Semantic Web Technologies integrate naturally with the worlds of open data science and open source machine learning, empowering better control and management of the risks and opportunities that come with increased digitization and model use The ongoing and accelerating digitisation of many aspects of social and economic life means the proliferation of data driven/data intermediated decisions and the reliance on quantitative models of various sorts (going under various hashtags such as machine learning, artificial intelligence, data science etc.
Risk, Randomness, Uncertainty and other Ambiguous Terms Uncertainty versus Risk is a popular discussion topic among risk managers, especially after major risk management disasters. The debate can get really hairy and drift into deep philosophical areas about the nature of knowledge etc. Yet the significance of having an as clear as possible language toolkit around these terms should not be underestimated. Practical risk management typically shuns too deep excursions into the meaning of things, yet that is not quite compatible with the use of sophisticated methods and tools (such as a Risk Model ) that assumes an understanding of the scope and limitations of “knowledge”.
Semantic Web Technologies: The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies. In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
Risk modeling is as much art as it is science: The Zen of Modeling aims to capture the struggle for risk modeling beauty An undocumented risk model is only a computer program A risk model that cannot be programmed is only a concept A risk model only comes to life with empirical validation Correct implementation of an imperfect model is better than wrong implementation of a perfect model In complex systems there is always more than one path to a risk model There are no persistently true models but there are many persistently wrong models Correlation is imperfectly correlated with causation Nirvana is the simplest model that is fit for purpose Hierarchical systems lead to hierarchical models.