SAGE Research Methods Foundations: Online Video Games Research Harko Verhagen, Stockholm University, Sweden, Magnus Johansson, Uppsala University, Sweden Wander Jager, University College Groningen, the Netherlands © SAGE Publications Ltd 2020 http://methods.sagepub.com/foundations/ DOI: 10.4135/ Playing video games is a pastime for large parts of the population in many countries, with over 2 billion video gamers worldwide in 2016 (Statista, 2018). The spread of mobile phones and smartphones has extended gameplay options beyond the use of computers, tablets, game consoles, and gaming arcades. The 2018 demographic data for the United States indicates that contrary to the depiction of gamers as young, male adults, 60% of all Americans play on a daily basis; the average age of a gamer is 34 years; and females make up 45% of the total gamer population (Entertainment Software Association, 2018). Finally, video games are considered a major part of the media industry, with revenues outperforming those of other digital media such as digital music and video-on-demand, and even the revenues of the television and movie industry. As with all media, games come in different forms, shapes, and genres. One complete run of gameplay, ending with either a next mission or game level, or the end of the game, can be short (the majority of mobile phone games are in this category) or very long (or indeed never ending—the longest running online game, JediMUd, has been active since 1992)— even if the gamer can play short or long episodes in both cases. Well-known game genres defined by content include roleplaying games, racing games, and first-person shooter games; other genres are related to the gameplay itself such as casual games and social games. An important divide is between games run on the device (so-called sandbox games), in which one (or a few, co-located and interacting with the same device) gamer plays with, against, or on the device, and online games, in which multiple players can interact with the game in parallel using their own devices while connected through an online game server. Such a widespread social phenomenon has led to questions in need of research. Since video games and gaming can be analyzed from many different angles, various research strands exist. Games can be the focus of research, as technical artefacts, and of their design process. The in-game behaviour can be of interest, both of the individual player as well as the interaction between players. The consequences of gaming have been studied as well, such as the effects on the feeling of empowerment, the learning of real-life skills due to gaming, or the spread of violent behaviour as a result of playing violent games. Based on an extensive analysis of co-citation and keyword co-occurrence in game research publications, Paul Martin (2018) finds four game research communities: 1. education/culture-education, 2. education/culture-humanities and social sciences, 3. technology-computer science, and 4. medical-health. Interaction between the research communities does exist, except for between communities 2 and 4. For the purpose of this text, focus will be on the research in communities 1 and 2, which, to a large extent, map onto two different research paradigms, namely game science and game studies, respectively. As online games have been the main research focus within this community since the early 2000s, the discussion here is limited to online video games. This entry illustrates the different research strands and challenges using two research examples in the following two sections. Research Example 1: Online Games Player Culture Aspects of player and play culture have been researched in different ways. One aspect of group culture is the presence of shared norms. What constitutes the group differs, though. In multiplayer online games, a common feature is the need to perform missions in the game in larger groups, where each player adds different skills and other resources to the group. Groups may be formed “on the spot” for a game session, or persist over time from game session to game session. In the game World of Warcraft, groups that persist over time are called guilds. Most guilds have a formal hierarchical structure for decision-making, a procedure for accepting new members and norm maintenance, and use platforms outside of the game to define and promote the guild. In Magnus Johansson’s study (2013), the Internet fora of a set of guilds are analysed for the group norms expressed there, which are then synthesised into a set of general norms. Methodologically, the data collection is online text data followed by thematic analysis. Not all fora are open to non-members, though, so to make sure this does not introduce a bias, at least some of the data has to be collected from the closed fora. This then implies one needs to apply for membership (or access for research purposes as a non-member), which may be dependent on the playing skills (and in-game player level) of the researcher. In another study Kou, Johansson, & Verhagen, 2017), the formulation of norms and norm-breaching behaviour is studied in the context of the game League of Legends. Riot Games, the company that developed the game, wanted to test amongst others a way to limit the rude behaviour common to most online games. The method tested was a user-driven “legal system”, which would handle complaints from gamers about the behaviour of other gamers and decide if and how to penalize gamers who were reported. The legal system (called the Tribunal) consisted of a