The growth of technology and the proliferation of information made modern complex systems more fragile and vulnerable. As a result, competitive advantage is no longer achieved exclusively through strategic planning but by developing an influential cadre of technical people who can efficiently manage and navigate modern complex systems. The dissertation aims to provide educators, practitioners, and organizations with a model that helps to measure individuals’ systems thinking skills, complex problem solving, personality traits, and the impacting demographic factors such as managerial and work experience, current occupation type, organizational ownership structure, and education level. The intent is to study how these skills, traits, and demographic factors can impact an individual’s abilities in working effectively with modern complex systems. These skills and traits also enable individuals to display distinctive patterns of thoughts in developing solutions that address complex technical problems. The dissertation further provides strategies for the management and enhancement of technical individuals based on assessing their performance. The model consists of three established instruments: Systems Thinking Skills, Perceived Complex Problem-Solving (PCPS), and Myers-Briggs Personality Type Indicator. These instruments are applied at the individual level to identify strengths and weak areas of improving an organization. In particular, PCPS is a researcher-developed instrument that captures the complex problem-solving perception of individuals. The different samples of the population for the dissertation come from students and practitioners.

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an approach that helps individuals better understand and effectively solve modern complex systems problems by encouraging holistic thinking. Systems thinking consists of two approaches holistic and reductionist views. This dissertation aims to study college engineering and non-engineering students’ preference for holistic thinking versus reductionist thinking, their ranking to the systems thinking dimensions, and whether this preference varies depending on demographics and general factors. Additionally, this study investigates the possibility of predicting the students’ preference for holistic thinking. The study uses the multi-criteria decision-making method, the Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process to determine the student’s preferences, and uses statistical analysis such as independent sample t-test and ANOVA to evaluate the factors. Also, the study uses machine learning classification models such as Logistic Regression, Support Vector Machine, Naïve Bayes, Decision Trees, voting classifiers, Bagging, and Random Forest to predict and evaluate the most predicting model. The results of the dissertation conclude that overall students prefer the reductionist approach and report the students’ preference towards dimensions of complexity, independence, uncertainty, systems worldview, and flexibility and the ranking difference based on some factors. Lastly, the results show that the students’ preference for holistic thinking can be predicted with a 77% accuracy using the Random Forest classifier.

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Die Analyse von Proxy-Zeitreihen aus Paläoklimaarchiven wie zum Beispiel Baumringen, Seesedimenten, Tropfsteinen und Eisbohrkernen mittels gefensterter Rekurrenznetzwerkanalyse ermöglicht die Identifizierung und Charakterisierung dynamischer Anomalien in der Klimavariabilität der Vergangenheit. Das Ziel der vorliegenden Arbeit ist die Entwicklung einer zuverlässigeren Routine zur gefensterten Rekurrenznetzwerkanalyse. Aufbauend auf dem bestehenden methodischen Rahmen werden die Bereiche der Phasenraumrekonstruktion und des Signifikanztests als verbesserungsfähig identifiziert. Deshalb werden verschiedene Methoden zur Rekonstruktion des Phasenraums aus unregelmäßig abgetasteten, verrauschten Daten verglichen. Außerdem wird ein allgemeiner flächenweiser Signifikanztest eingeführt, der, basierend auf einem ausgewählten Nullmodell, Korrelationen in den Analyseergebnissen numerisch abschätzt, um damit das Problem hoher Raten an falsch positiv signifikanten Ergebnissen zu adressieren. Im zweiten Teil der Arbeit wird die entwickelte Methodik genutzt, um die nichtlineare Variabilität des Klimas der Vergangenheit in Nord- und Südamerika zu untersuchen, indem vier reale Zeitreihen verschiedener Proxys studiert werden. Außerdem werden Proxy-System-Modelle genutzt, um auf die Frage der Eignung von Daten verschiedener Paläoklimaarchive zur Charakterisierung der Klimavariabilität mittels gefensterter Rekurrenznetzwerkanalyse einzugehen. Mit der Arbeit wird der Einsatz nichtlinearer Methoden zur Analyse von Paläoklima-Zeitreihen vorangebracht, das Potential und die Grenzen der gefensterten Rekurrenznetzwerkanalyse aufgezeigt und zukünftige relevante Fragestellungen, die die erhaltenen Ergebnisse und Schlussfolgerungen komplementieren können, identifiziert. / Studying palaeoclimate proxy data from archives such as tree rings, lake sediments, speleothems, and ice cores using windowed recurrence network analysis offers the possibility to characterise dynamical anomalies in past climate variability. This thesis aims at developing a more reliable framework of windowed recurrence network analysis by comparing different phase space reconstruction approaches for non-uniformly sampled noisy data and by tackling the problem of increased numbers of false positive significant points when correlations within the analysis results can not be neglected. For this, different phase space reconstruction approaches are systematically compared and a generalised areawise significance test which implements a numerical estimation of the correlations within the analysis results is introduced. In particular, the test can be used to identify patches of possibly false positive significant points. The developed analysis framework is applied to detect and characterise dynamical anomalies in past climate variability in North and South America by studying four real-world palaeoclimatic time series from different archives. Furthermore, the question whether palaeoclimate proxy time series from different archives are equally well suited for tracking past climate dynamics with windowed recurrence network analysis is approached by using the framework of proxy system modelling. This thesis promotes the use of non-linear methods for analysing palaeoclimate proxy time series, provides a detailed assessment of potentials and limitations of windowed recurrence network analysis and identifies future research directions that can complement the obtained results and conclusions.

