  Inspired by widely-used techniques of causal modelling in risk, failure, and
accident analysis, this work discusses a compositional framework for risk
modelling. Risk models capture fragments of the space of risky events likely to
occur when operating a machine in a given environment. Moreover, one can build
such models into machines such as autonomous robots, to equip them with the
ability of risk-aware perception, monitoring, decision making, and control.
With the notion of a risk factor as the modelling primitive, the framework
provides several means to construct and shape risk models. Relational and
algebraic properties are investigated and proofs support the validity and
consistency of these properties over the corresponding models. Several examples
throughout the discussion illustrate the applicability of the concepts.
Overall, this work focuses on the qualitative treatment of risk with the
outlook of transferring these results to probabilistic refinements of the
discussed framework.
