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1
Holzhauer, Sascha [Verfasser]. "Dynamic Social Networks in Agent-based Modelling : Increasingly Detailed Approaches of Network Initialisation and Network Dynamics / Sascha Holzhauer." Kassel : Kassel University Press, 2017. http://d-nb.info/1137030445/34.
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Georgieva, Kristina Boyanova. "Boolean network simulation for exploring the dynamics of industrial networks." Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289295.
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Perera, Pannilage Supun Sachinthaka. "Topological Approach for Modelling the Structure, Dynamics and Robustness of Supply Chain Networks." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20418.
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Traditionally simple and linear supply chains have, in recent years, evolved towards highly complex networked systems, due to globalisation and product specialisation. Recent application of network models to supply chain systems have revealed the existence of non-trivial and universal topological footprints, which provide important system level insights. This thesis uses topological network models to investigate the structure, dynamics and robustness of supply chain networks (SCNs). Firstly, the common topological characteristics of real-world SCNs are identified, by considering both undirected inter-firm relationship and directed material flow SCNs. Based on this analysis, it is evident that the number of firm-level connections in each SCN follow the power law distribution with power law exponents in the range of 1.5 - 3.5. A fitness-based growth model is then presented to simulate such topologies. The mechanism through which this growth model operates is justified on the basis of risk averse firm behaviour. The second half of this thesis is concerned with the role of SCN topology on the evolution of cooperation and robustness. It is found that the SCN topology, the level of rationality of firms and the relative payoff differences are all essential elements in the evolution of co-operation when strategic inter-firm interactions in an SCN are represented as Prisoner Dilemma games. Finally, a novel methodology to quantify and improve the robustness of material flow SCNs is presented. Here, the specific case of a material flow SCN with multi-sourcing, which is characterised by a tiered structure with directed and weighted links, is considered. An indicative robustness metric is proposed to characterise the robustness of the SCN, considering the degree to which supply chains overlap with each other. Since this model incorporates information beyond the topology of the SCN, it is a useful tool for decision making by the practitioners.
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Renals, Stephen John. "Speech and neural network dynamics." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/14271.
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This thesis is concerned with two principal issues. Firstly the radial basis functions (RBF) network is introduced and its properties related to other statistical and neural network classifiers. Results from a series of speech recognition experiments, using this network architecture, are reported. These experiments included a continuous speech recognition task with a 571 word lexicon. Secondly, a study of the dynamics of a simple recurrent network model is presented. This study was performed numerically, via a survey of network power spectra and a detailed investigation of the dynamics displayed by a particular network. Word and sentence recognition errors are reported for a continuous speech recognition system using RBF network phoneme modelling with Viterbi smoothing, using either a restricted grammar or no grammar whatsoever. In a cytopathology task domain the best RBF/Viterbi system produced first choice word errors of 6% and sentence errors of 14%, using a grammar of perplexity 6. This compares with word errors of 4% and sentence errors of 8% using the best CSTR hidden Markov model configuration. RBF networks were also used for a static vowel labelling task using hand-segmented vowels excised from continuous speech. Results were not worse than those obtained using statistical classifiers. The second part of this thesis is a computational study of the dynamics of a recurrent neural network model. Two investigations were undertaken. Firstly, a survey of network power spectra was used to map out the temporal activity of this network model (within a four dimensional parameter space) via summary statistics of the network power spectra. Secondly, the dynamics of a particular network were investigated. The dynamics were analysed using bifurcation diagrams, power spectra, the computation of Liapunov exponents and fractal dimensions and the plotting of 2-dimensional attractor projections. Complex dynamical behaviour was observed including Hopf bifurcations, the Ruell-Takens-Newhouse route to chaos with mode-locking at rational winding numbers, the period-doubling route to chaos and the presence of multiple coexisting attractors.
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Johnson, Hope Amy. "Plasticity of cortical network dynamics." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1835448081&sid=7&Fmt=2&clientId=1564&RQT=309&VName=PQD.
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Battiston, Federico. "The structure and dynamics of multiplex networks." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/30631.
