Дисертації: "Complex systems learning"

1

Sullivan, John P. "Emergent Learning: Three Learning Communities as Complex Adaptive Systems." Thesis, Boston College, 2009. http://hdl.handle.net/2345/663.

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Thesis advisor: Patrick J. McQuillan
In the 2007-2008 school year, the author conducted a collaborative case study (Stake, 2000) with the goal of discovering and describing "emergent learning" in three high school classrooms. Emergent learning, defined as the acquisition of new knowledge by an entire group when no individual member of the group possessed it before, is implied by the work of many theorists working on an educational analog of a natural phenomenon called a complex adaptive system. Complex adaptive systems are well networked collectives of agents that are non-linear, bounded and synergistic. The author theorized that classes that maximized the features of complex adaptive systems could produce emergent learning (a form of synergy), and that there was a continuum of this complexity, producing a related continuum of emergence. After observing a co-curricular jazz group, an English class, and a geometry class for most of one academic year, collecting artifacts and interviewing three students and a teacher from each class, the author determined that there was indeed a continuum of complexity. He found that the actively complex nature of the Jazz Rock Ensemble produced an environment where emergence was the norm, with the ensemble producing works of music, new to the world, with each performance. The English section harnessed the chaotic tendencies of students to optimize cognitive dissonance and frequently produce emergent learning, while the mathematics section approached the learning process in a way that was too rigidly linear to allow detectable emergence to occur
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Lynch School of Education
Discipline: Teacher Education, Special Education, Curriculum and Instruction

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2

Attebo, Edvin. "Safe learning and control in complex systems." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-178164.

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When autonomously controlling physical objects, a deviation from a trajectorycan lead to unwanted impacts, which can be very expensive or even dangerous. Thedeviation may be due to uncertainties, either from disturbance or model mismatch.One way to deal with these types of uncertainties is to design a robust control sys-tem, which creates margins for errors in the system. These margins make the systemsafe but also lowers the performance, hence it is desirable to have the margins assmall as possible and still make the system safe. One way to reduce the margins isto add a learning strategy to the control system, which improves the model repre-sentation using previous data. In this thesis, we investigate a robust control systemcalled tube-based model predictive control and then combine it with an adaptivegain scheduling method as the learning strategy. The adaptive feature in the gainscheduling method reduces the model mismatch between the model representationand the true dynamics by tuning the control parameters in the gain schedule usinga data-driven framework. To test this design, a dot is controlled to follow a pathin a constrained environment, around an obstacle. The dot should complete thetrack repeatedly without violating any constraints or crash into the obstacle whilereducing the model mismatch. Our results show that the error from the modelmismatch decreases with time without the dot touching the obstacle or moving out-side the constraints. As the error decreases, the margins in the controller becomesmaller, which makes it possible to control the system in a more efficient way andstill guarantee that the system remain safe.

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3

Bondorowicz, Stefan. "Adaptive control of complex dynamic systems." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302787.

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4

Eagle, Nathan Norfleet. "Machine perception and learning of complex social systems." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32498.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.
Includes bibliographical references (p. 125-136).
The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.
by Nathan Norfleet Eagle.
Ph.D.

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5

Al-Jubouri, Bassma. "Multi-criteria optimisation for complex learning prediction systems." Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30857/.

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This work presents a framework for the inclusion of multiple criteria in the design process of supervised learning algorithms; as well as studies the sophisticated interactions among them. The criteria included and tested experimentally in this thesis are: accuracy, model complexity, algorithmic complexity, diversity and robustness. The present thesis addresses important challenges related to considering multiple criteria such as: 1) defining suitable measures for the included criteria, 2) determining effective approaches to optimise the system performance using multiple objectives, 3) finding effective alternative approaches to include such criteria indirectly in the design stages when defining accurate measures is infeasible, and finally 4) analysing the possible interactions among the criteria as well as identifying the main factors/decision points that modulate them. This work introduces a novel Multi-Components, Multi-Layer Predictive System (MCMLPS). This system incorporates mechanisms designed to control the diversity, model complexity and robustness. In the first stage of this thesis, the accuracy, model and algorithmic complexities of the base components for the proposed system have been optimised empirically using two multi-objective optimisation approaches. The first approach consists of a scalarized multi-objective optimisation, where the models are generated from optimising a single cost function that combines the three criteria. The second approach uses a Pareto-based multi objective optimisation which establishes a trade-off among the three criteria to generate a set of selectively balanced models. These first results showed that models generated from Pareto-based multi objective optimisation approach are both more accurate and more diverse than the models generated from scalarized multi-objective optimisation approach. However, the Pareto-based approach is hindered by the high algorithmic complexity required to find the best model and the infeasibility of defining universal measures for some of the above-mentioned criteria. Thus, in later stages of this work these criteria are either presented as constraints or included indirectly in generating the base components for the MCMLPS. In a subsequent stage of this study, the diversity among the base components of the proposed MCMLPS system is encouraged by training them on local regions in the data, were the locality is determined using the similarity of the data features. Each local region contains either disjoint subsets of the data and/or subsets of the features. A range of similarity metrics such as pairwise squared correlation and conditional mutual information of the features are used. Interestingly, the squared correlation method can be applied in supervised as well as unsupervised learning as it does not consider the output class when splitting the data. Meanwhile, the conditional mutual information method can be applied only in supervised learning as it uses the output class in splitting the data. The full MCMLPS architecture is then analysed and its performance is compared to three well-known ensemble methods. Next, the effect of weighing the components of the MCMLPS and combining them is examined using six fusion methods. The results showed that, including the similarity metric used to divide the data into local regions in weighing the system components, of- ten results in the best accuracy compared to the other fusion methods. In the final phase of this study, the robustness of the proposed system in noisy environments is tested and compared to other ensemble methods. The system showed a comparable accuracy to the best performing ensemble and it often has a more robust performance than other ensembles in highly noisy environments. To conclude, the present thesis proposes a multi-component, multi-layer system which simultaneously incorporates multiple criteria in its design cycle. The results of this thesis suggest that the locality in learning and high diversity among the components of the proposed system can be particularly beneficial in designing ensemble learning methods for highly noisy data sets.

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6

Tong, Xiao Thomas. "Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10917.

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A complex system is one with many parts, whose behaviors are strongly dependent on each other. There are two interesting questions about complex systems. One is to understand how to recover the true structure of a complex system from noisy data. The other is to understand how the system interacts with its environment. In this thesis, we address these two questions by studying two distinct complex systems: dynamic systems and market microstructure. To address the first question, we focus on some nonlinear dynamic systems. We develop a novel Bayesian statistical method, Gaussian Emulator, to estimate the parameters of dynamic systems from noisy data, when the data are either fully or partially observed. Our method shows that estimation accuracy is substantially improved and computation is faster, compared to the numerical solvers. To address the second question, we focus on the market microstructure of hidden liquidity. We propose some statistical models to explain the hidden liquidity under different market conditions. Our statistical results suggest that hidden liquidity can be reliably predicted given the visible state of the market.
Statistics

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7

Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.

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This thesis advances the theory of network specialization by characterizing the effect of network specialization on the eigenvectors of a network. We prove and provide explicit formulas for the eigenvectors of specialized graphs based on the eigenvectors of their parent graphs. The second portion of this thesis applies network specialization to learning problems. Our work focuses on training reservoir computers to mimic the Lorentz equations. We experiment with random graph, preferential attachment and small world topologies and demonstrate that the random removal of directed edges increases predictive capability of a reservoir topology. We then create a new network model by growing networks via targeted application of the specialization model. This is accomplished iteratively by selecting top preforming nodes within the reservoir computer and specializing them. Our generated topology out-preforms all other topologies on average.

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8

CERBONI, BAIARDI LORENZO. "Adaptive models of learning in complex physical and social systems." Doctoral thesis, Urbino, 2016. http://hdl.handle.net/11576/2630552.

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9

Topcu, Taylan Gunes. "Management of Complex Sociotechnical Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97844.

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Sociotechnical systems (STSs) rely on the collaboration between humans and autonomous decision-making units to fulfill their objectives. Highly intertwined social and technical contextual factors influence the collaboration between these human and engineered elements, and consequently the performance characteristics of the STS. In the next two decades, the role allocated to STSs in our society will drastically increase. Thus, the effective design of STSs requires an improved understanding of the human-autonomy interdependency. This dissertation brings together management science along with systems thinking and uses a mixed-methods approach to investigate the interdependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work allocated to safety-critical decision makers and quantifies Rasmussen's workload boundary that represents the limit of attainable workload. The major contribution of this study is to quantify the qualitative theoretical construct of the workload boundary through a Pareto-Koopmans frontier. This frontier allows one to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their subjective preferences and contextual operational conditions. This study presents a novel integration of machine learning algorithms in an efficiency measurement framework to understand the influence of contextual factors. The results demonstrate that autonomous units successfully handle relatively simple operational conditions, while complex operational conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of efficiency measurement approaches that deal with the influence of contextual factors and their sensitivity to sample size. The results are organized in a structured taxonomy of their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.
Doctor of Philosophy
A system is an integrated set of elements that achieve a purpose or goal. An autonomous system (ADS) is an engineered element that often substitutes for a human decision-maker, such as in the case of an autonomous vehicle. Sociotechnical systems (STSs) are systems that involve the collaboration of a human decision-maker with an ADS to fulfill their objectives. Historically, STSs have been used primarily for handling safety critical tasks, such as management of nuclear power plants. By design, STSs rely heavily on a collaboration between humans and ADS decision-makers. Therefore, the overall characteristics of a STS, such as system safety, performance, or reliability; is fully dependent on human decisions. The problem with that is that people are independent entities, who can be influenced by operational conditions. Unlike their engineered counterparts, people can be cognitively challenged, tired, or distracted, and consequently make mistakes. The current dependency on human decisions, incentivize business owners and engineers alike to increase the level of automation in engineered systems. This allows them to reduce operational costs, increase performance, and minimize human errors. However, the recent commercial aircraft accidents (e.g., Boeing 737-MAX) have indicated that increasing the level of automation is not always the best strategy. Given that increasing technological capabilities will spread the adoption of STSs, vast majority of existing jobs will either be fully replaced by an ADS or will change from a manual set-up into a STS. Therefore, we need a better understanding of the relationships between social (human) and engineered elements. This dissertation, brings together management science with systems thinking to investigate the dependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work handled by safety-critical decision makers in STSs. Primary contribution of this study is to use an analytic method to quantify the amount of work a person could safely handle within a STSs. This method also allows to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their preferences and operational conditions. This study presents a novel integration of machine learning algorithms to understand operational influences that propel a human-decision maker to handle the work manually or delegate it to ADSs. The results demonstrate that autonomous units successfully handle simple operational conditions. More complex conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of data-driven analytical management approaches that deal with the operational factors and investigates their sensitivity to sample size. The results are organized based on their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.

