Structured Expert Judgement in Adversarial Risk Assessment: An Application of the Classical Model for Assessing Geo-Political Risk in the Insurance Underwriting Industry

Abstract

For many decision and risk analysis problems, probabilistic modelling of uncertainties provides key information for decision-makers. A common challenge is lacking relevant historical data to quantify the models used in decision and risk analyses. Therefore, experts are often sought to assess uncertainties in cases of incomplete or non-existing historical data. As experts might be prone to cognitive fallacies, a structured approach to expert judgement elicitation is encouraged with the aim to mitigate such fallacies. Further, it enhances the assessment’s transparency. An area, in which the assessment and modelling of uncertainties are particularly challenging due to incomplete or non-existing historical data is adversarial risk analysis (ARA). In contrast to more traditional application areas of decision and risk modelling, in ARA intelligent adversaries add more complexity to assessing uncertainties given that their behaviour and motivations can be versatile so that they adapt and react to decision-makers’ actions, including actions based on traditional risk assessments. This often inhibits the availability of historical data. This additional complexity is also shown by the challenges that machine learning methods face when informing adversarial risk assessments. As such, using expert judgements for assessing adversarial risk (at least supplementary) often provides a more robust decision. In this chapter, we discuss the importance of structured expert judgement for ARA and present an application of the Classical Model as a structured way for eliciting uncertainty from experts on geo-political adversarial risks. We elicit the frequency of terrorist attacks and strikes, riots and civil commotions (SR & CCs), including insurgencies and civil wars, in various global regions of interest. Assessing such uncertainties is of particular interest for insurance underwriting.