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Network science has provided useful answers to research questions in many fields, from biology to social science, from ecology to urban science. The first analyses of networked systems focused on binary networks, where only the topology of the connections were considered. Soon network scientists started considering weighted networks, to represent interactions with different strength, cost, or distance in space and time. Also, connections are not fixed but change over time. This is why in more recent years, a lot of attention has been devoted to temporal or time-varying networks. We now entered the era of multi-layer networks, or multiplex networks, relational systems whose units are connected by different relationships, with links of distinct types embedded in different layers. Multiplexity has been observed in many contexts, from social network analysis to economics, medicine and ecology. The new challenge consists in applying the new tools of multiplex theory to unveil the richness associated to this novel level of complexity. How do agents organise their interactions across layers? How does this affect the dynamics of the system? In the first part of the thesis, we provide a mathematical framework to deal with multiplex networks. We suggest metrics to unveil multiplexity from basic node, layer and edge properties to more complicated structure at the micro- and meso-scale, such as motifs, communities and cores. Measures are validated through the analysis of real-world systems such as social and collaboration networks, transportation systems and the human brain. In the second part of the thesis we focus on dynamical processes taking place on top of multiplex networks, namely biased random walks, opinion dynamics, cultural dynamics and evolutionary game theory. All these examples show how multiplexity is crucial to determine the emergence of unexpected and instrinsically multiplex collective behavior, opening novel perspectives for the field of non-linear dynamics on networks.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-89260.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Brookes, Richard. "Structure and dynamics in network liquids." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:af233937-3168-498a-b7cc-eed758f5e5de.
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The onset of the Glass Transition in tetrahedral network liquids is associated with the over-constrained nature of the structure and the low ability of the ions to move relative to one another. We investigate the interplay between the structure and dynamics in BeF2, a template for the ideal tetrahedral system. We see that the ionic diffusion coefficients can be predicted from the calculated viscosity of the system using the Eyring hopping model of diffusion, with a diffusive jump length approximately corresponding to the radius of the first coordination sphere. Novel correlation functions are developed which enable us to identify the events responsible, on an atomistic level, for the structural rearrangements which correspond to the barrier crossing in this hopping model of diffusion, and we find that these events can be identified as the exchange of ions in the local coordination poly- hedra, or cage, of the cations. The calculation of the rate of the decay of these cages allows us to predict the macroscopic diffusion coefficients with the definition of a jump length over which the diffusive hops occur, and to scale the behaviour of the system at different temperatures by setting the cage lifetime as an effective clock for the system. Comparison between simulations performed with and without the inclusion of the effects of anion polarisation suggest that the polarisation plays an important role in the ability of the system to undergo the cage decay events and to create the defect sites which facilitate a decrease in the number of constraints acting in the system. The decay of the cages describes the local rearrangement of the ions in the first coordi- nation shell of a given ion. The development of other correlation functions allows us to investigate the spatial relationship between these cage decay events over longer length and time scales, and also to investigate how the local structure of the first coordination shells of the cations relates to their ability to undergo the cage decay events and to form the defects. These functions are then used to investigate the link between the structure and the dy- namics in some molten trichloride systems, which have different network structures, and hence a different relationship between the cage decay and the diffusion. Finally, we investigate the effect of changing the potential parameters in BeF2, and we find that the effective polarisability of the system can be controlled such that a less diffusive system may be described, giving a good representation of both structural and dynamical experimental data.
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Ratanachote, Po-ngarm. "Distribution network dynamics with correlated demands." Thesis, Cardiff University, 2011. http://orca.cf.ac.uk/54428/.
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The distribution network designs for two-level supply chains have been analysed using stochastic analytical methods. The market demands faced by multiple retailers are correlated. The correlated demand is modelled as a first order Vector Auto-Regressive process, which is used to represent the progression of and relationships in sets of time series of demand. All participants are assumed to operate an Order-Up-To policy with a Minimum Mean Squared Error forecasting. Inventory and capacity costs have been considered. Control engineering methods have been exploited to obtain the closed form expressions of the variances of the inventory levels and the order rates. The ratios of costs between the decentralised and centralised systems have been used to evaluate the economic performance of the consolidated distribution network. The variance expressions are the key components for the cost ratios. Insights about the system can also be obtained from the analysis of the variance expressions. The impacts of demand patterns, lead-times and the number of decentralised locations on the consolidation decision have been investigated. The results show that the auto-correlation and cross-correlation of the market demands highly affect the consolidation decisions. The Square Root Law for Inventory and Bullwhip has been proved to hold with certain demand correlations. Consolidation scenarios that are always attractive under a specific demand pattern and a set of constraints about the lead-times have been presented. The structural transition of the demand into orders placed onto higher echelons has been investigated. The result shows that higher echelons may not need the point-of-sales data as it is already contained in the order they receive from the retailers. Finally, the model has been validated by its application to real world data and has shown to be a useful tool for practitioners to investigate the dynamic behaviour and economic performance of the distribution network design.
