Semantics for Improving Accuracy and Reducing Complexity in Strategic Decision Facilitation Tools

The work presented simplifies and makes accessible the process of using advanced probabilistic models to reason about complex scenarios without the need for advanced training. More specifically, it greatly simplifies the effort involved in building Bayesian Networks for making probabilistic predictions in complex domains. These methods typically require trained users with a sophisticated understanding of how to build and use these networks to predict future events. It entails the creation of simplified semantics that keeps the complexity of the methodology transparent to users. We provide more precise semantics to the definition of concept variables in the domain model, as well as using those semantics to assign more precise and robust meaning to predicted outcomes. This work is presented in the context of a tool and methodology, called DecAid, where complex cognitive models are created by defining domain-specific concepts using free language and defining relations and causal weights between them. In response to a user query the DecAid, unconstrained, directed graph is converted into a Bayesian network to enable predictions of events and trends.