This research study explores the influence of perceived fairness on players' playstyle in multiplayer card games. The study focuses on a card game called "Insomnia" and its modified versions to investigate players' perceptions of fairness and their subsequent behavior. Two series of playtests were conducted with different groups of participants, and data were collected through gameplay observations and semi-structured group interviews. Thematic analysis was performed on the collected data, comparing findings with existing literature to provide insights into fairness in card games and the consequent playstyles. The results reveal that players adapt their strategies and playstyles when the game becomes difficult and unfair, shifting towards cooperative actions and helping others. Furthermore, the study shows that their goals, motivations, and perceived fairness influence players' preferences and behaviors. The findings underscore the interplay between fairness, playstyle archetypes, and player behavior in multiplayer card games. The study emphasizes the significance of understanding individual motivations, social dynamics, and game design elements in shaping player experiences. It suggests that game designers should consider the complexity of player decision-making and the intricate relationship between motivations and perceptions of fairness to foster engaging gameplay.

Humans procrastinate and avoid performing activities that they deem dull, repetitive, and out of their comfort zone. Gamification was conceived to reverse the situation by turning those activities into fun and entertaining actions exploiting game-like elements. In practice, however, many challenges arise. Gameful environments cannot satisfy every player's preference and motivational need with a one-fits-all strategy. However, meeting players' motivational affordances can provide intrinsic rewards rather than extrinsic (e.g., points and badges). Producing intrinsic rewards is desirable as they are more likely to foster long-term retention than the extrinsic counterpart. Therefore, gamified systems should be designed to learn and understand players' preferences and motivational drivers to generate specific adaptation strategies for each player. Those adaptation strategies govern the procedural generation of personalized game elements - examples are task difficulty, social-play versus solo-play, or aesthetic tools. However, an appropriate personalization requires intelligent and effective player profiling mechanisms. Player profiles can be retrieved through the analysis of telemetry data, and thus in-game behaviors. In this project, we studied players' individual and social behaviors to understand their personalities and identities within the game. Specifically, we analyzed data from an open-world, persuasive, gamified system: Play&Go. Play&Go implements game-like mechanics to instill more ecological transportation habits among its users. The gamified app offers various ways for players to interact with the game and among one another. Despite Play&Go being one of the few examples of gamification implementing more diverse game mechanics than solely points and leaderboards, it still does not reach the complexity of AAA entertainment games. Thus, it limits the applicability of an in-depth analysis of players' behaviors, constrained by the type of available features. Yet, we argue that gameful systems still provide enough information to allow content adaptation. In this work, we study players' in-game activity from different perspectives to explore gamification's potential. Towards this, we analyzed telemetry data to (1) learn from players' activity, (2) extract their profiles, and (3) understand social dynamics in force within the game. Our results show how players' experience in gamified systems is closer to games than expected, especially in social environments. Hence, telemetry data is a precious source of knowledge also in gamification and can help retain information on players' churn, preferences, and social influence. Finally, we propose a modular theoretical framework for adaptive gamification to generate personalized content designed to learn players' preferences iteratively.

This is the result of a 10 week long study about if player behaviors and choices are affectedby the surrounding colors in their environment. A number of players are asked to movethrough a virtual environment, while being clocked, where they have to make left and rightdecisions based on different colors (red, green, blue and gray). While most choices consist oftwo different colors, some use the same color. The result shows that the players most likelyprefer one color over another, and that they most likely prefer a non-neutral color over aneutral color. With these results and the interviews that are conducted the conclusion is madethat it is very likely the players choices are affected by the colors in the environment, but it isnot clear to say how they are affected. / Det här är resultatet av en 10 veckor lång studie om spelares beteenden och om deras valpåverkas av färgerna i deras omgivning. Ett antal personer är tillfrågade att röra sig genom envirtuell miljö, på tid, där de gör höger- och vänsterval baserat på olika färger (röd, grön, blåoch grå). Medan de flesta av valen består av två olika färger så använder några samma färger.Resultatet visar att personerna troligen föredrar en färg framför en annan och att de troligtvisföredrar en icke-neutral färg framför en neutral färg. Med dessa resultat och de intervjuer somgjordes så blir slutsatsen att det är stor sannolikhet att personerna påverkas av färgerna i derasomgivning, men det är svårt att säga exakt hur de påverkas.