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Lospinoso, Joshua Alfred. "Statistical models for social network dynamics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5ed9b9c-020c-4379-a5f2-cf96439ca37c.
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The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.
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Beeren, L. K. "Probing network dynamics in barrel cortex." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1348307/.
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Recent studies have demonstrated that a rat can be trained to behaviourally report the electrical stimulation of a single cortical neuron (Houweling and Brecht, 2008). Other studies have reported detection of the optogenetic stimulation of ~300 neurons (Huber et al., 2008). However, although the animal can detect the stimulation, it is unclear what effect this small perturbation is having on the network and to what degree this will alter the animal’s ability to perform a task. This thesis investigates the effect on both the local network and on behaviour of several magnitudes of neuronal perturbation, from a single spike to the excitation of several thousand neurons. Finding the limitations under which a network can function provides powerful insights into how neurons interact to form meaningful networks. I performed simultaneous intra- and multi-unit extracellular recordings from the rat barrel cortex. I introduced a single spike into the patched neuron, and monitored the evolution of network activity via the extracellular probe. I found that the introduction of a single spike in a neuron produces a detectable increase in firing rate in the local network. To extend the investigation, channelrhodopsin-2 (ChR2), a light-sensitive membrane protein, was electroporated under visual control into a small number (1 - 10) of layer 2/3 pyramidal cells in the somatosensory cortex of the adult mouse. After exciting the ChR2-positive neurons, the resulting network activity was measured both by cell-attached and whole-cell patch-clamp recordings from nearby neurons and by monitoring up to 50 nearby cells in different cortical layers using the multi-site silicon probe. I found that excitation of a small number of neurons caused an increase in the spike rate of the local network, which lasted up to 300 ms. On the next level, large-scale perturbations were introduced into the brain by the optogenetic excitation of several thousand neurons in the cortexof transgenic mice expressing ChR2 under the Thy1 promoter. A short (2-20 ms) pulse of blue light produced a strong initial response, measured in both the LFP and spiking activity across supragranular layers of the barrel cortex. This initial response was often followed by ~5 bursts of spikes which resulted in an oscillation in the LFP. This oscillation was found to be of similar frequency and time-scale to an oscillation recorded in the barrel cortex resulting from the deflection of a single whisker. After pharmacologically blocking activity in the thalamus, confirmed by loss of the whisker response, the light-induced oscillations disappeared, indicating that the thalamus is necessary for their propagation. Optogenetic stimulation was also able to generate oscillations in the awake animal. I investigated the effect of such a large perturbation on mice undergoing a simple whisker-deflection discrimination task. It was found that the performance of the mice initially dropped to chance level if a strong perturbation was delivered 100 ms before the sensory stimulation. If the strong perturbation was sustained for every trial, the performance of the mice did not improve. If the perturbing stimulation was removed and then introduced gradually, the animal was able to adapt to the stimulation and learn to perform the task despite the perturbation. In summary, small perturbations have a measurable effect on the local network, implying the use of a rate code for at least some brain states in the barrel cortex. A large perturbation produces a strong cortical response, which often leads to a strong oscillation. The same stimulus interferes with the behaviour of a mouse undergoing a simple task, and yet the mouse can learn to perform accurately despite the noise. Together, these findings suggest a coding regime with high degrees of redundancy and robustness. Although the cortical activity patterns are easily perturbed - even a single spike causes a temporary increase in firing rate - this disturbance does not have debilitating effects on the behaviour or the experience of the animal.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Max-Planck-Institut für Physik komplexer Systeme, 2011. https://tud.qucosa.de/id/qucosa%3A26056.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Sahasrabudhe, Mandar. "Neural network applications in fluid dynamics." Thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-08112002-221615.
