Reducing uncertainty in discrete models

Reducing uncertainty in discrete models

Fuzzy Cognitive Mapping (FCM) represents the ‘mental model’ of individuals as a causal network equipped with an inference engine. As individuals may disagree or evidence is insufficient, causal links may be assigned a range rather than one value. When all links have range, the massive search space is a challenge to running simulations. We are designing, implementing, and evaluating new approaches to identify which ranges are important and simplify models accordingly. This involves creating a new Python library for Fuzzy Cognitive Maps and using Design of Experiments (DoE) techniques to efficiently understanding the behavior of a model. Our solutions are currently able to simplify models with a few dozen of links using widely accessible hardware such as personal laptops. Larger models still incur a significant computational load and we are thus using high-performance computing to accelerate computations, using GPUs and CPUs.