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## <span dir="">Modelling Complex Systems</span>
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_C. Ates_
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## <span dir="">Modelling Complex Systems</span>
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<span dir="">Complexity is associated with the emergent behaviour, where the whole presents more than the summation of the individual parts / actions. Here the key word is the “summation”, that is, the linear behaviour. Complex systems, on the other hand, exhibit nonlinear relationships, such as schools of fish, art colonies. An ant by itself is a relatively simple system when isolated, yet what they can achieve as colony is amazingly complex. This synergetic effect, nonlinearity so to say, is what we observe and interpret as self-organization, which increases the possibility of realizing rare events.</span>
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How can we analyse and model the behaviour of such systems? This is typically done via finding recurrent patterns in the changing states of a system. A nice analogy here is the game of chess: (i) 32 elements of 6 types, (ii) a limited number of rules and (iii) a limited space of 8x8 squares. Yet each move has about 10 possibilities – in an average game of 50 moves, this leads to 10<sup>50</sup> sequences. A number greater than (the probable) number of atoms in the universe! Hence, we end up with a practically infinite possibility space of game where no two games can be identical. Herein, “How can someone or a code can play learning it efficiently?” is a very valid question. <span dir=""> </span>The short answer is the existence of recurring patterns that offer similar possibilities and this is what we aim to capture while modelling nonlinear systems with ML.
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