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Liere, Diederik Willem van. "Network horizon and the dynamics of network positions a multi-method multi-level longitudinal study of interfirm networks /." [Rotterdam] : Rotterdam : Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Erasmus University [Host], 2007. http://hdl.handle.net/1765/10181.
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Hollingshad, Nicholas W. "A Non-equilibrium Approach to Scale Free Networks." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149609/.
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Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were long thought to be random, but in the late 1990s, Barabási and Albert found that an underlying structure did in fact exist in many natural and technological networks that are now referred to as scale free. Since then, researchers have gained a much deeper understanding of this particular form of complexity, largely by combining graph theory, statistical physics, and advances in computing technology. This dissertation focuses on out-of-equilibrium dynamic processes as they unfold on these complex networks. Diffusion in networks of non-interacting nodes is shown to be temporally complex, while equilibrium is represented by a stable state with Poissonian fluctuations. Scale free networks achieve equilibrium very quickly compared to regular networks, and the most efficient are those with the lowest inverse power law exponent. Temporally complex diffusion also occurs in networks with interacting nodes under a cooperative decision-making model. At a critical value of the cooperation parameter, the most efficient scale free network achieves consensus almost as quickly as the equivalent all-to-all network. This finding suggests that the ubiquity of scale free networks in nature is due to Zipf's principle of least effort. It also suggests that an efficient scale free network structure may be optimal for real networks that require high connectivity but are hampered by high link costs.
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Corradini, Daniele. "Computational study of resting state network dynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14524/.
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Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale.
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Schumacher, Ryan Donald. "Network dynamics and fluctuating architectural typology Flux /." Thesis, Montana State University, 2009. http://etd.lib.montana.edu/etd/2009/schumacher/SchumacherR0509.pdf.
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Located in the northern United States, along the Rocky Mountains, lies the state of Montana. Traditionally rural, Montana is experiencing significant growth in its urban and destination areas. With growth comes obstacles and opportunities. The majority of the state is sufficiently connected to the global transportation network for the movement of goods, but lacks diverse people moving systems. While goods have the benefit of being transported at high speeds via road, rail, and air, the majority of people do not. Roadways near urban areas are frequent victims of congestion, the vitality of many airports is in question, and rail is minimized to a northern Amtrak route that neglects most population centers. The lack of passenger transit systems effectively cuts travel possibilities in half for hundreds of thousands people. Montanans deserve an option for the future that streamlines their transportation infrastructure, integrates them with the rest of the world, and provides an example of positive development. The intent of this thesis is to analyze the current network of people moving systems in Montana in order to determine how a better understanding of network dynamics and transportation architecture can help create connections to the global transportation network and foster positive growth. Information will be presented in graphic and literary form starting with the economic and transportation infrastructure in the region. Precedents are used to gain insight on existing and proposed architectural solutions to facilitate a proposal for an integrated transportation network in Montana, using architecture that utilizes continuous change, passage, and movement as active support.
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Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.
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Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain's performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson's disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theory.
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Cabeza, Mar. "Spatial population dynamics in reserve-network design." Helsinki : University of Helsinki, 2003. http://ethesis.helsinki.fi/julkaisut/mat/ekolo/vk/cabeza/.
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Rossi, Stefano <1993>. "The Dynamics of Network Failure in Italy." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10622.
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La tesi ha l’obiettivo di analizzare le dinamiche di un network tra le differenti aziende che lo compongono. Lo studio é basato sul fatto che le aziende non cercano semplicemente materie prime, risorse umane e finanziarie, ma anche conigli, informazioni ed esperienza. Per questo, l’aiuto di una o più imprese esterne attraverso la formazione di un network può avere un’influenza pesante sul futuro e la competitività dell’azienda stessa.
