It runs until it reaches the bottom of the world. GO begins running the model with the currently set rule. AUTO-CONTINUE? automatically wraps to the top once it reaches the last row when the switch is on SETUP EXAMPLE initializes the rule settings according to the EXAMPLE slider The percentage on is determined by the DENSITY slider. SETUP RANDOM initializes the model with a percentage of the cells "on". time view of the cellular automaton's evolution. The rules are applied accordingly, and the next state of the cellular automaton appears in the row directly below, creating a space vs. There are 8 possible on/off rule configurations for every 3-cell neighborhood, each with a certain probability of turning on the cell below it at the next time step. HOW IT WORKSĪt each time step, every cell in the current row evaluates the state of itself and its immediate neighbors to the right and left. They are capable of mimicking many phenomena found in nature such as crystal growth, boiling, and turbulence. The behavior of these cellular automata tend to be very rich and complex, often forming self-similar tree-like or chaotic behavior. Stochastic cellular automata are models of "noisy" systems in which processes do not function exactly as expected, like most processes found in natural systems. (See the CA 1D Elementary model if you are unfamiliar with cellular automata.) Unlike most cellular automata, whose behavior is deterministic, the behavior of a stochastic cellular automaton is probabilistic. This is a one-dimensional stochastic cellular automaton. You can also Try running it in NetLogo Web If you download the NetLogo application, this model is included. Sample Models/Computer Science/Cellular Automata
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