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10

Urwin, Gerry. "Learning from complex information systems implementation : case studies in ERP projects." Thesis, Henley Business School, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268860.

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11

McKeown, Gary. "Implicit learning : representations and mechanisms in the control of complex systems." Thesis, Queen's University Belfast, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337107.

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12

Gelbrecht, Maximilian. "Physics-based Machine Learning Approaches to Complex Systems and Climate Analysis." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23010.

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Komplexe Systeme wie das Klima der Erde bestehen aus vielen Komponenten, die durch eine komplizierte Kopplungsstruktur miteinander verbunden sind. Für die Analyse solcher Systeme erscheint es daher naheliegend, Methoden aus der Netzwerktheorie, der Theorie dynamischer Systeme und dem maschinellen Lernen zusammenzubringen. Durch die Kombination verschiedener Konzepte aus diesen Bereichen werden in dieser Arbeit drei neuartige Ansätze zur Untersuchung komplexer Systeme betrachtet. Im ersten Teil wird eine Methode zur Konstruktion komplexer Netzwerke vorgestellt, die in der Lage ist, Windpfade des südamerikanischen Monsunsystems zu identifizieren. Diese Analyse weist u.a. auf den Einfluss der Rossby-Wellenzüge auf das Monsunsystem hin. Dies wird weiter untersucht, indem gezeigt wird, dass der Niederschlag mit den Rossby-Wellen phasenkohärent ist. So zeigt der erste Teil dieser Arbeit, wie komplexe Netzwerke verwendet werden können, um räumlich-zeitliche Variabilitätsmuster zu identifizieren, die dann mit Methoden der nichtlinearen Dynamik weiter analysiert werden können. Die meisten komplexen Systeme weisen eine große Anzahl von möglichen asymptotischen Zuständen auf. Um solche Zustände zu beschreiben, wird im zweiten Teil die Monte Carlo Basin Bifurcation Analyse (MCBB), eine neuartige numerische Methode, vorgestellt. Angesiedelt zwischen der klassischen Analyse mit Ordnungsparametern und einer gründlicheren, detaillierteren Bifurkationsanalyse, kombiniert MCBB Zufallsstichproben mit Clustering, um die verschiedenen Zustände und ihre Einzugsgebiete zu identifizieren. Bei von Vorhersagen von komplexen Systemen ist es nicht immer einfach, wie Vorwissen in datengetriebenen Methoden integriert werden kann. Eine Möglichkeit hierzu ist die Verwendung von Neuronalen Partiellen Differentialgleichungen. Hier wird im letzten Teil der Arbeit gezeigt, wie hochdimensionale räumlich-zeitlich chaotische Systeme mit einem solchen Ansatz modelliert und vorhergesagt werden können.
Complex systems such as the Earth's climate are comprised of many constituents that are interlinked through an intricate coupling structure. For the analysis of such systems it therefore seems natural to bring together methods from network theory, dynamical systems theory and machine learning. By combining different concepts from these fields three novel approaches for the study of complex systems are considered throughout this thesis. In the first part, a novel complex network construction method is introduced that is able to identify the most important wind paths of the South American Monsoon system. Aside from the importance of cross-equatorial flows, this analysis points to the impact Rossby Wave trains have both on the precipitation and low-level circulation. This connection is then further explored by showing that the precipitation is phase coherent to the Rossby Wave. As such, the first part of this thesis demonstrates how complex networks can be used to identify spatiotemporal variability patterns within large amounts of data, that are then further analysed with methods from nonlinear dynamics. Most complex systems exhibit a large number of possible asymptotic states. To investigate and track such states, Monte Carlo Basin Bifurcation analysis (MCBB), a novel numerical method is introduced in the second part. Situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis, MCBB combines random sampling with clustering methods to identify and characterise the different asymptotic states and their basins of attraction. Forecasts of complex system are the next logical step. When doing so, it is not always straightforward how prior knowledge in data-driven methods. One possibility to do is by using Neural Partial Differential Equations. Here, it is demonstrated how high-dimensional spatiotemporally chaotic systems can be modelled and predicted with such an approach in the last part of the thesis.

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13

Haghighi, Mona. "Rule-based Risk Monitoring Systems for Complex Datasets." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6248.

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In this dissertation we present rule-based machine learning methods for solving problems with high-dimensional or complex datasets. We are applying decision tree methods on blood-based biomarkers and neuropsychological tests to predict Alzheimer’s disease in its early stages. We are also using tree-based methods to identify disparity in dementia related biomarkers among three female ethnic groups. In another part of this research, we tried to use rule-based methods to identify homogeneous subgroups of subjects who share the same risk patterns out of a heterogeneous population. Finally, we applied a network-based method to reduce the dimensionality of a clinical dataset, while capturing the interaction among variables. The results show that the proposed methods are efficient and easy to use in comparison to the current machine learning methods.

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14

Azamfar, Moslem. "Deep Learning-based Domain Adaptation Methodology for Fault Diagnosis of Complex Manufacturing Systems." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623168030554759.

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15

Clissold, Paul. "An investigation into organisational learning within the development of complex civil aerospace systems." Thesis, University of the West of England, Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432323.

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16

Gelbrecht, Maximilian [Verfasser]. "Physics-based Machine Learning Approaches to Complex Systems and Climate Analysis / Maximilian Gelbrecht." Berlin : Humboldt-Universität zu Berlin, 2021. http://d-nb.info/1237685397/34.

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17

Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.

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VAIRO, TOMASO. "DARMS - Dynamic Asset-integrity and Risk Management System - How Machine Learning and Systems Engineering cooperate to enhance the resilience of complex systems." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1080188.

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“Static, incomplete, superficial, wrong”. The traditional approach to risk analysis, as applied in the process industries, has been largely criticized in response to recent major accidents. Since it was first proposed, modifications and improvements have been made, and a formal accepted approach is included in several regulations and standards (as the recent development of guidelines for the ageing management in SEVESO installations). Quantitative Risk Assessment (QRA) is based on consolidated procedures. Nevertheless, the need of safety improvement asks for more advanced tools for hazard identification and risk evaluation. Besides considering technical aspects (e.g., malfunctions and process upsets), operational errors, organizational aspects, such as lack of attention and motivation to the safety culture, may lead to risk increment in terms of likelihood of undesired failures. Not all those aspects may be investigated with conventional QRA techniques, which have also the disadvantage of being intrinsically static and failing to capture risk variations during the lifecycle of a plant or production site. Despite their proved effectiveness, many hazards identification and risk assessment techniques lack the dynamic dimension, which is the ability to learn from new risk notions, experience, and early warnings. Now’s the time to go beyond the limits of conventional static methods for hazard identification and risk assessment; the risk assessment is, indeed, a very useful approach in support of this change but at the same time it is not exhaustive to capture also the possible “failure” in the interface/interaction among the several single components of a complex system beside their specific failures. This research work discusses a novel approach for dynamizing the risk assessment process, integrating measured process data, asset integrity and operative conditions. In the first part of the thesis, the inferential process and the application of Machine Learning to inference is discussed, and various applications of standard, and tailored, machine learning algorithms to industrial and environmental risks are detailed as case studies. The second part is focused on the resilience engineering. The resilience paradigm is discussed, as well as the concept of emerging properties of complex systems. it will be shown how real-time data analytics, through appropriate AI models, combined with the expert knowledge of process engineering, constitute the fundamental technological key to pursue the resilience of plants and processes. The third section integrates the aforementioned concepts within the wide framework of Systems Engineering. Accordingly, a dynamic and systemic model is presented, to address the significant shortcomings of the current risk analysis models. The Dynamic Asset-integrity and Risk Management System (DARMS) is designed starting from the Bow-tie technique, integrated with improved Machine Learning algorithms, to overcome the epistemic uncertainty in the prior probabilities and likelihoods of escalation factors and barriers. Subsequently, a Hidden Markov Model (HMM), based on Bayesian Inference, is developed to analyze real-time risk, and produce reliable predictions on the state of the whole system during the operations. The application of the proposed model is demonstrated on an Oil and Gas terminal under Seveso legislation. The results of the case study provide a better understanding of the advanced Data Driven modeling of accident scenarios. The proposed model will serve as a useful tool for the operational safety management of complex systems.

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Arruda, Guilherme Ferraz de. "Mineração de dados em redes complexas: estrutura e dinâmica." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25062013-085958/.

