Mid Sweden University

Simulation modeling has long been used to analyze complicated systems by simplifying real-world processes. These models support knowledge-based decision-making, especially when dealing with imprecise concepts and complex problems. This thesis explores how modeling can improve understanding of complex social processes through the insights generated at the model design of modeling. It focuses on the Swedish case of accessibility planning for sustainable regional development. The thesis examines how accessibility is affected by a lack of institutional cooperation and conflicting definitions. The research adopts the perspective of complex adaptive systems and participatory modeling in several steps covered by the included papers and a discussion concerning the implications of their collective results. First, it investigates the current challenges in planning for accessibility in sparsely populated regional areas as a case study problem, reviewing the methods of accessibility evaluation, its peculiarities in a regional context, and develops the requirements towards a system/tool that answers these requirements. Second, it engages the stakeholders represented by planners and decision-makers from the municipalities within the region to define the basis for developing a model of the mentioned system based on their expert insights into the regional accessibility context – its concept, goals, and challenges. Third, based on the gained insights about the disagreement and ambiguity in defining and deploying the accessibility concept, it develops a research framework that enables to unpack complex concepts such as regional accessibility into more understandable and interpretable associations, engaging stakeholders in the definition process, and consequently, model design based on this process. Fourth, it designs and implements a regional accessibility model, exploring the insights gained from model design process, simulation dynamics, and simulation results to create a cross-municipal vision for accessibility planning through understanding and interpreting its complexity. The framework developed and adopted for this purpose unpacks complex concepts into key modeling variables for the development of conceptual and implemented models, including dynamic simulation models.