Using Personality Models to Make Better Games

Personality models are used today in a number of fields for a variety of tasks, from diagnosis in social work, to determination of an individual’s strengths in the workplace. This paper proposes that such tools could be used to help shape user experience in virtual worlds and suggests two methods to integrate these models in games.

In a 2008 article on the subjective well-being of online gamers it was suggested that gameplay could be tailored to the player to better accommodate his/her personality type (Chen, 2008). This would greatly increase the quality of each user’s subjective experience, and in effect increase both the longevity and value of the game. However, as of yet, there seems to be very little implementation of personality modeling systems in games. Rudimentary systems have been used in order to determine a user’s desired playing style, but they often are only used at the start of a game in order to suggest a character’s class, and have little or no further effect upon gameplay.

Much of the research around personality as it relates to playing games has been done using a commonly accepted five factor model (also known as the “Big 5”) which incorporates five different dimensions to describe a person’s disposition. These five dimensions include: Neuroticism, or how easily a person becomes distressed, Extraversion, which is a measure of how outgoing a person is, Openness to Experience, or how flexible a person is in their behavior, Agreeableness, which takes into account factors such as how trusting or how antagonistic a person is, and finally Conscientiousness, which measures a person’s dependability and organization (Costa & McCrae, 1992). Each of these dimensions are measured along a spectrum, which means that a conventional introvert would achieve a low score on openness and extraversion while a person who compulsively cleans might score higher in conscientiousness and neuroticism.

Suggested Integration

Personality types are most commonly determined by self-reporting via questionnaires. These questionnaires rely on the participant’s honest response to develop accurate results. This format makes sense in the context of social work or in increasing workplace efficiency, but not so much in an in-game environment.

The simplest implementation of an in-game personality modeling system would probably look something like the Generalized Occupational Aptitude Test (G.O.A.T.) in Fallout 3. In this example the player’s avatar is made to take an in-game aptitude test, however the test only serves to help the player choose skills for their avatar which would accommodate their playing style and has no further impact on gameplay. With some modification, it would be simple enough to have the results of such a test influence gameplay in later stages and would provide users with a more enriching experience. While this implementation is simple, it is hardly elegant and is fairly intrusive into the gameplay. In some cases, as in Fallout 3, it can be done while maintaining immersion, but in others this would prove very difficult if not impossible. It should also be noted that the current ‘lite’ versions of personality tests in games tend to be relatively short, whereas real psychological personality tests tend to be somewhat longer.

An alternate version of this implementation would take advantage of the ability of current generation console platforms to affect games via platforms such as the Playstation Network, Xbox Live or the Wii’s Dashboard. This approach would be similar to the first, in that the player would fill out a personality test, but the result would then be stored on the user’s profile, and would be usable in any game on that system that had the ability to take advantage of it.

A more elegant way of discerning a player’s personality type would require a system which observed the player’s behavior ingame, and then developed a model over time. This is a technique which undoubtedly is best suited for massively multiplayer sandbox games, but could also be applied to more linear and single player games with some creative design. Such a system could be implemented in many different ways, whether by creating quests which would probe at a player’s disposition, observing how sociable a player is via ingame statistical tools, or even observing different ingame social networks such as guilds/corporations/clans, etc. This method does have drawbacks, as it could be somewhat difficult to implement and brings into account issues of privacy. Each player should be made aware of such a system and be allowed to opt out of it if they so choose.

Once a model was established, it would then be used to shape individual gameplay towards a player’s preferences. In roleplaying games this could manifest in something as simple as rewarding extroverts for participating in social organizations, such as guilds or corporations, or even just assigning different missions or quests to a player depending on their personality type. More complex solutions could manifest in more subtle ways, and could be designed to organically reward players for playing the game in the way which they find most enjoyable.


An ingame system which tailored gameplay to each user’s personality could be immensely useful in subscription retention for MMOs, and in increasing the experiential value of regular single and multiplayer games. While such a system is most obviously useful for roleplaying games, it could also be implemented in a number of other genres with some creative game design. An acquired model could also be used for purposes not directly related to gameplay, such as intelligently targeted advertising or to help identify which users are most at risk for addiction in MMOs, but discussion of these uses are beyond the scope of this paper.


Chen, L. (2008). Subjective well-being: Evidence from the different personality traits of online game teenager players. CyberPsychology & Behavior, 11(5), 579-581.

Costa, P, & McCrae, R. (1992). Normal personality assessment in clinical practice: The NEO personality inventory. Psychological Assessment, 4(1), 5-13.