# Progression LLMS index: [llms.txt](/dvw/en/llms.txt) --- I mentioned earlier the main sequence that charts player type drift. This gets its name from the main sequence of star development used in cosmology [^55]. Players typically start off testing the immediate bounds on their behavior (killer) then begin to acquire knowledge of their environment (explorer); following this, they apply their knowledge (achiever), in the course of which they forge bonds with other players; finally, they retire from this and spend their time chatting with their friends (socializer). Sometimes, people stay in one area. Long-term killers will often be youngsters who have feelings of inferiority [^56] and need constantly to validate their worth to themselves. Long-term explorers know they have the ability to apply their knowledge, and feel no urge to demonstrate the fact to others. That said, a good many people follow the main sequence the whole way. There’s a flaw in this argument, though. As people grow in virtual worlds, their real selves and their virtual selves gradually become one. Because socializer is the last step on the main sequence, all socializers should be immersed to persona level. They’re not. You can start as a socializer, socializing effectively while being barely immersed to avatar level. Your development is due to social challenges rather than environmental ones. This rather undermines the use of player types in predicting the effects of growing maturity, doesn’t it? As it stands, yes, it does. There is, however, an interesting solution. My original work for Hearts, Clubs, Diamonds, Spades: Players Who Suit MUDs evolved from summarizing a discussion among the players of MUD2 that ran from November 1989 to May 1990. The paper as finally presented concerned only the actions and interactions between mortals (that is, normal players); it did not make particular reference to wizzes (that is, players trusted with administration powers—this is MUD2’s elder game). Wizzes [^57] had already played the game in its entirety, as that was the qualification to be a wiz; therefore, they were on the whole no longer concerned with the virtual world per se, just in its inhabitants. There were a few explorers who made wiz better to aid their exploring, and a few achievers who regarded building as a competitive act; the majority, though, were either socializers or killers. In separating wizzes from mortals, I noticed that there were some very distinct behavioral differences between the wizzes themselves. Wizzes were keen to articulate their beliefs about what they and their peers should and should not do to manage the game, as there were particularly strong conflicts between certain groups. Therefore, at the same time I constructed the player interest graph (refer to Figure 3.1) for mortals, I constructed a similar one for wizzes. The players/world axis was not relevant here (because nearly all wizzes were at the players side); in its stead was a different axis that rated the degree to which wizzes did things in an explicit or implicit [^58] fashion. Explicit wizzes favored the known over the unknown, realism over idealism, the overt over the covert, expectation over surprise, and order over disorder. Note that “explicit” here is meant in the general sense of the word: leaving nothing implied. It’s not intended to have any “explicit photographs” style overtones. In computer language semantics, explicit means data, implicit means code; in AI terms, explicit means knowledge, implicit means skills. That’s how I mean it. There were a number of conflicts suggested by this implicit/explicit analysis, which led to some interesting underlying dynamics (and alarming consequences for inter-wiz balance). However, as the graph was specific to the relatively small set of administrators in a game-oriented virtual world that selected them by unusual means, I felt it would have distracted from the main thrust of my player types model had I presented it in my paper. I therefore left it out. Perhaps, though, it doesn’t just apply to wizzes? What would happen were we to incorporate this implicit/explicit dimension [^59] into the basic player types model? Specifically, would it help us differentiate between sub-types of the player-oriented styles, killer, and socializer? Yes, it does [^60]. We get: - Politicians. Players who act in an open fashion on other players. Whether you view them as inspired, visionary leaders or interfering, self-serving busybodies depends on how gullible or cynical you are. - Networkers. Players who interact openly with other players—even complete strangers—on any and all subjects. Less charitably, gossips. - Friends. Players who interact primarily with people they have known a long time and with whom they have deep bonds (often forged through adversity). They do not feel bound by the conventional rules of interaction, because they understand each other so well. - Griefers. Bullies prepared to use force or other unpleasantness to get their way or be noticed. Figure 3.3 shows this as a graph. ![Figure 3.3](/figures/Figure-3_3.png) Figure 3.3 Players-oriented Player Interest Graph. If this served only to clear up some of the confusion about the different varieties of the killer type (griefers and politicians) it would have done its job. However, it’s not quite a one-trick pony. What happens when a player-oriented newbie enters a virtual world? The first issue many of them face is coming to terms with the fact that they are part of a community. Some will immediately comprehend the situation, but not everyone. Those that don’t will objectify other players, treating them as helpdesks, servants, gophers, and sources of experience points or kit. The reaction of the other players to this approach will cause all but the most mule-headed newbie socializer to understand fairly rapidly the social mechanics of the world. They can then move on to the business of getting to know people. To make friends