Complexes

We compared entropy for texts written in natural languages (English, Spanish) and artificial languages (computer software) based on a simple expression for the entropy as a function of message length and specific word diversity. Code text written in artificial languages showed higher entropy than text of similar length expressed in natural languages. Spanish texts exhibit more symbolic diversity than English ones. Results showed that algorithms based on complexity measures differentiate artificial from natural languages, and that text analysis based on complexity measures allows the unveiling of important aspects of their nature. We propose specific expressions to examine entropy related aspects of tests and estimate the values of entropy, emergence, self-organization and complexity based on specific diversity and message length. Complexity measurement of natural and artificial languages Gerardo Febres, Klaus Jaffe, Carlos Gershenson http://arxiv.org/abs/1311.5427  

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(...) complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon. Gershenson, C. & M. A. Niazi (2013). Multidisciplinary applications of complex networks modeling, simulation, visualization, and analysis. Complex Adaptive Systems Modeling 1 :17   http://dx.doi.org/10.1186/2194-3206-1-17