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A teoria das redes complexas é uma área altamente interdisciplinar que oferece recursos para o estudo dos mais variados tipos de sistemas complexos, desde o cérebro até a sociedade. Muitos problemas da natureza podem ser modelados como redes, tais como: as interações protéicas, organizações sociais, o mercado financeiro, a Internet e a World Wide Web. A organização de todos esses sistemas complexos pode ser representada por grafos, isto é, vértices conectados por arestas. Tais topologias têm uma influencia fundamental sobre muitos processos dinâmicos. Por exemplo, roteadores altamente conectados são fundamentais para manter o tráfego na Internet, enquanto pessoas que possuem um grande número de contatos sociais podem contaminar um grande número de outros indivíduos. Ao mesmo tempo, estudos têm mostrado que a estrutura do cérebro esta relacionada com doenças neurológicas, como a epilepsia, que está ligada a fenômenos de sincronização. Nesse trabalho, apresentamos como técnicas de mineração de dados podem ser usadas para estudar a relação entre topologias de redes complexas e processos dinâmicos. Tal estudo será realizado com a simulação de fenômenos de sincronização, falhas, ataques e propagação de epidemias. A estrutura das redes será caracterizada através de métodos de mineração de dados, que permitirão classificar redes de acordo com um conjunto de modelos e determinar padrões de conexões presentes na organização de diferentes tipos de sistemas complexos. As análises serão realizadas com aplicações em neurociências, biologia de sistemas, redes sociais e Internet
The theory of complex networks is a highly interdisciplinary reseach area offering resources for the study of various types of complex systems, from the brain to the society. Many problems of nature can be modeled as networks, such as protein interactions, social organizations, the financial market, the Internet and World Wide Web. The organization of all these complex systems can be represented by graphs, i.e. a set of vertices connected by edges. Such topologies have a fundamental influence on many dynamic processes. For example, highly connected routers are essential to keep traffic on the Internet, while people who have a large number of social contacts may infect many other individuals. Indeed, studies have shown that the structure of brain is related to neurological conditions such as epilepsy, which is relatad to synchronization phenomena. In this text, we present how data mining techniques data can be used to study the relation between complex network topologies and dynamic processes. This study will be conducted with the simulation of synchronization, failures, attacks and the epidemics spreading. The structure of the networks will be characterized by data mining methods, which allow classifying according to a set of theoretical models and to determine patterns of connections present in the organization of different types of complex systems. The analyzes will be performed with applications in neuroscience, systems biology, social networks and the Internet

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20

Russell, Carol Faculty of Engineering UNSW. "E-learning adoption in a campus university as a complex adaptive system: mapping lecturer strategies." Awarded by:University of Leicester, 2008. http://handle.unsw.edu.au/1959.4/39597.

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The adoption of e-learning technologies in campus universities has not realised its potential for meeting the learning needs and expectations of 21st century students. By modelling university learning and teaching as a complex adaptive system, this thesis develops a new way of understanding and managing the adoption of new learning technologies in campus universities. The literature on learning and teaching in higher education indicates that lecturers??? ability to innovate in their teaching is constrained by tacit and discipline-specific educational knowledge. Introducing new methods and technologies into mainstream university teaching requires explicit review of educational knowledge, and requires support from departmental and institutional organizational systems. Research on organizational change in other contexts, such as manufacturing industry, has used complex adaptive systems modelling to understand the systemic interdependence of individual strategies, organizations and technologies. These models suggest that the integration of new e-learning technologies into mainstream campus university teaching will involve corresponding change processes. Part of this change requires the linking up of diverse disciplinary perspectives on learning and teaching. The thesis develops a conceptual framework for researching university learning and teaching as a complex adaptive system that includes learning technologies, people, and their organization within a university. Complex adaptive systems theory suggests that the capacity of a campus university to adapt to new e-learning technologies will be reflected in patterns in the strategies of those lecturers who are early adopters of those technologies. A context-specific study in the University of New South Wales used cognitive mapping to represent and analyse the strategies of a group of 19 early adopters of e-learning technology. These early adopters were participants in a cross-discipline Fellowship programme intended to develop their ability to act as change agents within the university. Analysis of the maps gathered before and after the Fellowship, triangulated with data on the Fellows??? participation in organizational change, leads to a new way of modelling how university learning and teaching systems, including their technologies, adapt within a complex and changing higher education context.

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21

Robbin, Alice, and Lee Frost-Kumpf. "Extending theory for user-centered information systems: Diagnosing and learning from error in complex statistical data." John Wiley & Sons, Inc, 1997. http://hdl.handle.net/10150/105746.

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Utilization of complex statistical data has come at great cost to individual researchers, the information community, and to the national information infrastructure. Dissatisfaction with the traditional approach to information system design and information services provision, and, by implication, the theoretical bases on which these systems and services have been developed has led librarians and information scientists to propose that information is a user construct and therefore system designs should place greater emphasis on user-centered approaches. This article extends Dervinâ s and Morris's theoretical framework for designing effective information services by synthesizing and integrating theory and research derived from multiple approaches in the social and behavioral sciences. These theoretical frameworks are applied to develop general design strategies and principles for information systems and services that rely on complex statistical data. The focus of this article is on factors that contribute to error in the production of high quality scientific output and on failures of communication during the process of data production and data utilization. Such insights provide useful frameworks to diagnose, communicate, and learn from error. Strategies to design systems that support communicative competence and cognitive competence emphasize the utilization of information systems in a user centered learning environment. This includes viewing cognition as a generative process and recognizing the continuing interdependence and active involvement of experts, novices, and technological gatekeepers.

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STAFFINI, ALESSIO. "ESSAYS ON COMPLEX ECONOMIC SYSTEMS AND ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/131851.

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Questa tesi mira a modellare alcuni aspetti dell'ambiente economico come sistemi complessi e ad analizzare le dipendenze di tali sistemi utilizzando in particolare la Modellazione ad Agenti (ABM) e l'Intelligenza Artificiale. Nel primo capitolo ci proponiamo di modellare le dinamiche del mercato del lavoro con un ABM, prestando particolare attenzione agli effetti che l'istruzione produce nella competizione tra individui in cerca di lavoro e sulla formazione del loro salario. Creiamo due tipi di agenti che interagiscono tra loro e con l'ambiente circostante: i lavoratori e le imprese. La caratteristica principale del modello è che le competenze dei lavoratori sono assegnate casualmente e l'ottenimento di un titolo di studio avviene all'interno del modello in modo endogeno. Anche il livello tecnologico delle imprese è assegnato casualmente, e queste condizioni portano ad ottenere risultati molto simili ad ogni run. Mostriamo che, modificando le condizioni di partenza, in qualsiasi scenario analizzato i lavoratori poco qualificati sono quelli maggiormente penalizzati sia nel tasso di occupazione che nel livello salariale, e sottolineiamo quanto sia importante l'investimento in capitale umano. Controlli di robustezza hanno confermato l'affidabilità dei risultati ottenuti. Nel secondo capitolo presentiamo una Convolutional Neural Network combinata con una Bidirectional Long Short-Term Memory Network (CNN-BiLSTM) per la previsione di variabili macroeconomiche, analizzando 18 serie storiche dell'economia degli Stati Uniti d'America. Nel terzo capitolo, sviluppiamo una Deep Convolutional Generative Adversarial Network (DCGAN) per la previsione del prezzo delle azioni. Considerando sia le previsioni single-step che multi-step, i risultati ottenuti dalle nostre architetture proposte sono promettenti e forniscono risultati migliori rispetto ai modelli econometrici di base considerati, sia nel contesto economico che finanziario. Suggeriamo che l'Intelligenza Artificiale (ed in particolare, il Deep Learning) dovrebbe essere studiata e incorporata maggiormente dagli economisti, poiché riteniamo che possa fornire risultati eccellenti, soprattutto in un'era in cui la disponibilità di dati sta crescendo sempre di più.
This thesis aims to model some aspects of the economic environment as complex systems, and to analyze the dependencies of such systems using in particular Agent-Based Modeling (ABM) and Artificial Intelligence. In the first chapter, we aim to model the dynamics of the labor market with an ABM, paying particular attention to the effects that education produces in the competition between individuals looking for a job and on wage’s formation. We create two types of agents that interact with each other and with the surrounding environment: workers and firms. The main feature of the model is that the workers' skills are randomly assigned and obtaining an educational qualification takes place within the model endogenously. Even the technological level of the firms is randomly assigned, and these conditions lead to obtain very similar results at each run. We show that, by modifying the starting conditions, in any analyzed scenario the low-skilled workers are the ones penalized the most both in the employment rate and in the wage amount, and we stress how important the investment in human capital is. Robustness checks confirmed the reliability of the obtained results. In the second chapter, we propose a Convolutional Neural Network combined with a Bidirectional Long Short-Term Memory Network (CNN-BiLSTM) for the forecasting of macroeconomic variables, analyzing 18 time series about the economy of the United States of America. In the third chapter, we develop a Deep Convolutional Generative Adversarial Network (DCGAN) for stock price forecasting. Considering both single-step and multi-step forecasts, the results obtained by our proposed architectures are promising and improve upon the considered baseline econometric models, both in the economic and financial context. We suggest that Artificial Intelligence (and in particular, Deep Learning) should be investigated and incorporated more by the economists, as we believe it can deliver excellent results, especially in an era where big data availability is growing more and more.

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AlZahrani, Saleh Saeed. "Regionally distributed architecture for dynamic e-learning environment (RDADeLE)." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/3814.

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e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.

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Barbieri, Matteo <1993&gt. "Advanced Condition Monitoring of Complex Mechatronics Systems Based on Model-of-Signals and Machine Learning Techniques." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9607/1/matteo_barbieri_thesis.pdf.

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Prognostics and Health Management (PHM) of machinery has become one of the pillars of Industry 4.0. The introduction of emerging technologies into the industrial world enables new models, new forms, and new methodologies to transform traditional manufacturing into intelligent manufacturing. In this context, diagnostics and prognostics of faults and their precursors has gained remarkable attention, mainly when performed autonomously by systems. The field is flourishing in academia, and researchers have published numerous PHM methodologies for machinery components. The typical course of actions adopted to execute servicing strategies on machinery components requires significant sensor measurements, suitable data processing algorithms, and appropriate servicing choices. Even though the industrial world is integrating more and more Information Technology solutions to keep up with Industry 4.0 new trends most of the proposed solutions do not consider standard industrial hardware and software. Modern controllers are built based on PCs and workstations hardware architectures, introducing more computational power and resources in production lines that we can take advantage of. This thesis focuses on bridging the gap in PHM between the industry and the research field, starting from Condition Monitoring and its application using modern industrial hardware. The cornerstones of this "bridge" are Model-of-Signals (MoS) and Machine Learning techniques. MoS relies on sensor measurements to estimate machine working condition models. Those models are the result of black-box system identification theory, which provides essential rules and guidelines to calculate them properly. MoS allows the integration of PHM modules into machine controllers, exploiting their edge-computing capabilities, because of the availability of recursive estimation algorithms. Besides, Machine Learning offers the tools to perform a further refinement of the extracted information, refining data for diagnostics, prognostics, and maintenance decision-making, and we show how its integration is possible within the modern automation pyramid.

