This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.
Department of Systems and Computer Engineering

Gomes, V.M. (Viviane M.), Paiva, J.R.B. (Joao R. B.), Reis, M.R.C. (Marcio R. C.), Wainer, G.A, & Calixto, W.P. (Wesley P.). (2019). Mechanism for Measuring System Complexity Applying Sensitivity Analysis. Complexity, 2019. doi:10.1155/2019/1303241