<p>Coherent, large-scale shapes and patterns are evident in many landscapes, and evolve according to climate and hydrological forces. For large-scale, sandy coastlines, these shapes depend on wave climate forcing. The wave climate is influenced by storm patterns, which are expected to change with the warming climate, and the associated changes in coastline shape are likely to increase rates of shoreline change in many places. Humans have historically responded to coastline change by manipulating various coastal processes, consequently affecting long-term, large-scale coastline shape change. Especially in the context of changing climate forcing and increasing human presence on the coast, the interaction of the human and climate-driven components of large-scale coastline evolution are becoming increasingly intertwined. </p><p>This dissertation explores how climate shapes coastlines, and how the effects of humans altering the landscape interact with the effects of a changing climate. Because the coastline is a spatially extended, nonlinear system, I use a simple numerical modeling approach to gain a basic theoretical understanding of its dynamics, incorporating simplified representations of the human components of coastline change in a previously developed model for the physical system. </p><p>Chapter 1 addresses how local shoreline stabilization affects the large scale morphology of a cuspate-cape type of coastline, and associated large-scale patterns of shoreline change, in the context of changing wave climate, comparing two fundamentally different approaches to shoreline stabilization: beach nourishment (in which sediment is added to a coastline at a long-term rate that counteracts the background erosion), and hard structures (including seawalls and groynes). The results show that although both approaches have surprisingly long-range effects with spatially heterogeneous distributions, the pattern of shoreline changes attributable to a single local stabilization effort contrast greatly, with nourishment producing less erosion when the stabilization-related shoreline change is summed alongshore. </p><p>Chapter 2 presents new basic understanding of the dynamics that produce a contrasting coastline type: convex headland-spit systems. Results show that the coastline shapes and spatially-uniform erosion rates emerge from two way influences between the headland and spit components, and how these interactions are mediated by wave climate, and the alongshore scale of the system. Chapter 2 also shows that one type of wave-climate change (altering the proportion of `high-angle' waves) leads to changes in coastline shape, while another type (altering wave-climate asymmetry) tends to reorient a coastline while preserving its shape. </p><p>Chapter 3 builds on chapter 2, by adding the effects of human shoreline stabilization along such a convex coastline. Results show that in the context of increasing costs for stabilization, abandonment of shoreline stabilization at one location triggers a cascade of abandonments and associated coastline-shape changes, and that both the qualitative spatial patterns and alongshore speed of the propagating cascades depends on the relationship between patterns of economic heterogeneity and the asymmetry of the wave-climate change--although alterations to the proportion of high-angle waves in the climate only affects the time scales for coupled morphologic/economic cascades.</p> / Dissertation

Thesis (MA (Philosophy))--University of Stellenbosch, 2005. / It seems to be a priori impossible to formulate any general theory or model that encompasses all of the properties of complexity. So, one must make do with partial solutions. A possible approach we propose is to take inspiration from quantum theory, since there seems to be a strong analogy between complex systems and quantum systems. Although we do not propose any literal application of quantum mechanical formalism to complexity, we suggest that the language of quantum mechanics is already so well developed - and for a much wider spectrum of problems than most theories - that it can serve as a model for complexity theory. There are many problems common to both complex systems and quantum systems and we suggest that it might be useful to test the applicability of aspects of the “language” of quantum mechanics to a general complex system. What we suggest here is an interdisciplinary talk led between the natural sciences and philosophy, which we believe is the only way in which to deal with complexity “as such”.

Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account  slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day.

We can leverage data and complex systems science to better understand society and human nature on a population scale through language --- utilizing tools that include sentiment analysis, machine learning, and data visualization. Data-driven science and the sociotechnical systems that we use every day are enabling a transformation from hypothesis-driven, reductionist methodology to complex systems sciences. Namely, the emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, with profound implications for our understanding of human behavior. Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture's evolution through its texts using a "big data" lens. Given the growing assortment of sentiment measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large enough portion of a given text's lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories, forming patterns that are meaningful to us. By classifying the emotional arcs for a filtered subset of 4,803 stories from Project Gutenberg's fiction collection, we find a set of six core trajectories which form the building blocks of complex narratives. We strengthen our findings by separately applying optimization, linear decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. Within stories lie the core values of social behavior, rich with both strategies and proper protocol, which we can begin to study more broadly and systematically as a true reflection of culture. Of profound scientific interest will be the degree to which we can eventually understand the full landscape of human stories, and data driven approaches will play a crucial role. Finally, we utilize web-scale data from Twitter to study the limits of what social data can tell us about public health, mental illness, discourse around the protest movement of #BlackLivesMatter, discourse around climate change, and hidden networks. We conclude with a review of published works in complex systems that separately analyze charitable donations, the happiness of words in 10 languages, 100 years of daily temperature data across the United States, and Australian Rules Football games.