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Kalantari, John I. "A general purpose artificial intelligence framework for the analysis of complex biological systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5953.

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This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.

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Appeltant, Lennert. "Reservoir computing based on delay-dynamical systems." Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84144.

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Today, except for mathematical operations, our brain functions much faster and more efficient than any supercomputer. It is precisely this form of information processing in neural networks that inspires researchers to create systems that mimic the brain’s information processing capabilities. In this thesis we propose a novel approach to implement these alternative computer architectures, based on delayed feedback. We show that one single nonlinear node with delayed feedback can replace a large network of nonlinear nodes. First we numerically investigate the architecture and performance of delayed feedback systems as information processing units. Then we elaborate on electronic and opto-electronic implementations of the concept. Next to evaluating their performance for standard benchmarks, we also study task independent properties of the system, extracting information on how to further improve the initial scheme. Finally, some simple modifications are suggested, yielding improvements in terms of speed or performance.

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Hasan, Sheikh Sadid Al. "Complex question answering : minimizing the gaps and beyond." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, 2013. http://hdl.handle.net/10133/3436.

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Current Question Answering (QA) systems have been significantly advanced in demonstrating finer abilities to answer simple factoid and list questions. Such questions are easier to process as they require small snippets of texts as the answers. However, there is a category of questions that represents a more complex information need, which cannot be satisfied easily by simply extracting a single entity or a single sentence. For example, the question: “How was Japan affected by the earthquake?” suggests that the inquirer is looking for information in the context of a wider perspective. We call these “complex questions” and focus on the task of answering them with the intention to minimize the existing gaps in the literature. The major limitation of the available search and QA systems is that they lack a way of measuring whether a user is satisfied with the information provided. This was our motivation to propose a reinforcement learning formulation to the complex question answering problem. Next, we presented an integer linear programming formulation where sentence compression models were applied for the query-focused multi-document summarization task in order to investigate if sentence compression improves the overall performance. Both compression and summarization were considered as global optimization problems. We also investigated the impact of syntactic and semantic information in a graph-based random walk method for answering complex questions. Decomposing a complex question into a series of simple questions and then reusing the techniques developed for answering simple questions is an effective means of answering complex questions. We proposed a supervised approach for automatically learning good decompositions of complex questions in this work. A complex question often asks about a topic of user’s interest. Therefore, the problem of complex question decomposition closely relates to the problem of topic to question generation. We addressed this challenge and proposed a topic to question generation approach to enhance the scope of our problem domain.
xi, 192 leaves : ill. ; 29 cm

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Dahab, Sarah. "An approach to measuring software systems using new combined metrics of complex test." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL015/document.

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La plupart des métriques de qualité logicielle mesurables sont actuellement basées sur des mesures bas niveau, telles que la complexité cyclomatique, le nombre de lignes de commentaires ou le nombre de blocs dupliqués. De même, la qualité de l'ingénierie logicielle est davantage liée à des facteurs techniques ou de gestion, et devrait fournir des indicateurs utiles pour les exigences de qualité. Actuellement, l'évaluation de ces exigences de qualité n'est pas automatisée, elle n'est pas validée empiriquement dans des contextes réels et l'évaluation est définie sans tenir compte des principes de la théorie de la mesure. Par conséquent, il est difficile de comprendre où et comment améliorer le logiciel suivant le résultat obtenu. Dans ce domaine, les principaux défis consistent à définir des métriques adéquates et utiles pour les exigences de qualité, les documents de conception de logiciels et autres artefacts logiciels, y compris les activités de test.Les principales problématiques scientifiques abordées dans cette thèse sont les suivantes: définir des mesures et des outils de support pour mesurer les activités d'ingénierie logicielle modernes en termes d'efficacité et de qualité. La seconde consiste à analyser les résultats de mesure pour identifier quoi et comment s'améliorer automatiquement. Le dernier consiste en l'automatisation du processus de mesure afin de réduire le temps de développement. Une telle solution hautement automatisée et facile à déployer constituera une solution révolutionnaire, car les outils actuels ne le prennent pas en charge, sauf pour une portée très limitée
Most of the measurable software quality metrics are currently based on low level metrics, such as cyclomatic complexity, number of comment lines or number of duplicated blocks. Likewise, quality of software engineering is more related to technical or management factoid, and should provide useful metrics for quality requirements. Currently the assessment of these quality requirements is not automated, not empirically validated in real contexts, and the assessment is defined without considering principles of measurement theory. Therefore it is difficult to understand where and how to improve the software following the obtained result. In this domain, the main challenges are to define adequate and useful metrics for quality requirements, software design documents and other software artifacts, including testing activities.The main scientific problematic that are tackled in this proposed thesis are the following : defining metrics and its supporting tools for measuring modern software engineering activities with respect to efficiency and quality. The second consists in analyzing measurement results for identifying what and how to improve automatically. The last one consists in the measurement process automation in order to reduce the development time. Such highly automated and easy to deploy solution will be a breakthrough solution, as current tools do not support it except for very limited scope

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THIAGO, EDUARDO CAMPOS DE SAO. "INTERNATIONAL STANDARDIZATION AND ORGANIZATIONAL LEARNING IN COMPLEX ADAPTIVE SYSTEMS: THE CASE OF ISO 26000 SOCIAL RESPONSIBILITY STANDARD." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21749@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
O objetivo da dissertação é analisar a dinâmica de aprendizagem organizacional (AO) relativa ao processo de elaboração da Norma Internacional de Responsabilidade Social (ISO 26000), segundo a perspectiva da complexidade social. No contexto da normalização internacional e à luz dos desafios estratégicos enfrentados pela ISO referentes à governança global, parte-se do pressuposto de que a abordagem da complexidade social de AO pode contribuir para uma melhor compreensão do papel da aprendizagem na formação de consenso em dois níveis: entre as diversas categorias de stakeholders e entre países. A metodologia de pesquisa compreende: (i) revisão bibliográfica e documental sobre normalização internacional; sistemas adaptativos complexos; e aprendizagem organizacional, com especial atenção para abordagens integradoras; (ii) descrição do processo de desenvolvimento da norma internacional ISO 26000; (iii) proposição de modelo conceitual que integra normalização internacional e aprendizagem organizacional, segundo a perspectiva da complexidade social; (iv) pesquisa survey junto a especialistas do Grupo de Trabalho ISO-TMB-WG SR e de seu comitê espelho brasileiro; e (v) estudo de caso de AO no processo de elaboração da Norma Internacional ISO 26000, com resultados da pesquisa survey. Esses resultados incluem: (i) a análise da dinâmica de AO relativa ao processo de elaboração da norma ISO 26000; (ii) relação dos principais fatores facilitadores de AO neste caso, considerando os dois níveis de análise; e (iii) recomendações endereçadas à ISO e à ABNT para futuros desenvolvimentos de normas internacionais em ambientes sociais complexos.
The main objective of this dissertation is to analyze the learning dynamics and the specific learning mechanisms experimented by the different groups during the development process of ISO 26000 standard, through the lens of the social complexity perspective of organizational learning (OL). In the context of ISO 26000’s learning process, it was assumed that the social complexity perspective of organizational learning (OL) could be especially useful as it can improve the understanding of the role of learning in a double level of consensus – amongst stakeholders and across countries – in the light of the strategic challenges faced by ISO within the global governance arena. The research methodology comprises: (i) bibliographical and documental review on international standardization; social complex adaptive systems; organizational learning, with special attention to integrative approaches; (ii) review of the development process of ISO 26000 standard; (iii) design of a conceptual model that integrates the international standardization and organizational learning, through the lens of the social complexity perspective; (iv) development and application of a survey questionnaire to representatives of ISO-TMB-WG SR, including its Brazilian Mirror Committee; and (v) description of ISO 26000 study case. The main results can be summarized as follows: (i) learning dynamics analysis of the development process of ISO 26000 standard; (ii) list of main facilitating and constraining factors for OL in this case; and (iii) recommendations addressed to ISO regarding future international standardization processes in social complex environments.

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Cambe, Jordan. "Understanding the complex dynamics of social systems with diverse formal tools." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN043/document.

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Au cours des deux dernières décennies les objets connectés ont révolutionné la traçabilité des phénomènes sociaux. Les trajectoires sociales laissent aujourd'hui des traces numériques, qui peuvent être analysées pour obtenir une compréhension plus profonde des comportements collectifs. L'essor de grands réseaux sociaux (comme Facebook, Twitter et plus généralement les réseaux de communication mobile) et d'infrastructures connectées (comme les réseaux de transports publiques et les plate-formes en ligne géolocalisées) ont permis la constitution de grands jeux de données temporelles. Ces nouveaux jeux de données nous donnent l'occasion de développer de nouvelles méthodes pour analyser les dynamiques temporelles de et dans ces systèmes.De nos jours, la pluralité des données nécessite d'adapter et combiner une pluralité de méthodes déjà existantes pour élargir la vision globale que l'on a de ces systèmes complexes. Le but de cette thèse est d'explorer les dynamiques des systèmes sociaux au moyen de trois groupes d'outils : les réseaux complexes, la physique statistique et l'apprentissage automatique. Dans cette thèse je commencerai par donner quelques définitions générales et un contexte historique des méthodes mentionnées ci-dessus. Après quoi, nous montrerons la dynamique complexe d'un modèle de Schelling suite à l'introduction d'une quantité infinitésimale de nouveaux agents et discuterons des limites des modèles statistiques. Le troisième chapitre montre la valeur ajoutée de l'utilisation de jeux de données temporelles. Nous étudions l'évolution du comportement des utilisateurs d'un réseau de vélos en libre-service. Puis, nous analysons les résultats d'un algorithme d'apprentissage automatique non supervisé ayant pour but de classer les utilisateurs en fonction de leurs profils. Le quatrième chapitre explore les différences entre une méthode globale et une méthode locale de détection de communautés temporelles sur des réseaux scientométriques. Le dernier chapitre combine l'analyse de réseaux complexes et l'apprentissage automatique supervisé pour décrire et prédire l'impact de l'introduction de nouveaux commerces sur les commerces existants. Nous explorons l'évolution temporelle de l'impact et montrons le bénéfice de l'utilisation de mesures de topologies de réseaux avec des algorithmes d'apprentissage automatique
For the past two decades, electronic devices have revolutionized the traceability of social phenomena. Social dynamics now leave numerical footprints, which can be analyzed to better understand collective behaviors. The development of large online social networks (like Facebook, Twitter and more generally mobile communications) and connected physical structures (like transportation networks and geolocalised social platforms) resulted in the emergence of large longitudinal datasets. These new datasets bring the opportunity to develop new methods to analyze temporal dynamics in and of these systems. Nowadays, the plurality of data available requires to adapt and combine a plurality of existing methods in order to enlarge the global vision that one has on such complex systems. The purpose of this thesis is to explore the dynamics of social systems using three sets of tools: network science, statistical physics modeling and machine learning. This thesis starts by giving general definitions and some historical context on the methods mentioned above. After that, we show the complex dynamics induced by introducing an infinitesimal quantity of new agents to a Schelling-like model and discuss the limitations of statistical model simulation. The third chapter shows the added value of using longitudinal data. We study the behavior evolution of bike sharing system users and analyze the results of an unsupervised machine learning model aiming to classify users based on their profiles. The fourth chapter explores the differences between global and local methods for temporal community detection using scientometric networks. The last chapter merges complex network analysis and supervised machine learning in order to describe and predict the impact of new businesses on already established ones. We explore the temporal evolution of this impact and show the benefit of combining networks topology measures with machine learning algorithms

