Expert informants can be used as the
principal information source in the modeling of socio-techno-economic
systems or problems to support planning, foresight and decision-making.
Such modeling is theory-driven, grounded in expert judgment and
understanding, and can be contrasted with data-driven modeling
approaches. Several families of approaches exist to enable expert
elicited systems modeling with varying input information requirements
and analytical ambitions.

This paper proposes a novel
modeling language and computational process, which combines aspects from
various other approaches in an attempt to create a flexible and
practical systems modeling approach based on expert elicitation. It is
intended to have high fitness in modeling of systems that lack
statistical data and exhibit low quantifiability of important system
characteristics. AXIOM is positioned against Bayesian networks,
cross-impact analysis, structural analysis, and morphological analysis.
The modeling language and computational process are illustrated with a
small example model. A software implementation is also presented.