| Popis: |
Causal maps use directed network structures to represent causality between concepts in a system, and are vital for conceptual modeling- a core activity in the field of Modeling & Simulation (M&S). Simulation models are generated from collections of maps, introducing scalability challenges as modelers are unable to effectively process large collections manually, or when maps contain many concepts. Despite this, there is a paucity of research on reducing human interventions across the various steps in causal mapping. In this thesis, we develop Network Science tools to overcome these challenges and present a framework for processing maps automatically. First, we demonstrate how the accepted practice of manually transforming evidence into maps introduces significant bias and that indirect elicitation must be fully documented. To further reduce the risk of bias from modelers, we present and evaluate a method to combine maps using semantic and causal information. We then develop a systematic, data-driven approach to extract a useful model from combined maps, in part by characterizing whether recently proposed metrics on identifying central concepts are feasible in large maps. Our approach is validated through studies on suicide modeling and can subsequently be used to process causal maps in many other research areas. |
