Chapter One

In a relatively new, swift moving industry, much of the information available surrounding gaming, narratology, game uptake and the economic market is online.  While a great deal of information can be gathered from games, the very size of big data is in fact part of the issue with its gathering and utilisation, as “vast amounts of data are generated as people go about their daily activities” (Halfpenny & Proctor, 2015, p3).  Digital research in terms of the larger research institutions still shows limited use of games data; instead, big data collated from gameplay has broached the remit of sales companies such as Microsoft that have a vested sales interest in engaging with the collation. “Using behavioral data to update the game according to what is working well means making games that are challenging, but not something users get frustrated with and want to walk away from” (Rands, 2018).  While the economic returns of perfecting the most addictive styles of gameplay are perfectly obvious, less clear are the disadvantages of this approach; put simply, gameplay perfectionism is liable to lead to reduction in experimentation.  Small independent games designers in the 2018 market are regularly testing new gaming concepts, and this must be acknowledged as a strength within the industry. Games such as That Dragon, Cancer (2016) written by the parents of a terminally ill child and designed to draw a player into an awareness of the experience of having a terminally ill child, are using a games medium because it is interactive, and can potentially depict this better than another media perhaps might.

What is clear, is that videogaming is already a major contributor in terms of collated big data, and the potential is there for even greater return, causing a surge of interest amongst economic forces; “with terabytes of generated data from different sources per nanosecond, the internet is chock full of content to be gamified” (Reality Games, 2018).  So how could gaming data be utilised? The collation of gaming data can and probably will be used both to develop information on the number, size and profitability of longer term games servers, but also to collect and analyse social and experimental information.  The games Elite Dangerous (2014) and No Man’s Sky (2016) for example both consist of a space-based open world construct designed to unfold on similar principles to the way real space would unfold to a spaceship pilot.  To develop the mechanics of Elite Dangerous, the developers entered a series of space-based rules into the mechanic, and then allowed ‘space’ to unfold at random.  Players therefore had the sense of genuinely exploring unknown territory; a game such as this could perhaps be used to examine different options available in how to undertake the task of exploring space, particularly with the addition of realistic scaled travel distances.  “The potential benefits to researchers are enormous, offering opportunities to mount multidisciplinary investigations into major social and scientific issues on a hitherto unrealizable scale by marshalling artificially produced and occurring ‘big data’ of multiple kinds from multiple sources” (Halfpenny & Proctor, 2015, p3).

Reality Games is an online company proposing a business model that starts off making games with the sole aim of scraping data that can be utilised to generate income from players in-game.  While this model already exists in several forms, Reality Games is interesting in that it links in-game economics to real-world economics, by focusing entirely on games with an in-game economy involving real-world money for political exploitation and examination: “in the current mobile environment, the time you are doing content creation is time you are not earning money” (Reality Games, 2018).  There are fail safes that are likely to delay some of the more nefarious data uses, and this is again linked to the size of the data involved. “Exploiting these digital data sources to their full research potential requires new mechanisms for ensuring secure and confidential access to sensitive data, and new analysis tools for mining, integrating, structuring and visualising data from multiple sources” (Halfpenny & Proctor, 2015, p3).