Tesis sobre el tema "Complex systems learning"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores tesis para su investigación sobre el tema "Complex systems learning".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore tesis sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Sullivan, John P. "Emergent Learning: Three Learning Communities as Complex Adaptive Systems". Thesis, Boston College, 2009. http://hdl.handle.net/2345/663.
Texto completoIn 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
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.
Texto completoBondorowicz, Stefan. "Adaptive control of complex dynamic systems". Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302787.
Texto completoEagle, Nathan Norfleet. "Machine perception and learning of complex social systems". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32498.
Texto completoIncludes 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.
Al-Jubouri, Bassma. "Multi-criteria optimisation for complex learning prediction systems". Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30857/.
Texto completoTong, 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.
Texto completoStatistics
Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
Texto completoCERBONI, BAIARDI LORENZO. "Adaptive models of learning in complex physical and social systems". Doctoral thesis, Urbino, 2016. http://hdl.handle.net/11576/2630552.
Texto completoTopcu, Taylan Gunes. "Management of Complex Sociotechnical Systems". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97844.
Texto completoDoctor 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.
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.
Texto completoMcKeown, 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.
Texto completoGelbrecht, 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.
Texto completoComplex 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.
Haghighi, Mona. "Rule-based Risk Monitoring Systems for Complex Datasets". Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6248.
Texto completoAzamfar, 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.
Texto completoClissold, 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.
Texto completoGelbrecht, 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.
Texto completoAbdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering". Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.
Texto completoVAIRO, 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.
Texto completoArruda, 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/.
Texto completoThe 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
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.
Texto completoRobbin, Alice y 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.
Texto completoSTAFFINI, ALESSIO. "ESSAYS ON COMPLEX ECONOMIC SYSTEMS AND ARTIFICIAL INTELLIGENCE". Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/131851.
Texto completoThis 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.
AlZahrani, Saleh Saeed. "Regionally distributed architecture for dynamic e-learning environment (RDADeLE)". Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/3814.
Texto completoBarbieri, Matteo <1993>. "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.
Texto completoKalantari, 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.
Texto completoAppeltant, Lennert. "Reservoir computing based on delay-dynamical systems". Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84144.
Texto completoHasan, 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.
Texto completoxi, 192 leaves : ill. ; 29 cm
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.
Texto completoMost 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
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.
Texto completoO 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.
Cambe, Jordan. "Understanding the complex dynamics of social systems with diverse formal tools". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN043/document.
Texto completoFor 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
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.
Texto completoLindkvist, 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.
Texto completoI 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.
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.
Texto completoWalrath, 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.
Texto completoFairbrother, 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.
Texto completoKurka, 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.
Texto completoDissertação (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
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.
Texto completoPeirce, Heather Jean. "The dynamics of learning partnerships : case studies from Queensland". Queensland University of Technology, 2006. http://eprints.qut.edu.au/16248/.
Texto completoKaroki, Teckie Michelle. "Leadership Styles and Learning for Performance Within Commercial Banks in Kenya". ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2839.
Texto completoMirzaei, Ardalan. "Development of a Dynamic Model for Health Information Seeking Behaviour". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28740.
Texto completoSalavati, 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.
Texto completoMa, 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/.
Texto completoGoudarzi, 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.
Texto completoMolter, 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.
Texto completoIn 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
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.
Texto completoThe 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
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.
Texto completoNordell, 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.
Texto completoZheng, 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.
Texto completoPeniak, Martin. "GPU computing for cognitive robotics". Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3052.
Texto completoSTRADA, 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.
Texto completo