The emergence of collective patterns from repeated local interactions between individuals is a common feature to most living systems, spanning a variety of scales from cells to animals and humans. Subjects of this thesis are two aspects of emergent complexity in living systems: collective movement and transport network formation. For collective movement, this thesis studies the role of movement-mediated information transfer in fish decision-making. The second project on collective movement takes inspiration from granular media and soft mode analysis and develops a new approach to describe the emergence of collective phenomena from physical interactions in extremely dense crowds. As regards transport networks, this thesis proposes a model of network growth to extract simple, biologically plausible rules that reproduce topological properties of empirical ant trail networks.  In the second project on transport networks, this thesis starts from the simple rule of “connecting each new node to the closest one”, that describes ants building behavior, to study how balancing local building costs and global maintenance costs influences the growth and topological properties of transport networks. These projects are addressed through a modeling approach and with the aim of identifying minimal sets of basic mechanisms that are most likely responsible of large-scale complex patterns. Mathematical models are always based on empirical observations and are, when possible, compared to experimental data.

Since their discovery in the 1950's by Erdos and Renyi, network theory (the study of objects and their associations) has blossomed into a full-fledged branch of mathematics. Due to the network's flexibility, diverse scientific problems can be reformulated as networks and studied using a common set of tools. I define a network G = (V,E) composed of two parts: (i) the set of objects V, called nodes, and (ii) set of relationships (associations) E, called links, that connect objects in V. We can extend the classic network of nodes and links by describing the intensity of these associations with weights. More formally, weighted networks augment the classic network with a function f(e) from links to the real line, uncovering powerful ways to model real-world applications. This thesis studies new ways to construct robust micro powergrids, mine people's perceptions of causality on a social network, and proposes a new way to analyze crowdsourcing all in the context of the weighted network model. The current state of Earth's ecosystem and intensifying climate calls on scientists to find new ways to harvest clean affordable energy. A microgrid, or neighborhood-scale powergrid built using renewable energy sources attached to personal homes, suggest one way to ameliorate this energy crisis. We can study the stability (robustness) of such a small-scale system with weighted networks. A novel use of weighted networks and percolation theory guides the safe and efficient construction of power lines (links, E) connecting a small set of houses (nodes, V) to one another and weights each power line by the distance between houses. This new look at the robustness of microgrid structures calls into question the efficacy of the traditional utility. The next study uses the twitter social network to compare and contrast causal language from everyday conversation. Collecting a set of 1 million tweets, we find a set of words (unigrams), parts of speech, named entities, and sentiment signal the use of informal causal language. Breaking a problem difficult for a computer to solve into many parts and distributing these tasks to a group of humans to solve is called Crowdsourcing. My final project asks volunteers to 'reply' to questions asked of them and 'supply' novel questions for others to answer. I model this 'reply and supply' framework as a dynamic weighted network, proposing new theories about this network's behavior and how to steer it toward worthy goals. This thesis demonstrates novel uses of, enhances the current scientific literature on, and presents novel methodology for, weighted networks.

