Mathematical models are more and more used to support to solve multidisciplinary, real world problems of increasing complexity. They are often plagued by obstacles such as miscommunication between modellers with different disciplinary backgrounds leading to a non-transparent modelling process. Other difficulties include bad modelling practices, i.e. improper data handling, insufficient calibration, validation and uncertainty analysis, overselling model capabilities and incorrect use of model results in the decision process.
To tackle these difficulties, a body of knowledge on modelling, on the problem at hand and on the models itself to solve the problem, has been made explicit and organised in ontological knowledge bases with concepts and relations connecting the concepts. These ontological knowledge bases are furthermore structured in layers ranging from generic to detailed and specific. This facilitates communication between team members from different disciplines and also makes parts of the knowledge reusable. Tools to fill the knowledge bases and to support modelling projects (guidance from the knowledge base, logbook and project management) complete this better modelling practice framework.
Title thesis: "Better Modelling Practice: an ontological perspective on multidisciplinary, model-based problem solving"