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All of this is rather straightforward and uncontroversial (I hope), and noting that simulations like SimCity might work as effective models for actual cities is not terribly interesting—after all, this is precisely the purpose of simulations in general, and observing that the programmers at Maxis have created an effective simulation of the behavior of a real city is just to say that they’ve done their job well. Far more interesting, though, is a point that Wright makes later in the interview, comparing the considerations that go into the construction of models for simulation games like SimCity and more adversarial strategy games.

In particular, Wright likens SimCity to the ancient board game Go,[1] arguing that both are examples of games that consist in externalizing mental models via the rules of the game. In contrast to SimCity, however, Go is a zero-sum game played between two intelligent opponents, a fact that makes it more interesting in some respects. Wright suggests that Go is best understood as a kind of exercise in competitive model construction: the two players have different internal representations of the state of the game,[2] which slowly come into alignment with each other as the game proceeds. Indeed, except at the very highest level of tournament play, games of Go are rarely formally scored: the game is simply over when both players recognize and agree that one side is victorious. It’s not unusual for novice players to be beaten


  1. Go is played on a grid, similar to a chess board (though of varying size). One player has a supply of white stones, while the other has a supply of black stones. Players take turns placing stones at the vertices of the grid (rather than in the squares themselves, as in chess or checkers), with the aim of capturing more of the board by surrounding areas with stones. If any collections of stones is entirely surrounded by stones of the opposite color, the opponent “captures” the stones on the inside, turning them into the stones of her color. Despite these simple rules (and in contrast to chess, with its complicated rules and differentiated pieces), incredibly complex patterns emerge in games of Go. While the best human chess players can no longer defeat the best chess computers, the best human Go players still defeat their digital opponents by a significant margin.
  2. It’s important to note that this is not the same as the players having different models of the board. Go (like chess) is a game in which all information is accessible to both players. Players have different functional maps of the board, and their models differ with regard to those functional differences—they might differ with respect to which areas are vulnerable, which formations are stable, which section of an opponent’s territory might still be taken back, and so on.

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