 This approach has drawn strongly on ideas arising from complex systems, phase transitions, criticality, self-organized criticality, non-extensivity (see Tsallis entropy), q-Gaussian models, and agents based models (see agent based model); as these are known to be able to recover some of phenomenology of financial market data, the stylized facts, in particular the long-range memory and scaling due to long-range interactions.Within the subject the description of financial markets blindly in terms of models of statistical physics has been argued as flawed because it has transpired these do not fully correspond to what we now know about real finance markets.First, traders create largely noise, not long range correlations among themselves, except when they all buy or all sell, such as during a popular IPO or during a crash.