> [!cite]- Metadata > 2025-09-12 11:32 > Status: #primary#lecture > Tags: `Read Time:` > Will Wright is a highly influential video game designer known for creating groundbreaking simulation games such as SimCity, The Sims, and Spore. He co-founded the game company Maxis, which was later acquired by Electronic Arts, and is recognized for developing "software toys" - games that focus on player creativity rather than winning or losing. The computer is a modeling tool where we can now *model dynamics* as opposed to just structure. What game designers did is they took these dynamic systems out to the user. This is a doubly difficult task. *You are actually designing the rules for this world where the user would then design another set of rules on top of it.* In the game industry it's important to understand, it's not immediately obvious, that you are designing two completely different processors. There's the computer in front of you that you are programming in some symbolic computer language. *The technology side*. The other processor that's really much more difficult to process and really where everything is happening at, is the players mind. Their imagination. And that's *the Psychology side*. For me games are half psychology and half technology. Players in these games are actually exploring the *possibility space*. You want to make this space as interesting as possible. A *fitness landscape* or adaptive landscape is used to visualize the relationship between genotypes and reproductive success. The fitness is the height of the landscape. As a game designer what you are trying to do is find interesting new rule sets that will lead to interesting new possibility spaces. Games have always been good at what I call the reptilian brain - fear, aggression, violence. The kind of adrenaline rush stuff. The things games are really struggling with now are the outer cerebral cortex - compassion, empathy, reflection. These are things often engaged in movies and deeper literature. Games primarily engage with pride and guilt in a way that no other medium as able to. Games are built of particular types of patterns. These are topologies we often see in games. Closed Networks, Open Networks, and Landscapes. We also have patterns in time. These are very important in games. Players are doing something and receiving feedback for it. We're dealing with these nested feedback loops. This is actually the process of how a player learns to complete a game. It is actually much more important to think through the failure states rather than the success states. You want to make the failures interesting and varied. Primarily you want to understand why the failure occurred. It's really nice when you can get players to interpret your bugs as a feature. That's kind of a real trick. The Calvin Factor. Find out what the objective of the game is, then do the exact opposite. A lot of these games can be viewed as models. We have a certain amount of computer code that we try to build that emerges into a much greater possibility space really. With the least amount of code create the largest and most impressive possibility space for the players. It turns out that game players are amazingly good at sniffing out the size of this space. Almost any kind within a few minutes of playing a game, can really get a sense of that possibility space. It's almost like the exact opposite of what science is doing. Science looks at the vast amount of data and tries to develop rules and finite elements of explaining it. Modeling building in science is quickly replacing experimentation as a primary tool. Models have value primarily in their abstraction, in the removal of detail, as retaining the essential detail that you need. Example is a transit map versus a satellite map when exploring a new city. That's what models are all about. They are a form of communication. The model building process goes way outside of the actual game. The player looks at the box art and imagines what it would be like to play it. We can control the level of abstraction that we are presenting to the player. 29 minutes --- ### **References** https://www.youtube.com/watch?v=CdgQyq3hEPo&list=WL&index=10&t=709s