Player Types: Watch for Moving Targets

A Deeper Look at Richard Bartle’s Player Types, Part II

Since the Bartle Test of Gamer Psychology was created in 1996, more than 740,000 people have taken it. Gaming circles have seen the old saw “What’s your sign?” transformed into “I’m an KEAS. What’s your type?”  For example, I found this comment on a gaming blog:

“So I took the test again today.
Apparently I’m a little bit more into PVP these days and a little bit less in socializing.
Killer 93%, Explorer 60%, Achiever 33%, Socializer 13%”
Loregy.com

This comment hints at a two interesting subtleties about Bartle’s player type model.  First, most players exhibit a combination of all four player types, and second, and just as important, players may change their type from time to time.  In fact, as we will see, players will often move through a predictable progression of types over the course of playing any given game.

If you’re not familiar with Richard Bartle’s Player Type model, my last post delved into the definitions of each player type, how players of different types interacted with each other and amongst themselves, and how multi-user games need to achieve a balance between types.  The fact that players often exhibit behaviors of all four types provides another reason to avoid designing applications that don’t cater in some way to all of the player types.  In this post, we’ll take a deeper look at how Bartle’s full model explains the movement between types.

Expanding Bartle’s Original Model
Bartle’s original model mapped players on a two-dimensional grid with the two axes expressing each player’s degree of preference for acting on or interacting with the game world itself or its players.[1]  In his 2005 paper “Virtual Worlds: Why People Play,” Bartle notes that there were several flaws in this model:

“Although this model has been generally accepted as a useful tool among designers, it
nevertheless has flaws. Two are of particular importance. Firstly, it suggests that players
change type over time, but it doesn’t suggest how or why they might do so. Secondly, all
of the types to some degree, but especially the one for acting on players (that is, Killers),
seem to have sub-types that the model doesn’t predict.”[2]

Bartle corrected these flaws by adding a third axis, which further divided the four player types into those whose behavior is implicit (spontaneous or automatic) and those whose behavior is explicit (planned or deliberate).  With this new dimension, we get a 3D graph with the 8 player types shown in figure 1.

These new player types have the following behavioral characteristics:

Player Evolution
Even before Bartle formulated the concept of player types, game designers and players often observed that, within a given game, players often followed a number of changing behavior patterns. The most typical pattern saw new players starting out by aggressively playing the game and testing its boundaries; then having tired of haphazardly fighting everything in sight, they begin to methodically acquire knowledge about the world; having gathered enough knowledge they plot out a strategy to “win the game;” finally having “won,” they become elders who enjoy socializing, reliving old battles, and engaging with the world at their whim.

Using the original player types, players would start off as Killers then in turn become Explorers, Achievers, and Socializers.  However, if we take the expanded model and remove the player vs world axis, we can easily see how players progress from one developmental stage to the next, as shown in figure 2. (Note that the names for each stage are mine not Bartle’s.)

Using the eight player types, all of the possible progression tracks are captured in figure 3 below.

Thus, the generalized sequence becomes:

  1. Novices:  These players thrash around as they test the physical (opportunists) and social (griefers) boundaries of the game.  Their behavior is characterized by IMPLICIT ACTION.
  2. Learners: Players move to codifying their knowledge by “stringing together meaningful sequences of actions…either by experimenting (scientists) or by asking someone who already knows (networkers).”[2] They are interested in EXPLICIT INTERACTION.
  3. Veterans:  Next, players seek to apply their new found knowledge to become a master as defined by the world (planners) or other players (politicians).  They are interested in EXPLICIT ACTION.
  4. Elders:  Finally, having mastered their domain, players now interact with the world (hackers) or their (friends) in an instinctive manner rather than a planned one.  They are interested in IMPLICIT INTERACTION.

Of the ten possible tracks, Bartle made special note of the four most prevalent ones:

  • Main Sequence: Griefer – Scientist – Planner – Friend, which shows progression through all four of the original player types, is the most prevalent
  • Socializer Sequence: Griefer – Networker – Politician – Friend oscillates between the four Killer and Socializer sub-types
  • Explorer Sequence: Opportunist – Scientists – Planner – Hacker oscillates between the four Achiever and Socializer sub-types
  • Minor Sequence: Opportunist – Networker – Planner – Friend oscillates between the four Achiever and Socializer sub-types

Furthermore, Bartle observed that Scientists never became Politicians and that Politicians never became Hackers.  This oddity may be because scientists never develop the prerequisite social skills as a networker to become a politician and because politicians never develop a deep enough understanding of the physical environment as a planner to become a hacker.

Application Design Implications
For folks involved in applying game thinking to non-game systems, one of the key ways to think like a game designer is to use the concept of the player’s journey to guide the application of specific game techniques. By understanding the journey that their users are on, designers can support that journey through their designs. While the journey will be different for each application, Bartle’s expanded player type model provides us with important guide posts as to how users will behave at different stages of their journey.

  • Novices want to feel welcomed and need achievable goals, progress and rewards. Opportunist may drop out if too many obstacles are placed in their way. Griefers will look for ways to quickly build their big, bad reputation.
  • Learners need more sophisticated ways to interact and experiment within the application. Scientist will want to uncover the secrets to the underlying physics of the application as well as Easter eggs and hidden bonuses. Networkers need plenty of opportunities to interact with knowledgeable users.
  • Veterans need long-term goals that they can methodically work towards using the strategies and tactics that they’ve learned. Politicians will seek out noteworthy social goals and need ways to grow in the esteem of the game community.  Planners on the other hand want difficult challenges and obstacles to overcome.
  • Elders want to enjoy the fruits of the labors and be rewarded with special unlocks and activities.  Friends will want access to private VIP areas where they can socialize with their comrades, while hackers will want to unlock superuser tools with which to manipulate and control the system.

What’s next?
In the third and final post in this series, we will investigate the basis for applying Bartle’s player type model beyond the confines of the massively multi-player online games (MMOGs) for which it was originally conceived.  We will look at Richard Bartle’s own admonitions about using the player type model outside of MMOGs, and at academic research that matches the results of Bartle’s work while at the same time providing a more generalized theory of human behavior.

Notes:
[1] Bartle, Richard A. “Hearts, Clubs, Diamonds, Spades: Players Who Suit MUDs. Journal of MUD Research. Vol. 1, Issue 1. June, 1996.
[2] Bartle, Richard A. “Virtual Worlds: Why People Play.” Massively Multiplayer Game Development: v.2, Ed. Thor Alexander.  Charles River Media. 2005.

2 Comments , , , , ,

2 Responses to “Player Types: Watch for Moving Targets”

  1. Andy King September 25, 2012 at 1:07 pm #

    This is very useful information. Having just watched Amy Jo Kim’s interview in Coursera’s Gamification series it is interesting to see how Bartle expanded on his quadrant model to better define players. Being a gamer I can relate to the progression chart from n00b to Hardcore and the different paths to each destination. Having gained perspective in the class and through readings and other outlets I have a lot to consider as I try to think like a game designer instead of just an game experience consumer.

  2. Kenneth Hong October 2, 2012 at 4:08 am #

    Andy,
    Thanks for your comment. I’m glad you are enjoying the class and that this post was useful. Bartle’s framework has proven to be valuable in many contexts, but certainly needs to be adjusted and modified to suit any given situation.
    –Ken

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