Uncertainty and sensitivity analysis to complex systems

Abstract

In the complexity modeling, variance decomposition technique is widely used for the quanti¯cation of the variation in the output variables explained by covariates. In this work, the satisfaction of sampling-based variance decomposition strategy (SVDS) is¯rstly testi¯ed in the implementation of an analytic method for uncertainty and sensitivity analysis (UASA) of complex systems. Results suggest that SVDS may overvalue the impacts from individual covariates alone but underestimate the e®ects from their interactions when the model under discussion involves the interaction e®ects of nonlinear problems of individual covariates. Following the phenomenon, a modi¯cation of SVDS is proposed to generate sensitivity measures that well coincide with the analytic method. The testi¯ed strategy, together with our proposed modi¯cation, is then employed to clarify the roles of infectious rate and recovered rate, as well as of their interaction, in the estimation of equilibrium state (ES) for both SIR and SIS models. Results demonstrate that infectious and recovered rates almost play the *Corresponding author.

Quantification by simulation of the effect of herd management practices and cow fertility on the reproductive and economic performance of Holstein dairy herds

“…A complete factorial plan (simulation plan 2) was implemented to quantify the weight of the 6 factors studied and their interactions on the reproductive and economic outputs of the model, with a total of 216 simulated scenarios. A sensitivity analysis, using sensitivity indices (IS), was used to compute the weight of each individual factor on this complex simulated system (Zhu et al, 2017). These IS were estimated from the variance decomposition in the ANOVA test.…”