We examine the applicability of segregation and tipping points on the labor market of teachers to reconcile if higher levels of ethnic segregation in schools are responded to with labor movement. The main results derived from the research is that overall, there is no statistical significance that affects the sorting of teachers across schools. While the raw data show that higher levels of ethnic segregation yield significant results that give meaning to the idea that qualified teachers move out at different ethnic tipping points in schools, there is no significant effect of ethnic segregation on the number of teachers or the composition of teachers using a fixed effects model. The addition of size and parents' education as variables further emphasizes that negative effects in Malmö municipality cannot be found alongside different tipping points. The result of the study speaks to the number of foreign students in a school being largely irrelevant for teacher mobility across schools in a municipality.

‘Tipping points’ in the climate system are characterised by a nonlinear response to gradual forcing, and may have severe and wide-ranging impacts. One of the best ways to identify and potentially predict threshold behaviour in the climate system is through analysis of palaeoclimate records. It has been suggested that early warning signals occur on the approach to a tipping point, generated from characteristic fluctuations in a time series as a system loses stability. Although early warning signals have been found in climate models and high-resolution marine and ice core palaeodata, studies from terrestrial records are lacking. In this study, a number of Pleistocene terrestrial records were selected to represent a range of regions strongly influenced by different climate modes which are thought to be capable of displaying threshold behaviour. These records included lake sediments from the North Atlantic, tree-rings from the South Pacific, a Chinese speleothem and were complemented by a new Greenland ice core chronology. Recently developed methods to detect signals of ‘critical slowing down’, ‘flickering’, and stability changes on the approach to a tipping point were utilised. Specific methodological issues arising from analysing palaeoclimate data were also investigated using a simple bifurcation model. A number of key criteria were found to be necessary for the reliable identification of early warning signals in palaeoclimate records, most crucially, the need for a low-noise record of sufficient data length, resolution and accuracy. Analysis of a Chinese speleothem identified the East Asian Summer Monsoon as an important climate ‘tipping element’, which may display a cascade of impacts. However, in some cases where early warning signals may fail, a deeper understanding of the underlying system dynamics is required to inform the development of more robust system-specific indicators. This was exemplified by the analysis of an abrupt, centennial-duration shutdown recorded during the Younger Dryas Chronozone in New Zealand, which demonstrated no slowing down, consistent with a freshwater pulse into the Southern Ocean. This study demonstrates that time series precursors from palaeoclimate archives provide a means of useful forewarning of many potential climate tipping points.

abstract: Complex dynamical systems are the kind of systems with many interacting components that usually have nonlinear dynamics. Those systems exist in a wide range of disciplines, such as physical, biological, and social fields. Those systems, due to a large amount of interacting components, tend to possess very high dimensionality. Additionally, due to the intrinsic nonlinear dynamics, they have tremendous rich system behavior, such as bifurcation, synchronization, chaos, solitons. To develop methods to predict and control those systems has always been a challenge and an active research area. My research mainly concentrates on predicting and controlling tipping points (saddle-node bifurcation) in complex ecological systems, comparing linear and nonlinear control methods in complex dynamical systems. Moreover, I use advanced artificial neural networks to predict chaotic spatiotemporal dynamical systems. Complex networked systems can exhibit a tipping point (a “point of no return”) at which a total collapse occurs. Using complex mutualistic networks in ecology as a prototype class of systems, I carry out a dimension reduction process to arrive at an effective two-dimensional (2D) system with the two dynamical variables corresponding to the average pollinator and plant abundances, respectively. I demonstrate that, using 59 empirical mutualistic networks extracted from real data, our 2D model can accurately predict the occurrence of a tipping point even in the presence of stochastic disturbances. I also develop an ecologically feasible strategy to manage/control the tipping point by maintaining the abundance of a particular pollinator species at a constant level, which essentially removes the hysteresis associated with tipping points. Besides, I also find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former, large degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly irrelevance of linear controllability to these systems. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, I uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

The adoptation of environmentally friendly behaviour is rather slow, although the climate crisis is pressing. This thesis aims to understand the slow adoption of environmentally friendly behaviour, specifically focusing on vegetarianism and veganism, by employing social network analysis. By simulating interactions within an agent-based model, the study explores different mechanisms that hinder the diffusion of these behaviours. The research findings highlight the significance of the complexity of the contagion in shaping the speed and extent of the diffusion process. While minimally complex contagions are able to infect half of the network on average, vegetarianism and veganism do not spread, due to their complexity. Additionally, the initial number of vegetarians/ vegans was found to be the main driver of infection speed, besides inter-connectedness. The study also explores the possibility of a social tipping point, a critical threshold at which the diffusion process accelerates or reaches a critical mass. However, the research did not observe a tipping point in the adoption of vegetarianism and veganism. By examining the slow adoption of vegetarianism and veganism as a complex contagion, this research contributes to the comprehension of concrete network effect. The findings provide valuable insights for designing interventions and strategies to promote the widespread adoption of vegetarianism, veganism, and other environmentally friendly practices.

Secondary administrative and leadership teams continuously search for practices that bolster cultural proficiency to address increasingly diverse student cohorts. This qualitative case study identifies the culturally responsive school leadership (CRSL) behaviors and expressions of 24 high school administrative and leadership team members in a suburban school district in Utah. Data reveals how each team demonstrates the behaviors and expressions of CRSL framework and how these behaviors and expressions position each school on the culturally proficient continuum. Superimposing the CRSL behaviors and expressions on the cultural proficiency continuum provides a current reality for administrative and leadership teams to assess their tipping points and how to move toward cultural proficiency.

