Die Arbeit adressiert die empirisch beobachtete Fehleranfälligkeit einer populären Teilmenge modellgetriebener Decision Support Systeme (MDSS), die mit Spreadsheet-Werkzeugen erstellt werden. Der Fokus liegt bei diesen DSS auf der logischen Formulierung realer Zusammenhänge im multidimensionalen Kontext. Analysen einer empirisch belegten Fehlerrate weisen auf proprietäre, oft wenig intuitive Modellierungssprachen der am Markt befindlichen Produkte hin, die eine unnatürliche, fehleranfällige Modellierung provozieren. In Analogie zu den positiven Erfahrungen einer konzeptuellen Datenmodellierung schlägt die vorliegende Arbeit eine konzeptuelle Modellierungsebene vor, um diesem Problem zu begegnen. Eine Untersuchung vorhandener konzeptueller Modellierungsansätze kommt zu dem Ergebnis, dass bei der Konstruktion der konzeptuellen Modellierungsebene für MDSS eine Erweiterung oder Änderung eines bestehenden konzeptuellen Modellierungsansatzes nicht in Betracht gezogen werden kann. Basierend auf diesen Erkenntnissen wird eine konzeptuelle Modellierungsebene entwickelt, welche aus den drei Phasen Modellkonfiguration, Modelltransformation und Modellkompilierung besteht. In der Konfigurationsphase werden sämtliche Modellelemente (Dimensionen, Dimensionsausprägungen, Gruppen und Formeln) definiert und die Überlagerung von (vektoriellen) Formeln innerhalb des Modells bewusst zugelassen. Die Transformationsphase stellt die Konsistenz der modellierten Strukturen in Bezug auf deren Eindeutigkeit sicher, indem sämtliche Überschneidungen von Formeln innerhalb von Modellzellen in einem systemunterstützten Benutzerdialog beseitigt werden. Im dritten Teil des Konzepts, der Kompilierungsphase, werden die überschneidungsfreien, konzeptuellen Modelle in proprietäre Modellinstanzen übersetzt, sodass sie in Anwendungssystemen für Analysen verwendet werden können. Die prototypische Implementierung erfolgt anhand eines Modellierungseditors (Java) zur Umsetzung der Konzeptphase Modellkonfiguration. Modelltransformation und –kompilierung werden auf Basis von Microsoft Excel und VBA Programmen prototypisch umgesetzt. Abschließend kann mittels einer Evaluation auf positive Effekte des erarbeiteten Konzepts für die Qualität modellgetriebener Decision Support Systeme geschlossen werden.

In this thesis, the central interest is to better understand how participatory methods applied during participatory processes in natural resources management can serve as nuclei for social learning. Thereby, the main focus is on learning via interaction in groups. My approach begins with the aim of developing an analytical framework which reflects the main processes that are effective within participatory methods. The framework presents an analytical tool, including proposed methods to monitor and compare the results of participatory approaches with respect to social learning. Building upon this framework, I develop an agent-based model to simulate and explore group dynamics. This model is intended to support a theoretical exploration of whether or not and if so, at what stage, personal views of a problem evolve into a shared understanding of a problem (which can be seen as a key element of social learning), and an assessment of how individual mental models and group properties relate to each other. Results of the model are interpreted to offer suggestions about factors hindering or fostering social learning during the application of participatory methods.

In this thesis, a conceptual framework and related methodological approaches for complex system design are developed and tested. The approaches are based on insights from the fields of Systems Engineering (SE) and System-of-Systems Engineering (SoSE), as well as Natural Resources Management (NRM). The focus of this thesis is on: 1) the development of the System-of-Systems Design Framework “FRESCO”, 2) the development of a methodological framework for participatory systems design, 3) the application of the framework in two case studies, and 4) the development of an evaluation scheme to qualitatively measure the effectiveness of the methodological framework. The overall objectives of this doctoral dissertation are to highlight synergies between SE and NRM and to develop a methodological framework for designing decision making processes in a human-nature-technology context. The complexity of coupled and complex adaptive systems (CAS) such as the Water-Energy-Food Nexus (WEF-Nexus) and sustainability strategies, influences the design of decision-making processes and strategy building. Integrated process design which is promoted by the developed frameworks can assist in such tasks on an urban, regional, and national level.

Livestock grazing is the most important type of land-use in arid and semi-arid regions. In these regions, uncertain and highly variable climate conditions cause scarce and spatio-temporally variable resource availability. The major challenge to livestock grazing is the efficient utilisation of these resources without running the risk of degradation. Therefore, well adapted grazing strategies that consider both local environmental characteristics and the farmers' individual needs and perceptions are crucial for sustaining human livelihoods. Particularly, rotational grazing is presumed to render adaptation to spatio-temporal heterogeneities possible. A systematic investigation, however, that analyses the interrelations between the major components of rotational grazing systems on appropriate spatial and temporal scales was missing so far. This doctoral thesis investigates different management strategies for sustainable livestock grazing in semi-arid rangelands. Using an integrated modelling approach, it enters into the question: how to adapt grazing systems to spatio-temporal heterogeneous rangeland conditions, variable and changing climate conditions, and different individual needs and goals of livestock farmers? In order to address these issues, the taken approach tackles both methodical challenges and applied concerns. In the first part of this study, a generic modelling framework is developed that incorporates important components of grazing systems on appropriate spatial and temporal scales. To parameterise the model, a pattern-oriented approach is developed that uses qualitative patterns to derive a broad range of plausible parameter sets supporting a general model analysis. In the second part, a variety of management strategies is explored under different climatic, ecological, and economic conditions. The research focuses in particular on combined effects between and relative importance of different management components. The question how the results of different management strategies depend on the type of vegetation is investigated. Furthermore, the performance of rotational grazing strategies is analysed under different economic requirements and rainfall conditions. The study also identifies management strategies that are suitable to adapt a grazing system to spatio-temporally variable rangeland conditions. Overall, this thesis contributes to a general understanding of basic principles for adaptation to spatio-temporal heterogeneities as well as the interplay of different management components. The results allow an evaluation of management strategies for specific situations and the identification of strategies that are robust to a broad range of situations including different aspects of global change.

