Knowledge relatedness and diversification in complex socioeconomic systems

Recent approaches in applied complexity research use methods from network science, diversity measurement and econometrics to (1) reveal the relatedness between different knowledge fields and to (2) predict the path of knowledge diversification in complex socioeconomic systems (Hidalgo et al., 2007; Guevara et al. 2016ab, Hartmann et al., 2017ab). Here we show two applications of these methods to global maps of science (Guevara et al., 2016b) and labor market dynamics (Hartmann et al. 2017ab). In the first case, we use data from over 12 million publications, 300,000 scholars and 300 scientific fields to evaluate the accuracy of the research space—a new map of science based on co-publications— to predict the knowledge diversification of scientists, universities and countries. In the second case, we make use of an occupational dataset on 40 million Brazilian employees to map the knowledge relatedness between 600 occupations and 670 industries, and use logistic regression to predict the regional diversification dynamics of 558 Brazilian micro-regions. Both applications show that new interdisciplinary methods from complexity research can help to move beyond simplifying equilibrium approaches towards a more scientifically accurate and more practical understanding of economies and societies as complex evolving systems.