Introduction > QGeNIe

QGeNIe is a qualitative abstraction of GeNIe and is an interactive development environment for rapid creation of qualitative causal models of uncertain domains. These models represent propositions by means of nodes in an acyclic directed graph. These nodes are always propositional and take two possible values: True and False. The colors of these nodes represent the degrees of truth of the propositions. Mathematically speaking, the colors represent the probability of the state True (or False - it is the users' choice). While QGeNIe users can define the color scale, the default is a range between red and green, representing undesirable and desirable states. QGeNIe allows for an interactive exploration of the models, examining the effects of observations and manipulations of individual variables.

There are two important applications of QGeNIe:

1.It is a standalone system for rapid creation of simplified causal models, useful in all kinds of strategic planning problems, where problems are complex enough to be a challenge for an unaided human mind and, at the same time too complex to model by means of fully specified, precise quantitative models. QGeNIe captures the knowledge and intuitions of decision makers and focuses group discussion on calculating the global effects of various decision options, which is for sufficiently complex problems a challenge for an unaided human mind. Working with QGeNIe helps with uncovering indirect pathways through which actions may propagate through the system, often with surprising effects. QGeNIe models applied in this way are typically projected in the meeting room of group meetings, where participants propose different actions and explore their consequences. QGeNIe offers what can be called an instant gratification interface in the sense of showing interactively the effects of observations and manipulations.

2.Models developed by means of QGeNIe are simple first-cut versions of quantitative probabilistic models. They can be exported to GeNIe for further refinement into more precise quantitative models.