Editing Complex system (section)

===Complexity economics===
Over the last decades, within the emerging field of [[complexity economics]], new predictive tools have been developed to explain economic growth. Such is the case with the models built by the [[Santa Fe Institute]] in 1989 and the more recent [[economic complexity index]] (ECI), introduced by the [[MIT]] physicist [[Cesar A. Hidalgo]] and the [[Harvard]] economist [[Ricardo Hausmann]].

[[Recurrence quantification analysis]] has been employed to detect the characteristic of [[business cycles]] and [[economic development]]. To this end, Orlando et al.<ref>{{Cite journal |last1=Orlando |first1=Giuseppe |last2=Zimatore |first2=Giovanna |date=18 December 2017 |title=RQA correlations on real business cycles time series |journal=Indian Academy of Sciences – Conference Series |volume=1 |issue=1 |pages=35–41 |doi=10.29195/iascs.01.01.0009 |doi-access=free}}</ref> developed the so-called recurrence quantification correlation index (RQCI) to test correlations of RQA on a sample signal and then investigated the application to business time series. The said index has been proven to detect hidden changes in time series. Further, Orlando et al.,<ref>{{Cite journal |last1=Orlando |first1=Giuseppe |last2=Zimatore |first2=Giovanna |date=1 May 2018 |title=Recurrence quantification analysis of business cycles |url=https://www.sciencedirect.com/science/article/abs/pii/S0960077918300924 |journal=Chaos, Solitons & Fractals |language=en |volume=110 |pages=82–94 |bibcode=2018CSF...110...82O |doi=10.1016/j.chaos.2018.02.032 |issn=0960-0779 |s2cid=85526993}}</ref> over an extensive dataset, shown that recurrence quantification analysis may help in anticipating transitions from laminar (i.e. regular) to turbulent (i.e. chaotic) phases such as USA GDP in 1949, 1953, etc. Last but not least, it has been demonstrated that recurrence quantification analysis can detect differences between macroeconomic variables and highlight hidden features of economic dynamics.