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The principle of computational equivalence says that “systems found in the natural world can perform computations up to a maximal ("universal") level of computational power, and that most systems do in fact attain this maximal level of computational power.” | The principle of computational equivalence says that “systems found in the natural world can perform computations up to a maximal ("universal") level of computational power, and that most systems do in fact attain this maximal level of computational power.” | ||
The structure of a system need not be complicated for its behavior to be highly complex. Cellular Automata (e.g rule 30) can provide an example to provide models for a wide variety of complex natural systems. Even Turing machine model with one cell updating would be a model for complex behaviors. Universal machines that can do any kind of computations have many implications for natural science too | The structure of a system need not be complicated for its behavior to be highly complex. Cellular Automata (e.g rule 30) can provide an example to provide models for a wide variety of complex natural systems. Even Turing machine model with one cell updating would be a model for complex behaviors. Universal machines that can do any kind of computations have many implications for natural science too. | ||
It is easy to see the reflections of the Computational Equivalence Principle in Richard Dawkins Biomorphosis, Thomas Ray’s Tierra project, and Karl Sim’s virtual creatures. | It is easy to see the reflections of the Computational Equivalence Principle in Richard Dawkins Biomorphosis, Thomas Ray’s Tierra project, and Karl Sim’s virtual creatures. | ||