Abstract: A computer implemented method includes processing a deterministic factual graph to produce superfacts. The deterministic factual graph has deterministic factual graph leaf nodes individually resolving facts to discrete-valued outcomes and parent nodes of the deterministic factual graph leaf nodes resolving the discrete-valued outcomes to superfacts. Each superfact is a qualitative characterization summarizing discrete-valued outcomes. A stochastic factual graph is processed to produce a risk inference measure. The stochastic factual graph has stochastic factual graph leaf nodes incorporating the facts or superfacts. The stochastic factual graph is a Bayesian network where each stochastic factual graph node, except for a base node, is associated with a probability function, and edges between stochastic factual graph nodes represent conditional dependencies. The risk inference measure is compared to an escalation threshold.