La co-construction et la réutilisation de modèles font l'objet de plusieurs travaux dans le domaine de la simulation. Cependant, dans le domaine plus spécifique de la Simulation Orientée Agent (SOA), nous pouvons constater un manque sur ces deux points malgré un besoin fort de la part des thématiciens. 
La co-construction est essentielle pour optimiser la mise en commun du savoir de différents experts, mais nous faisons souvent face à des divergences de points de vue. Les méthodologies existantes pour la co-construction en SOA ne permettent qu'un faible niveau de collaboration entre thématiciens durant la phase initiale de modélisation, ainsi qu'entre les des thématiciens avec les modélisateurs ou les modélisateurs-informaticiens... Pour faciliter cette co-construction, nous proposons de suivre une méthodologie de conception favorisant cette collaboration. 
La réutilisation de modèle octroie un gain de temps significatif, une amélioration du modèle et l'apport de nouvelle connaissance. Les méthodologies en SOA dans ce domaine existent. Cependant, dans le spectre de réutilisation, elles sont souvent limitées au niveau du modèle complet ou de l'agent avec l'impossibilité de "descendre" plus bas. 
L'expérience de EDMMAS, un cas concret d'un modèle issu de trois réutilisations successives, nous a permis de constater une nouvelle complexité qui découle de la démultiplication des comportements des agents et crée un décalage conséquent entre le modèle opérationnel et le modèle conceptuel. Notre objectif est de promouvoir la réutilisation aussi bien des modèles, que des agents et de leurs comportements.Pour répondre à ces questionnements, nous proposons dans ce manuscrit une manière de codifier et d'intégrer la connaissance provenant de disciplines différentes dans le modèle, tout en utilisant des modules "composables" qui facilitent la réutilisation. Nous proposons (i) une nouvelle architecture Agent (aMVC), appliquée dans un cadre multidynamique (DOM), avec l'appui (ii) d'une approche méthodologique (MMC) basée sur la décomposition et réutilisation des comportements. 
Cet ensemble de propositions, (i) et (ii), permet de conduire un projet pluridisciplinaire de SOA avec un grand nombre d'acteurs, facilitant la co-construction des modèles grâce à l'instauration de nouvelles synergies entre les différents acteurs participant à la modélisation. Les concepteurs pourront travailler de manière autonome sur leur dynamique et la plateforme fera l'intégration de ces dernières en assurant la cohésion et la robustesse du système. Nos contributions offrent la capacité de créer les briques élémentaires du système de manière indépendante, de les associer et de les combiner pour former des agents, selon des dynamiques conformément à l'approche DOM. Elles permettent ainsi de comparer la logique selon différentes possibilités pour une même dynamique et d'ouvrir la perspective d'étudier un grand nombre d'alternatives de modélisation d'un même système complexe, et de les analyser ensuite à une échelle très fine. / Co-building and reuse of models are at the center of several studies in the field of simulation. However, in the more specific field ofMulti-Agent Based Simulation (MABS), there is a lack of methodology to resolve these two issues, despite a strong need by experts.Model co-building is essential to optimize knowledge sharing amongst different experts, but we often face divergent viewpoints. Existing methodologies for the MABS co-building allow only a low level of collaboration among experts during the initial phase of modeling, and between domain experts with modelers or computer scientists... In order to help this co-building, we propose and follow a methodology to facilitate this collaboration. Model reuse can provide significant time savings, improve models’ quality and offer new knowledge. Some MABS methodologies in this area exist. However, in the spectrum of reuse, they are often limited to a full model’s reuse or agent’s reuse with the impossibility of reusing smaller parts such as behaviors. The EDMMAS experiment was a concrete case of three successive model reuses. It allowed us to observe new complexity arising from the increase of agents’ behaviors. This creates a gap between operational model and conceptual model.Our goal is to promote the reuse of models, agents and their behaviors.To answer these questions, we propose in this thesis a new way to codify and integrate knowledge from different disciplines in the model, while using "composable"modules that facilitate reuse.We propose (i) a new agent architecture (aMVC), applied to a multidynamical approach (DOM), with the support (ii) of a methodology (MMC) based on the decompositionand reuse of behaviors.Proposals (i) and (ii) allow us to lead a multidisciplinary MABS project with a large number of actors, helping the co-building of models through the introduction of synergies among the different actors involved in the modeling. They can work independently on their dynamics and the platformwill integrate those, ensuring cohesion and robustness of the system. Our contributions include the ability to create the building blocks of the system independently, associate and combine them to formagents. This allows us to compare possibilities for the same dynamic and open the prospect of studyingmany alternate models of the same complex system, and then analyze at a very fine scale.