group of players of good standing volunteering to discuss the complaints sent in and come to a decision (combining judge and jury roles). The data consisted of the complaints (submitted in a structured form), the discussions between the jury members, and comments and reflections from the jury members about their jury duties. One of the researchers was part of the legal system, which gave them access to the internal discussion data, while the rest of the data collection was from the game discussion fora. The internal discussions provided insight into how norms are formed and negotiated, while the fora data provided information on the perception of the jury “duty” and how this role kept some of them in the game while not actively playing. The internal discussion data were collected via participant observation. In all cases of the online data collection—open or closed forum, or data collection during gameplay—consent will be needed. This may be difficult given the unclear identity of players using pseudonyms, and with online data collection it is always difficult to know if the same person is behind the pseudonym on each occasion. Data collection during gameplay can be done via recording (video or text notes) but if one has access to the game server (either via cooperation with the game company or since the game was custom-made for the research project), a wide variety of data can be collected in a large amount. These can be in-game utterances (chat in text or sound) but also each keypress, action chosen, and time passing between actions and reactions. Each computer input and output can be logged and analysed. Riot Games, used these data to experiment with design choices. Since popular games have many players worldwide, the amount of data is very large; in the case of the previous video link, over 250,000 players are involved and over 100 million votes were cast in the Tribunal processes mentioned, over a six-month period for European and North American players. Research Example 2: Online Games for Training of Understanding and Analysis of Complex Systems The use of games in education has a long tradition; for instance, in training of management students, case-based education and role-playing games have been a standard approach. Visualizing the dynamics of complex systems using models, such as water-based models of the macro-economical system where policy choices can be simulated by changing valves, is another example of the use of simulation for educational use. An early example of computerizing the macro-economic dynamics is a study by F. Gerard Adams and Christopher Gretzy (1991). In their case, the simulation game consisted of coupled spreadsheets and interacting computers in a local area network (LAN)—quite a different take on gaming with less focus on the aspect of play as central in game research but consistent with the idea of game science (Stenros & Kultima, 2018). In game science, a game is designed to solve a problem, in many cases analysing or understanding a problem of high complexity. Examples of the same type of central issue (complexity) as an entertainment game mechanism are flight simulators (inspired by real training systems) and the Simcity game series (urban planning and political decision-making). In social science, the understanding and analysis of complex systems is key as well, leading to the advance of simulation models of these complex systems. The game science paradigm is firmly based in the simulation and games community. One danger for using gameplay for education is that avid gamers may play the game to win it or to fulfil the role they see the game character to have, rather than behave in the game as they would do in reality. Another danger is that if a game is felt as being too unrealistic or hard to believe, the participants will not take it seriously. For understanding of macroeconomics as in the Adams and Gretzy study, a numerical simulation approach might work. For complex issues that policymakers deal with, this approach is less suited. A more modern approach within the social sciences is the use of agent-based simulations rather than equation-based simulations. By mimicking individual decision-making in interaction with the social and physical environment, complex social processes can be studied. Such models can be turned into realistic games for students in a learning situation, but also for policymakers to experience and experiment with the consequences of different policy choices. Key developments in the field of agent-based modelling that contributed to this realism address (a) the drivers and processes of decision-making, and (b) the networks connecting these agents. Concerning the drivers and processes of decision-making, agents can be equipped with multiple needs or utilities, and they may differ concerning the orientation and importance of their needs or utilities. As such, a heterogeneous population can be constructed whereby agents attach different weights to certain outcomes, and display a variety of choice behaviours, ranging from individual optimising to learning from peers and performing habits. The networks connecting these agents are also important, allowing researchers, for example, to model opinion leaders who are being followed by many others to acquire information on what to do (informative and normative influences; e.g., Van Eck et al., 2011). One of the first online games that used a more theoretically realistic artificial population is the Energy Transition Game (Jager et al. 2018). In this game, multiple players