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Emergence in Games
Many studies of emergence look at systems such as The Game of Life, which are autonomous and do not require active human participation in order to function. But Life is not really a game. Not only does it lack a quantifiable goal, but it is not a system of conflict in which one or more players participate. It is true that a user can set up the initial set of cells, which are either on or off. But once the game begins, Life runs all by itself. The behavior of Life, once the program is in motion, is divorced from human interaction.
Games are not like this at all. Games require players, and those players make decisions that move the game forward. The concept of meaningful play, for example, is premised on the idea that there are perceivable relationships between player action and game outcome. The participatory nature of games makes them tricky systems to examine, because unlike the "game" of Life, a true game will always include one or more players inside the system making decisions. In games, emergence arises through the interaction of the formal game system and decisions made by players. A wonderful example of this kind of emergence is bluffing in Poker. The strategy of bluffing—pre-tending to have a better hand than you actually do—is a key component of the game. But it is not explicitly described in the game rules. Bluffing is simply an emergent behavior that occurs in the game, facilitated by the betting procedure, the fact that players' hands are hidden, and the desire for players to win the conflict of the game. Bluffing is like the glider guns in The Game of Life, present in the space of possibility, though never explicitly stated in the rules. Even the simplest of games can generate vast and unexpected behaviors. John Holland makes this clear in a discussion of board games: Board games are a simple example of the emergence of great complexity from simple rules or laws. Even in traditional 3-by-3 Tic-Tac-Toe, the number of distinct legal configurations exceeds 50,000 and the ways of winning are not immediately obvious. The play of 4-by-4-by-4, three-dimensional Tic-Tac-Toe, offers surprises enough to challenge an adult. Chess and Go have enough emergent properties that they continue to intrigue us and offer new discoveries after centuries of study. And it is not just the sheer number of possibilities. There are lines of play and regularities that continue to emerge after years of study, enough so that a master of this century would handily beat a master of the previous century.[8]
Even simple rule sets can create tremendous amounts of emergent complexity. The way that this complexity manifests does not always intuitively follow from the rules. For example, compare two of the games that Holland mentions, Chess and Go. They are among the world's most ancient and sophisticated games, and they are similar in many respects. Both are two-player, turn-based strategy games played with pieces on a gameboard grid. Which game is more complex—at least from a formal, mathematical point of view? Looking at the rules, Chess would appear to be the more complex game. In Chess, there are six different pieces, each with unique ways of moving about the board. There are also numerous "special" rules, such as a pawn's opening move, queening a pawn, and castling a king. In contrast, Go has much simpler rules. There is only one kind of piece, and once placed on the board, a piece does not move on the grid. Despite Go's simpler set of rules, however, it is a mathematically more complex game. One demonstration of this is the development of artificial intelligence computer programs to play the games. Programming software opponents for both Go and Chess are long-standing computer science problems that have received attention around the world from both academics and commercial game developers over the last few decades. Although the focus of Go research has been in Asia, where the game is more common, the amount of attention given to programming AI for the two games is roughly equivalent. A few years ago, when Gary Kasparov lost to IBM's Deep Blue in Speed Chess, computers arguably exceeded human mastery of Chess. But a Go program has yet to be written that can challenge an advanced player of the game.
Why the discrepancy? Go demonstrates a higher degree of emergent complexity, arising out of the coupled interactions between game pieces. Like a cellular autonoma, each piece in Go forms relationships with neighboring pieces and empty spaces. When these simple relationships are multiplied across the grid of the gameboard (which is larger than a Chess grid), the linked interrelationships of the pieces adds up to a degree of complexity that exceeds Chess in raw numerical possibilities. The point of this comparison is not to identify which game is a superior game. Chess and Go are both noble pastimes for game players, and playing either one well is a demanding intellectual pursuit. The lesson here is that more complex rules do not necessarily equal more complexity in the system. Paradoxically enough, the simpler rules of Go generate a higher degree of emergent complexity. [8]Holland, Emergence, p. 22–23.
