The field of Alife uses simulation models to understand biological organization by abstracting crucial features and examining living systems 'as they could be'.
One of the most widespread representations used in Alife models is the cellular automaton (CA). This is a grid of cells in which each cell has a state (some property of interest) and is programmed to behave in identical fashion. Each cell has a neighborhood (usually the cells immediately adjacent to it) and the states of it neighbors affect changes in a cell's state. The most famous example is the Game of Life, in which each cell is either 'alive' or 'dead' at any time. Despite its extreme simplicity, the game showed that large numbers of interactions governed by simple rules lead to the emergence of order within a system. Cellular automata have been used to model many biological and ecological systems. In models of fires, epidemics, and other spatial processes, each cell represents a fixed area of the landscape and the cell states represent features of interest (e.g., susceptible, infected, or immune organisms in an epidemic model).
Other prominent ALife models include Tom Ray's Tierra model, which demonstrated adaptation within self-reproducing automata. Craig Reynolds' boids model demonstrated that flocking behavior emerges from simple interactions between individuals. James Lovelock's
Daisyworld model showed the potential for biotic feedback and adaptation to stabilize the biosphere.
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