take the role of energy companies and political parties that are competing for market share and votes of a simulated population. Using an architecture for simulating individual consumers on the basis of social and behavioural science, the behaviour of the simulated population is reproduced realistically enough to make playing the game an entertaining but also a meaningful experience. It is imaginable that many social challenges can be gamified using agent-based modelling tools. For example, many social-ecological challenges such as managing fishstocks, maintaining forests, and counteracting against desertification require a combination of knowledge on the eco-systems and on behaviour of communities. In games where teams of experts from different disciplines can be formed, and their communication and decisions tracked, researchers can study under what conditions multidisciplinary groups are more effective in managing complex societal problems. One issue might be that the game approach may be seen as less serious by policymakers or experts, whereas more traditional methods of knowledge transfer and inter-expert communication may have better acceptance. FURTHER READINGS Lankoski, P. & Björk, S. (eds.) (2015). Game Research Methods: An Overview. ETC Press. Retrieved from http://press.etc.cmu.edu/content/game-research-methods-overview. REFERENCES Adams, F. G., & Geczy, Ch. C. (1991). International Economic Policy Simulation Games on the Microcomputer. Social Science Computer Review,9(2), 191–201. doi: 10.1177/089443939100900201 Entertainment Software Association (2018). 2018 Essential Facts About the Computer and Video Game Industry. Retrieved from: http://www.theesa.com/wpcontent/uploads/2018/05/EF2018_FINAL.pdf Jager, W., Scholz, G., Mellema, R., and Kurahashi, S. (2018). The Energy Transition Game: Experiences and Ways Forward. In: S. Kurahashi & H. Takahashi (Eds). Innovative Approaches in Agent-Based Modelling and Business Intelligence (pp. 237-252). Springer. doi: 10.1007/978-981-13-1849-8 Johansson, M. (2013). ‘If you obey all the rules, you miss all the fun’ – a study on the rules of guilds and clans in online games’. Journal of Gaming & Virtual Worlds, 5(1), 77-95. doi: 10.1386/jgvw.5.1.77_1 Kou, Y., Johansson, M., & Verhagen, H. (2017). Prosocial Behavior in an Online Game Community: an Ethnographic Study. In: Proceedings of the 12th International Conference on the Foundations of Digital Games, article no. 15. doi: 10.1145/3102071.3102078 Martin, P. (2018). The Intellectual Structure of Game Research. Game Studies, 18 (1). Retrieved from: http://gamestudies.org/1801/articles/paul_martin. Statista (2018). Number of active video gamers worldwide from 2014 to 2021 (in millions). Retrieved April 20th, 2019 from https://www.statista.com/statistics/748044/numbervideo-gamers-world/. Stenros, J., & Kultima, A. (2018). On the Expanding Ludosphere. Simulation & Gaming, 49(3), 338–355. doi: 10.1177/1046878118779640 Van Eck, P. S., Jager, W., & Leeflang, P. S. (2011). Opinion leaders' role in innovation diffusion: A simulation study. Journal of Product Innovation Management, 28(2), 187-203. doi: 10.1111/j.1540-5885.2011.00791.x Author bios Harko Verhagen is an associate professor at the Department of Computer and Systems Sciences, Stockholm University. His research has focused on agent-based simulation of social interaction, social interaction in and around computer game play, social ontology and agent models, use of social media in online education, and issues of design for hybrid social spaces. He has published over 100 peer-reviewed papers, book chapters, etc.; guest co-edited special issues of journals such as Computational Mathematical Organization Theory, Journal of Gaming and Virtual Worlds, and Logic Journal of the IGPL, and recently co-edited the Handbook of Normative Multiagent Systems. His publication list is available via http://harko.blogs.dsv.su.se/ Magnus Johansson is a researcher and lecturer at the Department of Game Design, Uppsala University, Campus Gotland. He received his doctorate in Computer and Systems Sciences from Stockholm University in June 2013, and is head of the Department of Game Design, Uppsala University since 2017. Johansson has published more than 30 peer-reviewed publications to games conferences, books and journals. His research interest is often focused on the player, groups of players, the activity of playing games and how design influences the player experience. Previous publications deal with the social aspects of online games such as norms, social rules, toxic gaming and methods for studying games. Other perspectives that Johansson has studied includes how to create socially believable non-player characters, design and evaluation of games from a usability perspective, and how to design games for learning. Wander Jager (University of Groningen) is a social scientist with a broad interdisciplinary interest in social complex phenomena and transition to a sustainable society. Being inspired by the work of John Holland on chaos and self-organisation in the 1990s, he has since then been working and teaching on the micro-macro interactions in various social systems. His PhD thesis “Modelling Consumer Behaviour” (2000) integrated key behavioural theories into a computer simulation of human population behaviour. Formalising behavioural theory and empirical data in agent-based simulations is currently being used to study processes of social innovation in, for example, transportation, farming and city-planning (see http://local-socialinnovation.eu).