Context- and bias-free probabilistic mission impact assessment

Assessing and understanding the impact of scattered and widespread events onto a mission is a pertinacious problem. Current approaches attempting to solve mission impact assessment employ score-based algorithms leading to spurious results. We identify a fourfold problem with score-based algorithms: (1) score-based algorithms enforce deep training of experts to employed frameworks for specification (non-context-free), (2) require reference results for interpreting obtained results (non-bias-free), (3) require assessments outside of an experts' expertise (non-local), and (4) require validation of end-results against ground truth. This paper provides a formal, mathematical model for bias- and context-free mission impact assessment. Based on a probabilistic model we reduce mission impact assessment to a well-understood mathematical problem based on definitions from local expertise and allow for a validation at data level. This is useful for areas and applications where qualitative assessments are required, such as assessments in critical infrastructures or military contexts.