Spectral and wave function statistics of the quantum directed graph, QdG, are studied. The crucial feature of this model is that the direction of a bond (arc) corresponds to the direction of the waves propagating along it. We pay special attention to the full Neumann digraph, FNdG, which consists of pairs of antiparallel arcs between every node, and differs from the full Neumann graph, FNG, in that the two arcs have two incommensurate lengths. The spectral statistics of the FNG (with incommensurate bond lengths) is believed to be universal, i.e. to agree with that of the random matrix theory, RMT, in the limit of large graph size. However, the standard perturbative treatment of the field theoretical representation of the 2-point correlation function [1, 2] for a FNG, does not account for this behaviour. The nearest-neighbor spacing distribution of the closely related FNdG is studied numerically. An original, efficient algorithm for the generation of the spectrum of large graphs allows for the observation that the distribution approaches indeed universality at increasing graph size (although the convergence cannot be ascertained), in particular "level repulsion" is confirmed. The numerical technique employs a new secular equation which generalizes the analogous object known for undirected graphs [3, 4], and is based on an adaptation to digraphs of the idea of wave function continuity. In view of the contradiction between the field theory [2] and the strong indications of universality, a non-perturbative approach to analysing the universal limit is presented. The substitution of the FNG by the FNdG results in a field theory with fewer degrees of freedom. Despite this simplification, the attempt is inconclusive. Possible applications of this approach are suggested. Regarding the wave function statistics, a field theoretical representation for the spectral average of the wave intensity on an fixed arc is derived and studied in the universal limit. The procedure originates from the study of wave function statistics on disordered metallic grains [5] and is used in conjunction with the field theory approach pioneered in [2].

Leis de escala aplicadas a cidades ja foram identificadas para diversos contextos ao redor do mundo e sua validade defendida por diversos autores. A invariância em escala e as relações de crescimento superlinear, linear e sublinear foram observadas considerando-se a relac~ao entre diversos indicadores (sociais, econômicos, infraestrutura) e o tamanho das cidades. Nesta pesquisa foi avaliada a aplicação das leis de escala para as cidades brasileiras em relação a 14 indicadores dos Censos do Instituto Brasileiro de Geografia e Estatística (IBGE) que se alinham com indicadores da Norma ISO 37120:2017. As leis de escala foram avaliadas para esses indicadores em relac~ao ao tamanho populacional das cidades brasileiras por meio do levantamento e processamento dos dados disponíveis. Não foi possível confirmar a generalidade da aplicação dessas leis, sendo apontadas as suas limitações. / Scaling laws applied to cities have already been identied for diverse contexts around the world and their validity defended by several authors. The scale invariance and the superlinear, linear and sublinear growth behaviors were observed considering the relationship between several indicators (social, economic, infrastructure) and the size of the cities. This research evaluated the applicability of the scaling laws for Brazilian cities relative to 14 indicators from Instituto Brasileiro de Geografia e Estatística (IBGE) Censuses that are aligned with indicators proposed by ISO 37120: 2017 norm. The scaling laws were evaluated for these indicators in relation to the population size of Brazilian cities through the collection and processing of available data. It was not possible to conrm the generality of the application of these laws, and their limitations were pointed out.

Thesis advisor: Patrick J. McQuillan / This dissertation presents two case studies of educational leadership, followed by an extensive discussion of methodological, historical, and philosophical issues that pertain to education research, policy, and leadership development. The case studies utilize qualitative research methods and the theoretical framework of complex systems to ascertain how and to what extent principals fostered cultural and educational change at their schools, with attention to how principals leveraged distributed leadership, instructional leadership, and the generation of cultural norms. Findings from the study were consistent with literature on systems leadership, and reinforce the significance of history and path dependence in school systems, the need to limit disequilibrium and turbulence within sustainable ranges, the importance of trust within social networks to facilitate productive change processes, and the importance of shared cultural norms to align staff values and behavior. Following the explication of the two cases, a meta- analysis is presented to address the methodological and interpretive limits of the study. The role of human development and the influence of cultural ideology and social infrastructures are highlighted as crucial dimensions of reality that warrant integration in educational research. Integral Theory is utilized as a means to explore the cultural, social, and psychological factors involved in achieving more comprehensive interpretations of social reality. Key topics include: complex systems, Integral Theory, modernity, postmodernity, education reform, neoliberalism, and developmental psychology. / Thesis (PhD) — Boston College, 2018. / Submitted to: Boston College. Lynch School of Education. / Discipline: Teacher Education, Special Education, Curriculum and Instruction.