Many regions of the Amazon are experiencing drastic changes as deforestation and climate change drive the world’s largest continuous rainforest towards a ‘tipping point’. These disturbances are changing natural cycles that once past a critical threshold, will mark an unstoppable transition to an altered ecosystem. Losing areas of the Amazon rainforest will have implications for the global climate, global carbon budget, and global hydrological regimes. Scholars have projected these tipping points for areas of the eastern Amazon rainforest, but much less scholarship focuses on the headwaters of the Western Amazon, an area of great cultural and biological importance. Ecuador is one such country. This study aims to model a tipping point for the Ecuadorian Amazon by investigating the potential outcomes of a warming climate and land cover change through 1. a comprehensive review of regional circulation models and global circulation models in the Ecuadorian Amazon, 2. a comprehensive review of anthropogenic disturbances in the Ecuadorian Amazon and their impact on communities, soil, flora and fauna, and 3. A model projecting the deforestation tipping point of the Ecuadorian Amazon. The results of my study will identify patterns of forest loss and provide quantitative assessments of potential ‘tipping points’ in a future Ecuadorian Amazon. The methods and model created herein can be used by future researchers to evaluate regional drivers of deforestation and predict land cover change under future scenarios.

We analyze the policy implications of aversion to Knightian uncertainty (ambiguity) about the possibility of tipping points. We demonstrate two channels through which uncertainty aversion affects optimal policy in the general setting. The first channel relates to the policy's effect on the probability of tipping, and the second channel to its differential impact in the pre- and post-tipping regimes. We then extend a recursive dynamic model of climate policy and tipping points to include uncertainty aversion. Numerically, aversion to Knightian uncertainty in the face of an ambiguous tipping point increases the optimal tax on carbon dioxide emissions, but only by a small amount.

‘Project performance’ is the metric of delivering project objectives. This research is motivated by levels of project failure and the purpose of the research is to investigate improved project performance. The scientific spectrum is considered; arguing project management as a sub-field of management science based in ‘design science’. Despite research since the 1950s, there is no established community paradigm for project management, illustrated by multiple ‘schools of thought’ failing to stimulate performance improvement. This is investigated with respect to the changing nature of projects and their management; application in numerous industrial sectors, across increasing scope of the product lifecycle (including service projects), and the changing role of project managers as value adding ‘implementers’ rather than status ‘reporters’. Methodology examines the community paradigm and identifies the lack of community paradigm and argues that gap spotting is not appropriate. Conducting research that fills knowledge gaps does not identify underlying issues and reinforces fundamental failings. Underlying assumptions are identified and challenged. Key characteristics are examined in the context of requirements of the community paradigm. The purpose of theory is to describe, explain and predict. Some techniques describe and explain. Few, if any, predict. This locates ‘performance prediction’ as the research issue and suggests it is a missing function for performance improvement. The research focus considers single tasks within a project network. A research model of early stage deviation from plan is developed from the literature on project pathogens and incubation processes. ‘Deviation lifecycle’ as a project function is identified as having no previous evidence in literature. This is developed into a practice model extending the role of failure modes and effects analysis (FMEA) and integrating weak signals and tipping point theory to test performance. Case studies examine representative application of the model and build on the previous cases to illustrate potential for practice. The case studies were reviewed by industrial experts. The changing role of project managers to value added implementers implies a need to improve performance. Research found potential to understand and predict early stage deviation and develops the deviation lifecycle and research model. Across the case studies the research model illustrated potential application. Practical implications indicate potential contribution of project management techniques based on prediction rather than traditional reporting. Developing the community paradigm based on design science is discussed as further work. The originality of the research challenges the lack of theoretical foundation for project management by discussion of the community paradigm and proposes design science as a candidate. The work identifies ‘prediction’ as a relevant but missing function from the project management ‘toolbox’, and introduces the concept of the deviation lifecycle and note no previous literature. The research develops an industrial research model that extends the application of FMEA to examine ‘performance’ and integrates weak signals and tipping point analysis to manage the resolution.

Analyses of data with missing values often require assumptions about missingness mechanisms that cannot be assessed empirically, highlighting the need for sensitivity analyses. However, universal recommendations for reporting missing data and conducting sensitivity analyses in empirical studies are scarce. Both steps are often neglected by practitioners due to the lack of clear guidelines for summarizing missing data and systematic explorations of alternative assumptions, as well as the typical attendant complexity of missing not at random (MNAR) models. We propose graphical displays that help visualize and systematize the results of sensitivity analyses, building upon the idea of "tipping-point" analysis for experiments with dichotomous treatment. The resulting "enhanced tipping-point displays" (ETP) are convenient summaries of conclusions drawn from using different modeling assumptions about the missingness mechanisms, applicable to a broad range of outcome distributions. We also describe a systematic way of exploring MNAR models using ETP displays, based on a pattern-mixture factorization of the outcome distribution, and present a set of sensitivity parameters that arises naturally from such a factorization. The primary goal of the displays is to make formal sensitivity analyses more comprehensible to practitioners, thereby helping them assess the robustness of experiments' conclusions. We also present an example of a recent use of ETP displays in a medical device clinical trial, which helped lead to FDA approval. The last part of the dissertation demonstrates another method of sensitivity analysis in the same clinical trial. The trial is complicated by missingness in outcomes "due to death", and we address this issue by employing Rubin Causal Model and principal stratification. We propose an improved method to estimate the joint posterior distribution of estimands of interest using a Hamiltonian Monte Carlo algorithm and demonstrate its superiority for this problem to the standard Metropolis-Hastings algorithm. The proposed methods of sensitivity analyses provide new collections of useful tools for the analysis of data sets plagued with missing values. / Statistics

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