Sims, BattleBots, Cellular Automata God and Go

[Readings] (02.06.09, 1:04 pm)

These are notes from an interview that Celia Pearce did with Will Wright in 2001. The notes are my impressions from the interview and how the design principles and ideas can be carried over to my work in the adaptation of fiction through simulation. The interview has a great deal to do with the principles of mental models and how those relate to play and the way that players can both consume and produce content.

Wright’s design philosophy: Wright’s original ideas were the most affected by his practice of building models as a kid. Making things is about creating models, which are at first static, then dynamic models, then about giving others tools to build their own things. This is an approach that is about creation, and is continually outward moving, from creating objects, to creating tools to make objects, and then creating tools for others to be able to create tools to make objects. At a distance, this philosophy resonates strongly with the mental model theory that is later explicitly adopted.

The reason for creating tools is to enable players to solve a problem from within the space of a game. This supposes that players have goals and problems concerning what to do within the game, though. If this is the case, then building empathy is about the size of the solution space – the player will have more empathy with the game if they are able to do something personal within the game.

Wright’s influences were forms of simulations, which set up worlds with rules, but were open to play with. The engagement and enjoyment in these comes from exploring their boundaries. When such experimentation is possible, it enables the practice of experiments and the scientific method. This sort of boundary play is common to simulation genres, and is often used in attempting to exploit and disrupt the game, and is thus generally oppositional to immersion.

The origin of the experimentation with rules goes back to war games, which have elaborate rule systems, and the enjoyment is partially the negotiation, application, analysis, and mastery of the rules. These are described as laid back, in opposition to intense twitch arcade games (which conversely produce an experience more like flow/immersion). The pleasure is thus the kind of putting together and figuring out sort, rather than the kind of being in a world. The two seem to be at odds, but I think they are not necessarily contradictory.

An interesting detail is that the model of the system in war games is more than can be contained or simulated in one’s head, so it the game must be played in order for the full rules to become apparent. This also yields a mechanic of experimentation, which becomes prevalent in maxis games. In these, the model really exists in the game programming, or in the designers’ minds. The actual computer running the game is an intermediary layer between the rules and the player. The core element of this is thus that the play is a process of learning the designers’ model.

Much has to do with use of metaphors. Players of SimCity initially think of it as a train set that comes to life, or think of The Sims as a dollhouse that comes to life. Gradually, through play, the players come to adopt new metaphors. The interaction with SimCity metaphorically resembles gardening more than a train set. With The Sims, the metaphor depends on play style.

Regarding how to advance and make improvements for sequels and next versions, it is necessary to analyze how players use the different parts of the game, and add material to the exchange (in The Sims). Wright explains how using data mining and observing this information is exploring the landscape of how people play the game. At abstract, this is building a model of the model (Pearce’s terms). Wright compares the process to cultural anthropology. This idea is relevant in comparison to building a game off of something, like Austen, who is established well in fan culture.

Wright is interested in an extended and automated system around this data mining process, which analyzes player behavior, preferences, and the things they create, and then responds to those, and can share those with other players. The ideal format of this is automated and invisible.

Regarding abstraction, Wright describes how elements of gameplay are abstracted. The parts that are not simulated must be moved to the player’s head. These are the elements that I commonly refer to as the representative elements. This is described as a kind of offloading. Games are abstracted in the sense that selections, what the player may select or manipulate, is simplified to some degree. What is missing represents gaps. It is the player’s role to fill in these gaps, to make the resulting system seem consistent. This is analogous to the gap filling in the sense of narratives.

In competitive games like Go, play is about bringing the players’ models together. Each player has sense of regions and territory, but this may be in disagreement with the other player. The conflict is on terms not of what is physically present on the board, but in terms of what the models and plans are in players heads. This is strongly connected to the theory of mental models. It is important to note that in comparison with other perspectives of mental model theory, this is about models and deception, and involves a lot of work with inducing beliefs and illusions. It also ties with the physical board, but involves overlays. Experience and practice are critical.

Simulation can be used as a communication tool, for people to model their community or environment, reflect their world using language of simulation, and share and communicate this model. Disagreement is generally at root a disagreement over a model (could be argued over a metaphor), so communication helps explore these disagreements.

When playing a game like The Sims, the player fluidly shifts between thinking of the character as an extension of the self versus a separate agent. The player may move between identification to alternation, thinking of the character as an avatar or extension of self (using first person to describe the character’s actions, “I am going to make dinner”), or as an autonomous agent (generally described in third person, “he is not doing what I want him to do”). This is a type of jumping in and out, which is very fluid, and is surprising to Wright. Roots of this may be somewhat considered in case of performance in sense of Goffman, or Mead, in the sense that the self can be considered as an object.

An issue at stake in the matter of gameplay is comparing the possibility space of games. Wright’s goal is to enable variability and flexibility in the space, so that player has greatest control. This is contrary to sense of controlled models, where the designer has supreme control over experience, aesthetic value is giving player maximum return on experience. The metaphor of the game as a landscape of space continues.

However, play is also a process of navigating this space, and it may have a convoluted terrain. Gives an example of hill climbing- where the player might want to get to certain places and navigate there. This involves understanding of model and creative discovery, and requires the topography to be consistent, but also relies on the ability for the player to create goals within the space in the first place. The significance of this is not really addressed. The question that I want to ask is, “why does the player have goals within the game”, especially as Maxis games do not have explicit objectives. There are clearly things that are valuable and meaningful to some players, but what are they, and what value and meaning are they getting from them?

Result of engagement is a situation where player is both consumer and producer, (Ken Perlin calls this hybrid a “conducer”), where the player pays for the right to produce content. This content is shared. The model described echoes the emergence and popularity of blogging and YouTube (that emerged after the date of this interview), where people may share their own creations. An important issue I am interested in is why they share, what they share, and what meaning others get from them. The process of this is similar to fan culture, which thrives on building from some cultural base which already means something to a group.

Reading Info:
Author/EditorPearce, Celia
TitleSims, Battle Bots, Cellular Automata, and Go
Tagsspecials, games, simulation, emergence
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