The challenge when constructing a risk assessment model is to fully understand the mechanisms at work and then to identify the optimum number of detailed variables for the model’s intended use. This follows the reductionist approach previously discussed—breaking the problem down into pieces for later reassembly into meaningful risk estimates.
We must understand and embrace the complexity in order to achieve the optimum amount of simplification—this is the process of ‘intelligent simplification.’ The best approach is to begin with the robust solution, including all details and all nuances that make up the real-world phenomena. Only then can a shortcut be contemplated. That way, what is sacrificed by the simplification is clear.