By modeling potential policy options before implementing them, decision makers can see which intervention strategies and associated resource deployments provide the best chance of success (fewest expected deaths, for example). Ambitious but realistic goals for CS models are that they be explanatory and through experimentation and data collection we develop a medium-level of confidence in their predictive capability.
Complex systems modeling is challenging. For the case of highly contagious disease, for instance, although network structure captures heterogeneity better than compartmental models, insufficient data to parameterize network structure results in massive new uncertainties. To achieve validated CS model output, a balanced approach is needed which incorporates complexity better than compartmental models without creating the uncertainty problems of “photo-realistic” disease spread simulations.
Building Confidence in Complex Systems Models for Reducing Population Health Risks, Complex Systems Design & Management Asia 2016, February 2016 [PDF]