Das Klimasystem der Erde besteht aus zahlreichen interagierenden Teilsystemen, die sich über verschiedene Zeitskalen hinweg verändern, was zu einer äußerst komplizierten räumlich-zeitlichen Klimavariabilität führt. Das Verständnis von Prozessen, die auf verschiedenen räumlichen und zeitlichen Skalen ablaufen, ist ein entscheidender Aspekt bei der numerischen Wettervorhersage. Die Variabilität des Klimas, ein sich selbst konstituierendes System, scheint in Mustern auf großen Skalen organisiert zu sein. Die Verwendung von Klimanetzwerken hat sich als erfolgreicher Ansatz für die Erkennung der räumlichen Ausbreitung dieser großräumigen Muster in der Variabilität des Klimasystems erwiesen. In dieser Arbeit wird mit Hilfe von Klimanetzwerken gezeigt, dass die Klimavariabilität nicht nur auf größeren Skalen (Asiatischer Sommermonsun, El Niño/Southern Oscillation), sondern auch auf kleineren Skalen, z.B. auf Wetterzeitskalen, in Mustern organisiert ist. Dies findet Anwendung bei der Erkennung einzelner tropischer Wirbelstürme, bei der Charakterisierung binärer Wirbelsturm-Interaktionen, die zu einer vollständigen Verschmelzung führen, und bei der Untersuchung der intrasaisonalen und interannuellen Variabilität des Asiatischen Sommermonsuns. Schließlich wird die Anwendbarkeit von Klimanetzwerken zur Analyse von Vorhersagefehlern demonstriert, was für die Verbesserung von Vorhersagen von immenser Bedeutung ist. Da korrelierte Fehler durch vorhersagbare Beziehungen zwischen Fehlern verschiedener Regionen aufgrund von zugrunde liegenden systematischen oder zufälligen Prozessen auftreten können, wird gezeigt, dass Fehler-Netzwerke helfen können, die räumlich kohärenten Strukturen von Vorhersagefehlern zu untersuchen. Die Analyse der Fehler-Netzwerk-Topologie von Klimavariablen liefert ein erstes Verständnis der vorherrschenden Fehlerquelle und veranschaulicht das Potenzial von Klimanetzwerken als vielversprechendes Diagnoseinstrument zur Untersuchung von Fehlerkorrelationen. / The Earth’s climate system consists of numerous interacting subsystems varying over a multitude of time scales giving rise to highly complicated spatio-temporal climate variability. Understanding processes occurring at different scales, both spatial and temporal, has been a very crucial problem in numerical weather prediction. The variability of climate, a self-constituting system, appears to be organized in patterns on large scales. The climate networks approach has been very successful in detecting the spatial propagation of these large scale patterns of variability in the climate system. In this thesis, it is demonstrated using climate network approach that climate variability is organized in patterns not only at larger scales (Asian Summer Monsoon, El Niño-Southern Oscillation) but also at shorter scales, e.g., weather time scales. This finds application in detecting individual tropical cyclones, characterizing binary cyclone interaction leading to a complete merger, and studying the intraseasonal and interannual variability of the Asian Summer Monsoon. Finally, the applicability of the climate network framework to understand forecast error properties is demonstrated, which is crucial for improvement of forecasts. As correlated errors can arise due to the presence of a predictable relationship between errors of different regions because of some underlying systematic or random process, it is shown that error networks can help to analyze the spatially coherent structures of forecast errors. The analysis of the error network topology of a climate variable provides a preliminary understanding of the dominant source of error, which shows the potential of climate networks as a very promising diagnostic tool to study error correlations.