with someone, you need to meet them, which means you have to network. Some people you won’t like, and some won’t like you, but after a while you’ll find you hang out with the same, relatively small group of people most of the time and be acquainted with many more. Your group will almost inevitably experience conflicts, both internal and external. Perhaps several people want to be leader, or don’t like where the leader is taking them. The group may grow too large, or too small, or otherwise become dysfunctional. Environmental changes could trigger problems—a change in the way the virtual world works, for example, or a loss of group status. Ultimately, everyone in the group has a view and will at least some of the time want to impose it on the other members—even if it’s only as a form of self-defense. The trials that result put big stresses on relationships, some of which may break down irrevocably. Eventually, though, players will come through with a fuller understanding of themselves and of their friends. Their earlier ties will have been strengthened, and they will at last be able to settle down and appreciate one another. What we have here is a main sequence for socializers: griefer to networker to politician to friend. Drawn on the graph [^61], it’s the same reverse-alpha shape as the standard main sequence, which is nice. Back in two dimensions, it would be killer to socializer to killer to socializer, but it’s less obvious what’s happening when flattened that way. At this point, we can see where F. Randall Farmer’s Path to Ascension fits in. This is perhaps unsurprising, given that Habitat was a socializer-centric system. Ignoring the “non-player” caretaker and geek god roles: Motivators map onto politicians; actives and passives both map onto networker (actives being more immersed than passives) [^62]. So, having added an extra dimension, the obvious question to ask next is: What—if anything—does adding an extra dimension buy us concerning the world-oriented players (achievers and explorers)? Well, the subdivisions are relatively easy to construct: - Planners. Organized achievers, who decide what they want to do then go off and do it. - Scientists. Explorers who experiment in a thorough, methodical fashion. - Hackers [^63]. Explorers whose understanding of the virtual world is such that they can proceed purely by intuition. - Opportunists. Achievers who go where their fancy takes them. Figure 3.4 shows the resulting 3D graph. ![Figure 3.4](/figures/Figure-3_4.png) Figure 3.4 3D Player Interest Graph. Whether this is of tremendous use is another matter, of course [^64]. Instead of four player types, we now have eight: opportunists, planners, politicians, griefers, hackers, scientists, networkers, and friends. The conflicts between some of the eight are meaningful (for example griefers versus planners), but for others the old types work just as well and are better at encapsulation (for example socializers versus achievers). If having more categories doesn’t deliver more tools for designers, there’s little point in designers using them. Mere categorization doesn’t itself guarantee utility, even if it can be shown to be inclusive (as this one can) and is easy to validate empirically (as this one is). After all, adding a dimension for the time of the year in which a player was born would be inclusive and verifiable, but designers would find it of very little general efficacy [^65]. Concerning our current discussion on player type progression, though, the addition of an extra dimension does have an effect for world-oriented players: It gives another sequence—opportunist to scientist to planner to hacker. We also can “unflatten” the main sequence (killer to explorer to achiever to socializer) from 2D into 3D, giving griefer to scientist to planner to friend. Tempting though it is to look for a sequence matching this of opportunist to networker to politician to hacker, this can’t be validated empirically: People simply don’t follow that career path. However, there is a minor fourth sequence, for opportunist achievers who learn by networking rather than by experimentation. This gives us: - Griefer to scientist to planner to friend (main sequence) - Opportunist to networker to planner to friend (minor sequence) - Griefer to networker to politician to friend (main socializer sequence) - Opportunist to scientist to planner to hacker (main explorer sequence) At last we’re getting close to a model good enough to use for something. If designers can assess how players are likely to mature, they can account for their progress in their game designs. We’re not quite there yet, though. [^55]: The Hertzsprung-Russel diagram: Map absolute visual magnitude against spectral. The main sequence, on which most stable stars lie, runs roughly diagonally from high luminosity/low temperature to low luminosity/high temperature. [^56]: “Ha! You call that an inferiority complex?!” [^57]: The term is gender non-specific, meaning “wizards and witches.” [^58]: For MUD2-specific reasons to do with the nature of the wiz invisibility command, the original axis labeling was open (explicit) and closed (implicit). [^59]: Readers of Michael Moorcock and players of D&D can use law/chaos if they really must, but it’s not quite a match. [^60]: Unsurprisingly, as obviously I wouldn’t have mentioned it otherwise. [^61]: For the record, this also works on the original, wiz-specific graph. [^62]: This suggests that at least in some cases, refining the model to account for gradations of immersion may be useful. [^63]: This is in the traditional sense of the word, not the break-into-your- computer-and-erase-your-data one. [^64]: Tools are only objects if they have no use. What’s more, it’s far better if the tool is designed to fit the problem than if the problem is designed to fit the tool. I don’t want to be a man with a hammer looking for things I can nail. [^65]: Sorry to break the bad news to any astrologers among you.