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Davis, Jacqueline Topsy Mengersen. "The role of culture in children's sex-typed preferences for colours, toys, and affordances : a systems theory approach." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289913.

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Children's sex-typed preferences for colours and toys are well-established, and often function as markers of sex-typicality in research on the development of sex-typed behaviour. However, children's sex-typed colour and toy preferences have not been tested cross-culturally, or in remote unindustrialised cultural settings. The present thesis tested children's preferences for sex-typed toys in four cultural settings: Shipibo villages in the Lake Imiria region of the Peruvian Amazon; kastom villages in the mountains of Tanna Island in Vanuatu in the South Pacific; children attending school in Lenakel town on Tanna Island; and in a large industrialised city in Australia. It also tested children's colour preferences in three of these cultures. It was hypothesised that colour and toy preferences would show some similarities across cultures, and further, that similarities in toy preferences across cultures would be explained by the different types of play afforded by the toys. Results suggested that colour preferences, specifically, a sex difference in preference for pink, are specific to industrialised cultures. Results further suggested that some sex differences in toy preferences replicate in different cultures, and that the relationship between toy preferences and children's preferences for play affordances is a potentially important area for further research. The present thesis also provided two demonstrations of how new statistical methods, adapted from complex and dynamic systems theory, could be applied to the cross-cultural dataset. A machine learning method suggested that sex, more than culture, affects children's sex-typed toy preferences. A multistate dynamic method further suggested that sex, more than culture, affects the dynamics of children's toy choices.

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Lindkvist, Emilie. "Learning-by-modeling : Novel Computational Approaches for Exploring the Dynamics of Learning and Self-governance in Social-ecological Systems." Doctoral thesis, Stockholms universitet, Stockholm Resilience Centre, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122395.

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As a consequence of global environmental change, sustainable management and governance of natural resources face critical challenges, such as dealing with non-linear dynamics, increased resource variability, and uncertainty. This thesis seeks to address some of these challenges by using simulation models. The first line of research focuses on the use of learning-by-doing (LBD) for managing a renewable resource, exemplified by a fish stock, and explores LBD in a theoretical model using artificial intelligence (Paper I and II). The second line of research focuses on the emergence of different forms of self-governance and their interrelation with the dynamics of trust among fishers when harvesting a shared resource, using an agent-based model. This model is informed by qualitative data based on small-scale fisheries in Mexico (Paper III and IV). Paper I and II find that the most sustainable harvesting strategy requires that the actor values current and future yields equally, cautiously experiments around what is perceived as the best harvest action, and rapidly updates its ‘mental model’ to any perceived change in catch. More specifically, Paper II reveals that understanding these aspects in relation to the type of change can yield not only increased performance, but also, and more importantly, increased robustness to both fast and slow changes in resource dynamics. However, when resource dynamics include the possibility of a more fundamental shift in system characteristics (a regime shift), LBD is problematic due to the potential for crossing a threshold, resulting in possible persistent reductions in harvests (Paper I). In Paper III, results indicate that cooperative forms of self-governance are more likely to establish and persist in communities where fishers’ have prior cooperative experience, fishers’ trustworthiness is more or less equal, and that this likelihood increases when resource availability fluctuates seasonally. Finally, to achieve a transformation toward more cooperative forms of self-governance, interventions are required that can strengthen both financial capital and trust among the members of the cooperatives (Paper IV). The unique contribution of this thesis lies in the method for ‘quantitatively’ studying LBD, the stylized model of a small-scale fishery, and the analysis of the two models to advance our understanding of processes of learning and self-governance in uncertain and variable social-ecological environments. Together, the results shed light on how social and ecological factors and processes co-evolve to shape social-ecological outcomes, as well as contributing to the development of novel methods within the emerging field of sustainability science.
I vårt antropocena tidevarv är ett långsiktigt förvaltarskap av naturresurser inom social-ekologiska system av yttersta vikt. Detta kräver en djup förståelse av människan, ekologin, interaktionerna sinsemellan och deras utveckling över tid. Syftet med denna avhandling är att nå en djupare och mer nyanserad förståelse kring två av grundpelarna inom forskningen av hållbar förvaltning av naturresurser–kontinuerligt lärande genom learning-by-doing (LBD) för att förstå naturresursens dynamik, samt vad som kan kallas socialt kapital, i detta sammanhang i betydelsen tillit mellan individer, som naturligtvis ligger till grund för framgångsrik gemensam förvaltning. Denna föresats operationaliseras genom att använda två olika simuleringsmodeller. Den ena modellen undersöker hur en hållbar förvaltning av en förnyelsebar resurs, i denna avhandling exemplifierad av en fiskepopulation, kan uppnås genom LBD. Den andra modellen söker blottlägga det komplexa sociala samspel som krävs för att praktisera gemensam förvaltning genom att använda ett fiskesamhälle som fallstudie. Tidigare forskning på båda dessa två områden är relativt omfattade. Emellertid har den forskning som specialiserat sig på LBD i huvudsak inskränkt sig till empiriska fallstudier. Vad som bryter ny mark i denna avhandling är att vi konstruerar en simuleringsmodell av LBD där vi kan studera lärandeprocessen i detalj för att uppnå en mer hållbar förvaltning över tid. Beträffande modellen som behandlar socialt kapital så har tidigare forskning fokuserat på hur en organisation, eller grupp, kan uppnå hållbar förvaltning. Dock saknas ett helhetsgrepp där som tar hänsyn till alla nivåer; från individnivå (mikro), via gruppnivå (meso), till samhällsnivå (makro). Detta är något som denna avhandling försöker avhjälpa genom att undersöka betydelsen av individers egenskaper, uppbyggnaden av socialt kapital, samt hur detta påverkar emergens av ett samhälle dominerat av mer kooperativa förvaltningsformer respektive mer hierarkiska diton. I papper I and II studeras kärnan av LBD som återkoppling mellan en aktör och en resurs, där aktören lär sig genom upprepade interaktioner med en resurs. Resultaten visar att LBD är av avgörande betydelse för en hållbar förvaltning, speciellt då naturresursens dynamik är stadd i förändring. I den mest hållbara strategin bör aktören värdera nuvarande och framtida fångster lika högt, försiktigt experimentera kring vad aktören upplever som bästa strategi, för att sedan anpassa sin mentala modell till upplevda förändringar i fångst relativt dess insats någorlunda kraftigt. I papper III och IV behandlas uppbyggnaden av förtroende mellan individer och grupp, samt själv-organiserat styre. Genom att använda småskaligt fiske i Mexiko som en illustrativ fallstudie, utvecklades en agent-baserad modell av ett arketypiskt småskaligt fiskesamhälle. Resultaten indikerar att kooperativa förvaltningsformer är mer dominanta i samhällen där de som utför fisket har liknande pålitlighet, starkt gemensamt socialt kapital vid kooperativets start, och då resursen fluktuerar säsongsmässigt (papper III). Papper IV visar att för att uppnå en transformation från hierarkiska förvaltningsformer till kooperativa diton krävs interventioner som inriktar sig på både socialt och finansiellt kapital. Denna avhandling bidrar således till en djupare förståelse kring hur socialt kapital växer fram, samt hur mer strategiska LBD processer bör utformas när abrupta och osäkra förändringar i ekosystemen blir allt vanligare på grund av människans ökade tryck på planeten.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Submitted. Paper 4: Manuscript.

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Frazier, Lisa A. "Policy Cybernetics: A Systems Framework for Responding to and Learning from Complex Problems and Consequences in Public Affairs." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1542614068759527.

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Walrath, Douglas J. "Complex Systems in Engineering and Technology Education: A Mixed Methods Study Investigating The Role Computer Simulations Serve in Student Learning." DigitalCommons@USU, 2008. https://digitalcommons.usu.edu/etd/49.

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This research was conducted to determine if students receiving complex systems instruction in the form of software simulations recognize patterns and underlying elements of complex systems more effectively than students receiving traditional instruction. Complex systems were investigated with an analytic (reductive) approach in a control group and with a synthesis approach in the treatment group. Exploration of this top-down approach to learning complex systems counters traditional bottom-up methodologies, investigating systems and subsystems at the component level. The hypothesis was that students experiencing complex systems scenarios in a computer-based learning environment would outperform their counterparts by constructing a greater number of explanations with emergent-like responses. A mixed method experimental, pretest posttest, control group triangulation design research study was designed for high school students enrolled in an Introduction to Technology and Engineering course. A pretest consisting of one open-ended near transfer problem and one far transfer problem was administered, investigating the generation of reductive (clockwork) and complex (emergent-like) mental models. A stratified sampling procedure was used to assign students to control or treatment groups. Following treatment, an analysis of covariance failed to reveal statistically significant evidence supporting the hypothesis. However, qualitative data in the form of student transcriptions, daily lab reports, and data entry worksheets revealed evidence of emergent-like response and behaviors.