Tropische Regenwälder sind durch anthropogene Aktivitäten gefährdet und wurden als Kippelement identifiziert. Ein Kippen in einen neuen Zustand könnte tiefgreifende Auswirkungen auf das globale Klima haben, sobald die Vegetation von einem bewaldeten in einen Savannen- oder Graslandzustand übergegangen ist. Waldbrände können die Grenze zwischen Savanne und Wald verschieben und somit das dynamische Gleichgewicht zwischen diesen beiden möglichen Vegetationszuständen unter sich änderndem Klima beeinträchtigen. In der vorliegenden Doktorarbeit wurde ein neues Erdsystemmodell entwickelt und angewendet, um explizit die Auswirkungen von Feuer, Klimawandel und Landnutzung auf eine potenzielle tropische Hysterese abzuschätzen. In den ersten beiden Teilen der Arbeit wurde das Vegetationsmodell LPJmL vor allem in Hinblick auf Feuersimulation verbessert und anschließend biophysikalisch an das Erdystemmodell CM2Mc gekoppelt. Im dritten Teil dieser Arbeit wurde das resultierende Modell CM2Mc-LPJmL schließlich angewendet, um wichtige biophysikalische Feuer-Vegetations-Klima-Rückkopplungen und einen potentiellen Kipppunkt bzw. eine Hysterese der tropischen Wälder zu untersuchen. Die Ergebnisse der Experimente zeigten, dass eine alleinige Klima Störung nicht zu einem großflächigen Kipppunkt tropischer Wälder führt. Andererseits führte die vollständige Entwaldung bei einer erhöhten CO2-Konzentration von über 450 ppm und die Wirkung von Waldbränden zu einer Verschiebung großer Teile des Amazonas Regenwaldes in einen stabilen Graslandzustand. Die Leistung dieser Arbeit ist die Entwicklung eines neuen Erdsystemmodells, das die Vorteile des umfassenden dynamischen Vegetationsmodells LPJmL und eines prozessbasierten Feuermodells mit dem geringen Rechenaufwand von CM2Mc verbindet. Diese Doktorarbeit untersuchte zum ersten Mal den expliziten Einfluss von Feuer auf tropische Kipppunkte und auf eine mögliche vegetative Erholung in einem umfassenden feuerfähigen Erdsystemmodell. / Tropical rain forests are endangered by anthropogenic activities and are recognized as one of the terrestrial tipping elements. An ecosystem regime change to a new state could have profound impacts on the global climate, once the biome has transitioned from a forest into a savanna or grassland state. Fire could potentially shift the savanna-forest boundary and hence impact the dynamical equilibrium between these two possible vegetation states under a changing climate. In this thesis, a new Earth system model was developed and applied to explicitly estimate the impact of fire, climate change and land-use on a potential tropical tipping point and hysteresis. The first part of this thesis describes the improvement of simulating fire within the dynamic global vegetation model (DGVM) LPJmL (Lund-Potsdam-Jena-managed-Land). In the second part, the improved LPJmL model was biophysically coupled to the Earth system model CM2Mc, which involved numerous changes in the original LPJmL model. In the third part of this thesis, the resulting model CM2Mc-LPJmL was finally applied to investigate important biophysical fire-vegetation-climate feedbacks and a potential tipping point and hysteresis of tropical forests. The results of the modeling experiments indicated that a sole climate disturbance does not lead to a large-scale tipping of tropical forests into a savanna or grassland state. On the other hand, complete deforestation alongside elevated CO2 above 450 ppm and the impact of fire led to a shift of large parts of the Amazon into a stable grassland state. The contribution of this thesis is the development of a new Earth system model, including the advantages of the comprehensive dynamic vegetation model LPJmL, a process-based fire model and the low computation cost of CM2Mc. This thesis studied for the first time the explicit impact of fire on tropical tipping points and a possible vegetation recovery in a comprehensive fire-enabled Earth system model.

Public infrastructure systems are crucial components of modern urban communities as they play major roles in elevating countries’ socio-economics. However, the inherent complexity and systemic interdependence of infrastructure construction/renewal projects have left sites hindered with multiple forms of performance disruptions (e.g., schedule delays, cost overruns, workplace injuries) that result in long-term consequences such as claims, disputes, and stakeholder dissatisfactions. The evolution of advanced data-driven tools (e.g., machine learning and complex network analytics) can play a pivotal role in driving improvements in the management strategies of complex projects due to such tools’ usefulness in applications related to interdependent systems. In this respect, the research presented in this dissertation is aimed at developing data-driven strategies geared towards a resilience-based approach to managing complex infrastructure projects. Such strategies can support project managers and stakeholders with data-informed decision-making to mitigate the impacts of systemic interdependence-induced risks at different levels of their projects. Specifically, the developed data-driven resilience-based strategies can empower decision-makers with the ability to: i) predict potential performance disruptions based on real-time and dynamic project conditions such that proactive response/mitigation strategies and/or contingencies can be deployed ahead of time; and ii) develop adaptive solutions against potential interdependence-induced cascade project disruptions such that rapid restoration of the most important set of performance targets can be restored. It is important to note that data-driven strategies and other analytics-based approaches are not proposed herein to replace but rather to complement the expertise and sensible judgment of project managers and the capabilities of available analysis tools. Specifically, the enriched predictive and analytical insights together with the proactive and rapid adaptation capabilities facilitated by the developed strategies can empower the new paradigm of resilience-guided management of complex dynamic infrastructure projects. / Thesis / Doctor of Philosophy (PhD)

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