Researchers in a joint project need to develop a common language through which they can express their added value in terms of improving the risk and reward of investment or operational business opportunities. Such common language also provides a framework for comparing decision-alternatives in a consistent way, thereby giving insight to decision-makers. Given an integrated technical-to-business model, uncertain model input variables should first be specified as probability density functions (pdf’s), which give a frequency distribution within the uncertainty range. Next, the model is run using the Monte Carlo sampling process and histograms of the model’s output-KPIs (Key Performance Indicators, i.e. decision metrics such as Net Present Value, Internal Rate of Return etc.) can be computed (Figure 1).
These output-histograms represent the uncertainty in the project’s forecast performance from which the project’s expected utility and risk can be computed (risk = probability of an undesired outcome multiplied by the value of the undesired outcome). Given the prior knowledge (i.e. before doing the research) on input variables (expressed as a priori pdf’s) and on the relationships between the model’s input variables governing the system’s state-variables, researchers should update the model with the new pdf’s and relationships resulting from their research. Thus, they can demonstrate, for a particular case study, their added value in terms of updated KPI-histograms (Figure 2). The method is generic, enabling the multi-disciplines to speak the same language, integrate their findings into a common formalism and assess the added value of their new research.