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Fairbrother, Michael. "Exploring Teachers’ Perceptions of the Complex Contextual Factors Influencing Decisions to Participate in Professional Learning on Early Reading and Their Uptake of Classroom Strategies." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41254.

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Research demonstrates those who fail to learn to read well face unfair and lifelong societal disadvantage (Allington, 2011; Castles et al., 2018; Frontier, College, 2018). The number of children who fail to learn to read proficiently remains unacceptable and persists even as research suggests practices to help struggling readers (Allington, 2011; Castles et al., 2018). Building upon dismal findings from literacy networks and evidence from empirical research this study addresses this problem by exploring how contextual factors influence teachers’ learning and practice and student early reading achievement through two research questions: 1) How do contextual variables at the school, board and provincial level influence the planning, delivery and uptake of early reading professional learning opportunities? 2) How do teachers perceive the relationships between (a) their professional learning experiences, (b) their classroom early reading practices, and (c) student reading outcomes? This complexivist multiple instrumental case study explores the role of context upon teachers’ (N = 6) perspectives in three diverse schools (rural, urban and suburban) in one school board with the voices of principals (N = 3) and board-level reading experts (N = 3) providing additional layers of context. Within-case findings demonstrate the importance of meeting local teacher and student needs. Contextual networks represent pathways leading to learning, teaching and student reading development. Cross-case findings reveal the universal needs of the participants for meeting students’ core social and academic needs. Finally, a conceptual framework depicts the interaction of contextual factors within the teaching, learning and student achievement process. Theoretical, empirical and practical implications anchor a discussion proposing a research agenda situating teacher early reading learning into a professional learning collective compassionate to the learning needs of teachers who in turn can be more responsive to the local and universal needs of their students.

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Kurka, David Burth 1988. "Online social networks = knowledge extraction from information diffusion and analysis of spatio-temporal phenomena = Redes sociais online: extração de conhecimento e análise espaço-temporal de eventos de difusão de informação." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259074.

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Orientador: Fernando José Von Zuben
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-27T03:14:35Z (GMT). No. of bitstreams: 1 Kurka_DavidBurth_M.pdf: 1660677 bytes, checksum: 7258daf8129b4dac9d1f647195775d3c (MD5) Previous issue date: 2015
Resumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociais
Abstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connections
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica

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Peirce, Heather Jean. "The dynamics of learning partnerships : case studies from Queensland." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16248/1/Heather_Peirce_Thesis.pdf.

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This study examines the emerging notion of learning partnerships. As the study of such partnerships is a nascent research field, no single definition has yet emerged in the literature. However, within an uncertain and rapidly changing global context, two strategic initiatives have been identified which will support individuals, communities and organisations in their transition to a knowledge-based economy whilst building capacity for change and renewal. These two strategies are fostering learning communities/regions/towns and developing learning partnerships between multiple stakeholders. The term "learning partnership" has appeared in a wide variety of literatures including those of adult learning, management, social science and education. Working papers and emerging case reports identify a diversity of applications and a range of operational models or configurations that link multiple stakeholders. Learning partnerships have been associated with vocational education and training, innovation and research, lifelong learning, organisational learning and knowledge cultivation. These literatures reveal a paucity of Australian research to explain how multiple stakeholders form and develop these configurations, particularly in the Queensland context. The purpose of this study is to build deeper understanding of the meaning of a learning partnership in the Australian and (more precisely) the Queensland context. A working definition of a learning partnership, adopted as the basis for the research, indicates a strategy designed to foster continuous learning, collaboration, innovation and renewal in response to the demands of the knowledge-based economy and knowledge and learning societies. The research focuses on organisational arrangements in order for the researcher to gain deeper understanding from the key stakeholders in their work environments. Three diverse situations were selected for detailed exploration of their issues, relationships, activities, processes and working knowledge. With a view to contributing to emerging theory, an organisational case study methodology was adopted to identify and explore the nature of the relationships and issues confronting the key stakeholders in three Queensland-based learning partnerships. An interpretive theoretical framework draws on the social theory of symbolic interactionism and the "systems thinking" of General Systems Theory. An interpretivist perspective influenced the case study research strategy and guided data collection, analysis and reporting. Within the case studies, data collection methods included observations, informal meetings, synergetic focus groups, semi-structured interviews, diary notes, researcher memos and documents. From these multiple data sources, the researcher was able to assemble three case files. The inductive process for within-case analysis for the case reports, and later, cross-case analysis, integrated as a form of constant comparison technique, was used as a basis for presenting findings. These findings are reported as three separate "in progress" models to address three interrelated research questions. The case reports explain complex and interconnected organisational arrangements - evolving, adapting and responding to internal and external tensions. While there is considerable activity which could be regarded as representing learning partnerships, there is no cohesive policy framework to support such partnerships, and much ambiguity, "muddy" definitions and unclear terminology. It appears that a "new breed" of knowledge-worker is emerging - linking, networking, interacting, exchanging - to work across organisational intersections. The study shows that like "herding cats", co-ordinating and managing the inter relationships at the organisational intersection take time, resources, vision, processes for interaction, individual willingness and "in-kind" support. Whilst there is opportunity for linking disparate groups to cross-fertilise ideas, working knowledge, and information, and there is the potential to cultivate a knowledge and learning ecosystem (a fertile compost heap for knowledge generation and an innovative learning system) - "intellectual horsepower" - such configurations may also derail, realign or stagnate. It is individual stakeholders who form the relationships, interact, share ideas, and build networks, and it is the individual who maintains the relationships, engages in the process and learns from the experience. Therein lies a paradox between the strength of diversity of the collective (synergies) and their weakness as the relationships may be compromised by a single individual who withdraws or transfers. Drawing on a computing analogy, this could be akin to "corruption" in a system which may not be sufficiently robust to tolerate ambiguity, or a system that is too inflexible to survive threats while maintaining the momentum to adapt and renew. On the basis of this research it would appear that a more robust or resilient paradigm is emerging with interconnected, blurred boundaries and much "talking and thinking" about more sustainable futures. The study identifies these as indicative of wider social and economic changes. The thesis proposes three conceptual models as particularly useful in interpreting these "shifting systems and shifting paradigms": the concentric, the centripetal, and the plutonic.

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Peirce, Heather Jean. "The dynamics of learning partnerships : case studies from Queensland." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16248/.

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Анотація:

This study examines the emerging notion of learning partnerships. As the study of such partnerships is a nascent research field, no single definition has yet emerged in the literature. However, within an uncertain and rapidly changing global context, two strategic initiatives have been identified which will support individuals, communities and organisations in their transition to a knowledge-based economy whilst building capacity for change and renewal. These two strategies are fostering learning communities/regions/towns and developing learning partnerships between multiple stakeholders. The term "learning partnership" has appeared in a wide variety of literatures including those of adult learning, management, social science and education. Working papers and emerging case reports identify a diversity of applications and a range of operational models or configurations that link multiple stakeholders. Learning partnerships have been associated with vocational education and training, innovation and research, lifelong learning, organisational learning and knowledge cultivation. These literatures reveal a paucity of Australian research to explain how multiple stakeholders form and develop these configurations, particularly in the Queensland context. The purpose of this study is to build deeper understanding of the meaning of a learning partnership in the Australian and (more precisely) the Queensland context. A working definition of a learning partnership, adopted as the basis for the research, indicates a strategy designed to foster continuous learning, collaboration, innovation and renewal in response to the demands of the knowledge-based economy and knowledge and learning societies. The research focuses on organisational arrangements in order for the researcher to gain deeper understanding from the key stakeholders in their work environments. Three diverse situations were selected for detailed exploration of their issues, relationships, activities, processes and working knowledge. With a view to contributing to emerging theory, an organisational case study methodology was adopted to identify and explore the nature of the relationships and issues confronting the key stakeholders in three Queensland-based learning partnerships. An interpretive theoretical framework draws on the social theory of symbolic interactionism and the "systems thinking" of General Systems Theory. An interpretivist perspective influenced the case study research strategy and guided data collection, analysis and reporting. Within the case studies, data collection methods included observations, informal meetings, synergetic focus groups, semi-structured interviews, diary notes, researcher memos and documents. From these multiple data sources, the researcher was able to assemble three case files. The inductive process for within-case analysis for the case reports, and later, cross-case analysis, integrated as a form of constant comparison technique, was used as a basis for presenting findings. These findings are reported as three separate "in progress" models to address three interrelated research questions. The case reports explain complex and interconnected organisational arrangements - evolving, adapting and responding to internal and external tensions. While there is considerable activity which could be regarded as representing learning partnerships, there is no cohesive policy framework to support such partnerships, and much ambiguity, "muddy" definitions and unclear terminology. It appears that a "new breed" of knowledge-worker is emerging - linking, networking, interacting, exchanging - to work across organisational intersections. The study shows that like "herding cats", co-ordinating and managing the inter relationships at the organisational intersection take time, resources, vision, processes for interaction, individual willingness and "in-kind" support. Whilst there is opportunity for linking disparate groups to cross-fertilise ideas, working knowledge, and information, and there is the potential to cultivate a knowledge and learning ecosystem (a fertile compost heap for knowledge generation and an innovative learning system) - "intellectual horsepower" - such configurations may also derail, realign or stagnate. It is individual stakeholders who form the relationships, interact, share ideas, and build networks, and it is the individual who maintains the relationships, engages in the process and learns from the experience. Therein lies a paradox between the strength of diversity of the collective (synergies) and their weakness as the relationships may be compromised by a single individual who withdraws or transfers. Drawing on a computing analogy, this could be akin to "corruption" in a system which may not be sufficiently robust to tolerate ambiguity, or a system that is too inflexible to survive threats while maintaining the momentum to adapt and renew. On the basis of this research it would appear that a more robust or resilient paradigm is emerging with interconnected, blurred boundaries and much "talking and thinking" about more sustainable futures. The study identifies these as indicative of wider social and economic changes. The thesis proposes three conceptual models as particularly useful in interpreting these "shifting systems and shifting paradigms": the concentric, the centripetal, and the plutonic.