A fundamental change in societal values and economic structures is required to address increasing pressures on ecosystems and natural resources. Transition research has developed in the last decades to analyze the co-dynamics of technological, institutional, social and economic elements in the provision of key functions such as energy, water and food supply. This doctoral dissertation provides conceptual and methodological contributions to the pro-active governance of sustainability transitions. Three research gaps are identified that are addressed in this dissertation. First, a comprehensive conceptualization of learning in sustainability transitions is currently missing that comprises learning at multiple societal levels (ranging from individuals to policy-actors). Learning concepts are often not explicitly discussed in transition research even though learning is considered as fundamental for innovation processes, niche formation and development as well as breakthrough and diffusion of innovations. Second, methods for the analysis and design of transition governance processes are lacking that specify case-specific intervention points and roles of actors in the implementation of innovations. Third, participatory modeling approaches are only applied to a limited extent in transition research despite a high potential for supporting communication and learning. The conceptualization of multi-level learning developed in this doctoral research conceptualizes learning at different societal levels as specific learning contexts ranging from individual and group contexts to organizational and policy contexts. The conceptual framework further differentiates between learning processes, intensity, objects, outcomes, subjects and factors, allowing for a more detailed analysis of learning within and across learning contexts. Thus, learning contexts can be linked by processes that involve actors from different learning contexts (e.g., community groups and policy-makers), as well as exchanges of physical aspects, institutions and knowledge (in the form of ‘learning factors’). This research has also provided a classification of model uses in transition research that supports a purposeful discussion of the opportunities of modeling and promising future research directions. The methodology developed in this doctoral research aims at the analysis and design of transition governance processes by specifying the various opportunities to contribute to sustainability transitions through purposeful action at different societal levels, as well as related roles of stakeholders in implementing such processes of change. The methodology combines different streams of previous research: 1) a participatory modeling approach to identify problem perceptions, case-specific sustainability innovations as well as related implementation barriers, drivers and responsibilities; 2) a systematic review to identify supportive and impeding learning factors from the general literature that can complement case-specific factors; and 3) a method for the analysis and design of case-specific transition governance processes. Three case studies in Canada (topic: sustainable food systems), Cyprus (water-energy-food nexus) and Germany (sustainable heating supply) have been selected to test and iteratively develop the methodology described above. The results for each case study reveal that there are learning objects (i.e., learning requirements) in all learning contexts, which underscores the importance of multi-level learning in sustainability transitions, ranging from the individual to the group, organizational and policy levels. Actors have various opportunities to actively facilitate societal transformations towards sustainable development either directly through actions at their particular societal levels (i.e., context-internal learning) or indirectly through actions that influence learning at other societal levels. In fact, most of the learning factors require cooperation across learning contexts during the implementation process. The comparing of learning factors across case studies underline the importance of several factor categories, such as ‘physical a ‘disturbance or crisis’, ‘information and knowledge’. Of the 206 factors identified by stakeholders, 40 factors are case-specific and not contained in the general, review-based factor list. This underscores the value of participatory research, as general, top-down analyses might have overlooked these case-specific factors. The methodology presented in this dissertation allows for the identification and analysis of case-specific intervention points for sustainability transitions at multiple societal levels. The methodology furthermore permits the analysis of interplay between individual, group, organizational and policy actions, which is a first step towards their coordination. The focus on sustainability innovations links the broad topic of sustainability transitions to a set of opportunities for practical interventions and overcoming their implementation barriers. The methodology presented allows for the analysis and design of these interlinkages between learning contexts. While the methodology cannot provide any ‘silver bullets’ for inducing sustainability transitions, it is flexible enough to identify an appropriate abstraction level for analyzing and designing transition governance processes. The methodology developed in this doctoral research also provides several contributions for the development of participatory modeling methods in transition research. Thus, the participatory method supports an integrated analysis of barriers and drivers of sustainability innovations, and allows application in practice and education. The concepts and methods developed in this research project allow for reflection on transition governance processes from a systemic viewpoint. Experiences in the case studies underline the applicability of the concepts and methods developed for the analysis of case-specific transition governance processes. Despite substantial differences in the geographic location, culture and topics addressed, all case studies include promising sustainability innovations and the engagement of multiple actors in their implementation. The diversity and multitude of initiatives in the case study regions provides an optimistic outlook on future opportunities for large-scale sustainability transitions.