Stanford EDUC 299X - Complexity and Emergence Notes (3 pages)

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Complexity and Emergence Notes



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Complexity and Emergence Notes

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3
School:
Stanford University
Course:
Educ 299x - Visualizations in Learning

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Complexity and Emergence Notes Biology Life entropy in reverse matter natural tending toward increasing order Entropy and thermodynamics in general rules static systems dead systems When there is an energy flux the situation gets more complicated literally Consider water In a stagnant pond heat distributes evenly motions are random no discernible patterns or structures In a moving river complicated structures naturally emerge eddies vortices In a boiling pot of water hexagonal convection cells naturally emerge Where does freezing fit in Is it a different sort of emergence Nature is full of self organizing chaotic and emergent systems Flocking crystallization autocatalytic loops weather atoms molecules cells etc galaxies ferns ant colonies James Lovelock conjects that bottom up systems dominate Gaia Hypothesis as demonstrated by Daisy World a very nice agent y simulation shows that algae are as significant as geology Evolution time the observed world because there is not enough time E g random chemistry 12 billion years an enzyme However when emergence is thrown into the equation everything changes e g random chemistry autocatalytic loops t 12 billion years enzymes and much more Computer Science Artificial Systems Games chess in particular Chess has fewer then two dozen rules yet it is still not solved after many centuries of study Whereas tic tac toe is solved though it does have over 50 000 legal configurations Go is even more extreme John Holland the Father of genetic algorithms Early Neural Network Simulation In 1952 started out at IBM with the prototype of the first fully electronic digital calculator It could store a few thousand numbers Holland simulated a 512 neuron neural network model that demonstrated that reverberating assemblies could arise cell assemblies Inn the human CNS these correspond to different environmental stimuli Checkersplayer His partner Art Samuel wrote a checkersplayer on the same hardware It could learn and it learned to beat him Not a



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