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Karoki, Teckie Michelle. "Leadership Styles and Learning for Performance Within Commercial Banks in Kenya." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2839.

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The literature indicates that leaders influence the establishment of organizational learning culture (OLC) by the application of transformational (TFO), transactional (TAC), and passive-avoidant (PAV) styles. Further, the literature links OLC to the financial performance of organizations by leadership involvement in establishing learning organizations. However, the manner in which the practice of OLC occurs and the relationship of OLC with TFO, TAC, and PAV is unclear, as is the link between OLC and financial performance (ROA); especially for growing economies outside North America. The purpose of this study was to address this gap in the literature through a quantitative study of leadership styles and their relationship to OLC based on complexity and contingency leadership theories, and organizational learning theory. The research questions focused on establishing the association between TFO, TAC, and PAV and OLC, and the link between OLC and ROA. Data from 40 commercial banks in Kenya were collected and multiple regression models developed. TFO and PAV Leadership styles were associated significantly with OLC; TAC did not have a significant relationship with OLC. OLC was linked significantly to ROA. The results of this study show that leaders of commercial banks build relationships with followers and support learning within their institutions; however, the results of this study show that these leaders engage in a limited form of organizational learning practice. This study has potential to contribute to positive social change by providing information about leadership and organizational learning strategies that advance transformational engagement with followers and organizational performance.

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Mirzaei, Ardalan. "Development of a Dynamic Model for Health Information Seeking Behaviour." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28740.

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Models and theories in health information seeking behaviour (HISB) have been developed to explore a patient's information search at a single time point. However, a patient's search for health information for specific medicines or medical conditions does not remain static but progresses and transforms over time. There are, however, limited studies that quantitatively explore HISB as a dynamic concept. Furthermore, complex systems modelling can be used to analyse the dynamic nature of HISB. Therefore, this research aimed to create an agent-based model of HISB to explore its dynamism. The first study was a systematic literature review of the HISB field. In addition, a temporal network analysis was applied to illustrate the connections of the HISB predictors through time identified from the research articles. The review identified 1595 significant predictors categorised into a taxonomy of 67 items. The study also identified publicly available databases of consumer surveys in HISB that were used as part of the second study for database mapping. Four databases containing consumers' HISB questions had their questions mapped and manually labelled in the dataset according to a taxonomy. Six machine learning methods were used to build a model to classify the HISB variables in a dataset. Mapping the HISB variables was best achieved using three databases and three of the six models, correctly identifying 95% of the HISB variables. The final study developed an agent-based model (ABM) of HISB in NetLogo. The ABM dynamic features were demonstrated by incorporating feedback loops and identifying emergent behaviour via permutation testing. Two of the features were consistent with real-world data. This thesis demonstrated that HISB is a dynamic process through the development of an ABM. This initial model provides a basis for further work to incorporate this ABM into a multi-agent model to observe other system-level behaviours and validate the model against longitudinal data.

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Salavati, Sadaf. "Novel Use of Mobile and Ubiquitous Technologies in Everyday Teaching and Learning Practices : A Complex Picture." Licentiate thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-31341.

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As of autumn 2011, all schools in Sweden have adopted and applied the latest curriculum for the compulsory school system. The following is written in concern to technology: "The school is responsible for ensuring that each pupil on completing compulsory school: [...] can use modern technology as a tool in the search for knowledge, communication, creativity and learning." (Skolverket, 2011, pp.13-14) With this said, there are no guidelines or manuals on how this is to be conducted. In a report from the Swedish Schools Inspectorate it was concluded that the investment in technology is not being used for school education. The education systems keep investing in technology in the belief that schools and teachers will sooner or later adopt and benefit from the use of mobile and ubiquitous technologies. The aim of this study is to “create an understanding of the aspects that have an impact on adopting novel use of mobile and ubiquitous technologies in everyday teaching and learning practices in compulsory schools.” The empirical foundation will be based upon three projects: Geometry Mobile (GeM); Learning Ecology with Technologies from Science for Global Outcomes (LETS GO); and Collaborative Learning Using Digital Pens and Interactive Whiteboards (Collboard). All were conducted at local compulsory schools in Växjö municipality, Sweden, in collaboration with teachers, students and fellow researchers from the CeLeKT research group at Linnaeus University. Two Thematic Analyses have been conducted: the first, an inductive analysis exploring the Students’ and Teachers’ Experience of using Mobile and Ubiquitous Technologies in their learning and teaching environment. The second analysis is deductive and uses themes from the Unified Theory of Acceptance and Use of Technology models with the aim of understanding the Perception and Acceptance of Teachers’ use of Mobile and Ubiquitous Technologies. In the results from the two analyses there are clear indicators on the added value that mobile and ubiquitous technology brings to the classrooms: students are able to actively participate, collaborate and discuss in different learning settings, which enhances their understanding of the subject at hand. The challenges are mainly to be found in the lack of training and education in use of the technology as a supporting tool for teaching and learning. Further factors influencing the teachers and the students are ease of use and reliability of the technology and societal changes. The results of the analysis and the theoretical base of Technology Enhanced Learning have been illustrated with Soft Systems Methodologies Rich Picture, providing a holistic view of the problematic situation and making it possible to discuss the various parts as well as the situation as a whole. This study indicates that there are several factors influencing the adoption of the novel use of mobile and ubiquitous technologies in everyday teaching and learning within a complex situation on different levels.

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Ma, Fei. "Low achievement in English language learning : a case study of a Chinese tier-3 university under the lens of complex systems theory." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/51951/.

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The context of current research is a tier-3 university in Ningbo China, where English education is compulsory for all students. As an English teacher working in this university for 17 years, I note that each year a large number of students have very poor performance and are struggling in English learning. My inquiry aims to find out the major reasons giving rise to their low achievement, so that a more effective intervention could be designed to help them. My literature review leads me to focus on 12 factors that are traditionally claimed to have associations with English low achievement. Meanwhile I remain open to the new factors arising during the whole research process. The priority of this research is to identify the key causal factors and reveal the nature of these factors. Through extensive reviews, I realize that English language development could be best viewed as a complex system consisting three sub-systems, i.e., the learner, the teacher and the learning environment. English low achievement is the negative emergence of this system. Plenty of contributing factors are involved in this process. They are intertwined and interactive in intricate ways. Because of this complex interaction, the outcome of learning might be more than or less than the sum of the factors. Exploring the independent factors alone cannot draw a clear picture of how low achievement is developed. Complex systems theory integrates the parts and the wholeness, therefore offers a deeper and more encompassing theoretical framework for current research. My research design is a case study with mixed methods. I purposefully selected three sample classes which represent students from three different disciplines, i.e., natural science, arts and humanities, and social science. Three tools are used to collect data, namely non-participant observation, semi-structured interview and questionnaire survey. I observed the classroom teaching of each class for two or three times, and their after-class self-learning activity for two or three times as well. In addition, I interviewed seven low-achieving and two high-achieving students from the three classes, as well as their three English teachers. Furthermore, I conducted questionnaire surveys in the three classes, and among the English teachers in this university. My findings demonstrate that there are three categories of factors in respect to English low achievement. First, the literature shows that the factors such as gender, family background and IQ have close association with academic achievement. However, the current study reveals that they are less likely to be the major causal factors for English low achievement. Second, some factors have moderate associations with the low achievement, that is, they do not play the critical roles in contributing to this learning outcome. These factors include lack of integrative motivation, peers’ adverse influence, poor learning strategies, lack of self-confidence, the problem of curriculum, and the negative attitude toward learning. Last, most importantly, this inquiry finds that four factors are likely to be the key reasons resulting in English low achievement, i.e., lack of effort, lack of interest, poor prior attainment and teacher’s adverse effect. Generally the low achievement is chiefly the result of interactions of these four factors. Under the lens of complex systems theory, it is revealed that the sub-systems consist of the learner him/herself, the teacher and the learning environment, and that the causal factors display the nature of interconnectedness and dynamism. Failure of one of them may lead to failure of dependent others. English low achievement is likely to be the result of chain reactions of multiple failing factors. In addition, the sub-systems and their constituent factors are not static, but in flux. In one time period, a factor may exert positive influence upon English learning; over time, the factor may change and exercise negative influence. In education, therefore, it is important not only to stimulate but to maintain key favourable factors in dealing with low achieving students. This inquiry draws the learning trajectories of low achievers by exploring their learning experience. Their performance is usually good at the outset of their English learning. In their secondary schools, a negative perturbation usually breaks into their academic life, which causes a butterfly effect, then, a slippery slope starts. They are stuck into the attractor of low achievement, which is difficult to escape from without external assistance. Most of them once tried to improve their performance, but failed. As a result their low achievement continues till university in which some turn the tide with the positive change of the three sub-systems. This research finds that a certain percentage of English low achievers are reversible, however it demands the concerted and persistent effort of all three parities, i.e., the learner, the teacher and the university. As far as the learner is concerned, the intervention should primarily deal with the affective variables with regard to his/her problems related to effort, interest, prior attainment and teachers.

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43

Goudarzi, Alireza. "On the Effect of Topology on Learning and Generalization in Random Automata Networks." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/193.

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We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical connectivity by showing that the network ensemble manifests maximal topological diversity near Kc. Our work is partly motivated by self-assembled molecular and nanoscale electronics. Our findings allow to determine an automata network topology class for efficient and robust information processing.

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44

Molter, Colin. "Storing information through complex dynamics in recurrent neural networks." Doctoral thesis, Universite Libre de Bruxelles, 2005. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211039.

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The neural net computer simulations which will be presented here are based on the acceptance of a set of assumptions that for the last twenty years have been expressed in the fields of information processing, neurophysiology and cognitive sciences. First of all, neural networks and their dynamical behaviors in terms of attractors is the natural way adopted by the brain to encode information. Any information item to be stored in the neural net should be coded in some way or another in one of the dynamical attractors of the brain and retrieved by stimulating the net so as to trap its dynamics in the desired item's basin of attraction. The second view shared by neural net researchers is to base the learning of the synaptic matrix on a local Hebbian mechanism. The last assumption is the presence of chaos and the benefit gained by its presence. Chaos, although very simply produced, inherently possesses an infinite amount of cyclic regimes that can be exploited for coding information. Moreover, the network randomly wanders around these unstable regimes in a spontaneous way, thus rapidly proposing alternative responses to external stimuli and being able to easily switch from one of these potential attractors to another in response to any coming stimulus.

