the virtual world to - pervasive games uses the data from the real world as its base. There is a lot of cross-over between these two genres, like seeing the dinosaurs in Jurassic Park Alive through your phone after finding them in a local park. A big issue with pervasive games is central to its claim-to-fame: due to being so context and location-dependent, pervasive games are bound to have biases that make some contexts much better than others. A prime example of this is how in Pokémon GO, players in rural areas have nearly no Pokémon spawns, while players in cities have a constant stream of new monsters to catch. To solve this, this dissertation aims to develop an analytics platform for pervasive games that allows developers to obtain feedback on their game's balance. This goal will be achieved by cross-referencing player data and context data, using machine learning techniques to discover what works and what doesn't. This work's goal isn't simply to offer a tool to developers that help balance their games but also to help solve a significant issue holding pervasive games back from experimenting with more complex and deep experiences.