In this thesis, it is shown experimentally that the more information is to be stored in robust cyclic attractors, the more chaos appears as a regime in the back, erratically itinerating among brief appearances of these attractors. Chaos does not appear to be the cause but the consequence of the learning. However, it appears as an helpful consequence that widens the net's encoding capacity. To learn the information to be stored, an unsupervised Hebbian learning algorithm is introduced. By leaving the semantics of the attractors to be associated with the feeding data unprescribed, promising results have been obtained in term of storing capacity.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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45

Réné, Lydie. "La dynamique des interactions au coeur d'un dispositif de formation à distance, vu comme un système complexe de communication : focus sur les représentations et les communications des acteurs." Thesis, Aix-Marseille 3, 2011. http://www.theses.fr/2011AIX30059.

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Cette thèse se propose de montrer comment les interactions vont influer sur le système complexe de communication d'une formation à distance, et en dévoiler les limites. L'objectif général est de montrer que, dans ce système, il est possible d'expliquer l'abandon par les pertes de prises qui se dessinent dans la dynamique entre les représentations et les communications des acteurs, et que certaines limites du système favorisent ces pertes de prise. La finalité de cette thèse est de cerner ces limites pour définir une catégorie d'abandon communicationnel. Cette recherche qualitative s'appuie sur la théorie de la communication pragmatique de Palo Alto, pour analyser les interactions entre les acteurs, et sur la théorie des représentations sociales, pour traduire en images l'évolution des représentations sur une année. Le concept de « prise » relie la situation communicationnelle à l'abandon. Il permet de confronter les repères communs aux acteurs et leurs perceptions personnelles des situations vécues, pour rendre compte de leur moyen d'agir et de s'engager
The purpose of this thesis is to show how interactions can influence a complex system of distance training communication, and reveal its limits. The main aim is to show that dropping out can be explained by loss of grips from the representations and communication dynamics of involved actors, and how some limits of the system favor this loss of grips. The end purpose of this thesis is to number these limits by defining a category of communicational drop out. This qualitative research is based on the Palo Alto theory of pragmatic communication, to analyze interactions between actors, as well as the theory of social representations by translating the evolution of representations into images, over one year. The concept of “grip” creates a link between communicational events and dropping out. It confronts actors' shared landmarks with their personal perception of lived events, which in turn, accounts for their level of commitment and action

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46

Barris, Coralie Sian. "An examination of learning design in elite springboard diving." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63807/1/Coralie_Barris_Thesis.pdf.

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The overarching aim of this programme of work was to evaluate the effectiveness of the existing learning environment within the Australian Institute of Sport (AIS) elite springboard diving programme. Unique to the current research programme, is the application of ideas from an established theory of motor learning, specifically ecological dynamics, to an applied high performance training environment. In this research programme springboard diving is examined as a complex system, where individual, task, and environmental constraints are continually interacting to shape performance. As a consequence, this thesis presents some necessary and unique insights into representative learning design and movement adaptations in a sample of elite athletes. The questions examined in this programme of work relate to how best to structure practice, which is central to developing an effective learning environment in a high performance setting. Specifically, the series of studies reported in the chapters of this doctoral thesis: (i) provide evidence for the importance of designing representative practice tasks in training; (ii) establish that completed and baulked (prematurely terminated) take-offs are not different enough to justify the abortion of a planned dive; and (iii), confirm that elite athletes performing complex skills are able to adapt their movement patterns to achieve consistent performance outcomes from variable dive take-off conditions. Chapters One and Two of the thesis provide an overview of the theoretical ideas framing the programme of work, and include a review of literature pertinent to the research aims and subsequent empirical chapters. Chapter Three examined the representativeness of take-off tasks completed in the two AIS diving training facilities routinely used in springboard diving. Results highlighted differences in the preparatory phase of reverse dive take-offs completed by elite divers during normal training tasks in the dry-land and aquatic training environments. The most noticeable differences in dive take-off between environments began during the hurdle (step, jump, height and flight) where the diver generates the necessary momentum to complete the dive. Consequently, greater step lengths, jump heights and flight times, resulted in greater board depression prior to take-off in the aquatic environment where the dives required greater amounts of rotation. The differences observed between the preparatory phases of reverse dive take-offs completed in the dry-land and aquatic training environments are arguably a consequence of the constraints of the training environment. Specifically, differences in the environmental information available to the athletes, and the need to alter the landing (feet first vs. wrist first landing) from the take-off, resulted in a decoupling of important perception and action information and a decomposition of the dive take-off task. In attempting to only practise high quality dives, many athletes have followed a traditional motor learning approach (Schmidt, 1975) and tried to eliminate take-off variations during training. Chapter Four examined whether observable differences existed between the movement kinematics of elite divers in the preparation phases of baulked (prematurely terminated) and completed take-offs that might justify this approach to training. Qualitative and quantitative analyses of variability within conditions revealed greater consistency and less variability when dives were completed, and greater variability amongst baulked take-offs for all participants. Based on these findings, it is probable that athletes choose to abort a planned take-off when they detect small variations from the movement patterns (e.g., step lengths, jump height, springboard depression) of highly practiced comfortable dives. However, with no major differences in coordination patterns (topology of the angle-angle plots), and the potential for negative performance outcomes in competition, there appears to be no training advantage in baulking on unsatisfactory take-offs during training, except when a threat of injury is perceived by the athlete. Instead, it was considered that enhancing the athletes' movement adaptability would be a more functional motor learning strategy. In Chapter Five, a twelve-week training programme was conducted to determine whether a sample of elite divers were able to adapt their movement patterns and complete dives successfully, regardless of the perceived quality of their preparatory movements on the springboard. The data indeed suggested that elite divers were able to adapt their movements during the preparatory phase of the take-off and complete good quality dives under more varied take-off conditions; displaying greater consistency and stability in the key performance outcome (dive entry). These findings are in line with previous research findings from other sports (e.g., shooting, triple jump and basketball) and demonstrate how functional or compensatory movement variability can afford greater flexibility in task execution. By previously only practising dives with good quality take-offs, it can be argued that divers only developed strong couplings between information and movement under very specific performance circumstances. As a result, this sample was sometimes characterised by poor performance in competition when the athletes experienced a suboptimal take-off. Throughout this training programme, where divers were encouraged to minimise baulking and attempt to complete every dive, they demonstrated that it was possible to strengthen the information and movement coupling in a variety of performance circumstances, widening of the basin of performance solutions and providing alternative couplings to solve a performance problem even when the take-off was not ideal. The results of this programme of research provide theoretical and experimental implications for understanding representative learning design and movement pattern variability in applied sports science research. Theoretically, this PhD programme contributes empirical evidence to demonstrate the importance of representative design in the training environments of high performance sports programmes. Specifically, this thesis advocates for the design of learning environments that effectively capture and enhance functional and flexible movement responses representative of performance contexts. Further, data from this thesis showed that elite athletes performing complex tasks were able to adapt their movements in the preparatory phase and complete good quality dives under more varied take-off conditions. This finding signals some significant practical implications for athletes, coaches and sports scientists. As such, it is recommended that care should be taken by coaches when designing practice tasks since the clear implication is that athletes need to practice adapting movement patterns during ongoing regulation of multi-articular coordination tasks. For example, volleyball servers can adapt to small variations in the ball toss phase, long jumpers can visually regulate gait as they prepare for the take-off, and springboard divers need to continue to practice adapting their take-off from the hurdle step. In summary, the studies of this programme of work have confirmed that the task constraints of training environments in elite sport performance programmes need to provide a faithful simulation of a competitive performance environment in order that performance outcomes may be stabilised with practice. Further, it is apparent that training environments can be enhanced by ensuring the representative design of task constraints, which have high action fidelity with the performance context. Ultimately, this study recommends that the traditional coaching adage 'perfect practice makes perfect", be reconsidered; instead advocating that practice should be, as Bernstein (1967) suggested, "repetition without repetition".

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47

Nordell, Dan. "ISIS – Information principles, skills, relations and capabilities for an inclusive learning society : -." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-21897.

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In our complex world of today we see that the leader- and followership is getting harder and harder to “command and control” our organizations and our society. The awareness of the decision makers are often too fragmented in relation to the often complex real situations out there. The information is only flowing in organizational stovepipes and not across boundaries as it should today. The patterns of mutual complex dependencies have taken over and we have difficulties in controlling the consequences of our decisions. The relations over organizational borders are often weak and fragmented and the mutual trust is low. Technology has always been a driver for the society to evolve and our idea is that technology can be the main driver for evolving cross boundary collaboration in order to meet the demands of the world. Technology can be the main driver but only if we consider all of the important dimensions when implementing Information and communication technologies (ICT) In order to find the right method of using technology and scientific methods for achieving better cross boundary collaboration a number of data collection activities has been performed, described and analyzed in the work with this thesis. The activities has been diverse in its nature, brainstorming activities, qualitative interviews and a small case study has been combined in order to derive the result – a path forward against further research for a better cross boundary collaboration in our western community. The thesis now have identified a path forward and a scientific framework for taking all of our experiences, existing capabilities, earlier performed research one step further and lift it up to the cross boundary level in organizations and in our society. We have the chance of cultivating all of the properties, relations, amounts of information, and evolve our technology. This may be all that we need in order to achieve a more socially sustainable climate in leader- and followership in our organizations and society. Who knows … maybe we can change the world … or at least make a real difference somewhere!

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48

Zheng, Hongying. "Complex, dynamic and co-adaptive systems : a study of language teachers' beliefs about EFL teaching and learning in the context of secondary schools in China." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609592.

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49

Peniak, Martin. "GPU computing for cognitive robotics." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3052.

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This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.

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50

STRADA, FRANCESCO. "Augmented reality and serious games for learning: exploring potentialities, assessing effectiveness, and investigating user experience." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2942118.

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Дисертації з теми "Complex systems learning"

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