Partendo da un’analisi quantitativa di diverse imprese che lavorano in territorio italiano, la ricerca prova a esplicare perché e come queste dinamiche interne al network si comportano, e perché la rete può arrivare al fallimento dell’obiettivo iniziale o dell’impresa stessa. Nonostante tutto, le relazione formatesi e il network tra aziende e lavoratori rimane spesso incontrallabile e difficilmente prevedibile. Questa situazione presenta molte difficoltà ai manager, costretti continuamente ad adattarsi e cambiare le proprie strategie, soprattutto in vista di un mercato sempre più dinamico. La tesi approffondirà gli aspetti che causano il fallimento di un network considerando le influenze dell’andamento del mercato, la competitività locale e le sinergie richieste per cooperare tra aziende differenti. Infine, l’analisi avrà l’obiettivo finale di estrarre da questi casi reali un modello generale, che possa essere ri-applicato a seconda di contesti e influenze culturali differenti.
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Kasthurirathna, Dharshana Mahesh. "The influence of topology and information diffusion on networked game dynamics." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14570.
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This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent.
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Onaga, Tomokatsu. "Concurrency-induced transitions in epidemic dynamics on temporal networks." Kyoto University, 2018. http://hdl.handle.net/2433/232228.
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Lee, Changheon. "Dynamics of Advice Network and Knowledge Contribution: A Longitudinal Social Network Analysis." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/243117.
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Online communities have become an increasingly popular channel for social interaction, enabling knowledge and opinion sharing across a board range of topics and contexts. Their viability and sustainability depends largely on contributions from community members in terms of time, resources, and knowledge. However, how individuals' knowledge contribution behavior changes over time and what network structural characteristics influence individuals' contribution behavior is not well understood. This study investigates "co-evolution" of social networks (i.e. advice network) and knowledge contribution behavior thorough a lens of social selection and social influence mechanism. This study are particularly interested in examining the dynamics of the advice network ties and the knowledge contribution behavior in the context of virtual financial communities in which people voluntarily participate to exchanges investing-related information. Unlike popular friendship-based online social networks, virtual financial communities in this study enables members to construct their own advice network by adding, maintaining, or terminating advice ties. Changes in network ties are referred to as social selection, while changes in individuals' behavior in response to the current network position are referred to as social influence. Dynamic network modeling is applied to investigate effects of social selection and influence separately and then examine the interplay between social selection and behavioral influence. Examination of such effects both separately and simultaneously requires a longitudinal data that capture dynamic changes in both the advice ties and the behavior under study.
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Nath, Madhurima. "Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/86841.
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Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the number of samples required for an accurate estimate increases with system size. Instead, an adaptive method using Bernstein polynomials as kernel density estimators proves useful. Network reliability has a wide range of applications ranging from epidemiology to statistical physics, depending on the description of the functionality. For example, it serves as a measure to study the sensitivity of the outbreak of an infectious disease on a network to the structure of the network. It can also be used to identify important dynamics-induced contagion clusters in international food trade networks. Further, it is analogous to the partition function of the Ising model which provides insights to the interpolation between the low and high temperature limits.Ph. D.
The research presented here explores the effects of the structural properties of an interacting system on the outcomes of a diffusive process using Moore-Shannon network reliability. The network reliability is a finite degree polynomial which provides the probability of observing a certain configuration for a diffusive process on networks. Examples of such processes analyzed here are outbreak of an epidemic in a population, spread of an invasive species through international trade of commodities and spread of a perturbation in a physical system with discrete magnetic spins. Network reliability is a novel tool which can be used to compare the efficiency of network models with the observed data, to find important components of the system as well as to estimate the functions of thermodynamic state variables.
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Hilton, Kristina B. "Dynamics of Multicultural Social Networks." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6711.
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Historically human endeavors around the globe are in search of bilateral relationships. Knowledge and commerce has played a very significant role in increasing the ability for humans to connect for the betterment of the human species. As the means of communication improve, mutual benefits to the community as a whole also increase. Moreover, the benefits are filtered down to members of the overall community. Recent advancement in electronic communication technologies and in knowledge, in particular, physical, chemical, engineering and medical sciences and philosophies, have facilitated nearly instantaneous multi-cultural interactions. Local problems and solutions have become global. This has generated a need for cooperation, coordination, and co-management at local and global levels. This instant communication and easy access to almost all members of the global community, with minimal cost and effort, causes an increase in uncertainty and lack of clarity in communication and misunderstanding between the members of the community. This leads to a fuzzy and stochastic environment. In short, the 21st century has seen a significant increase in the need to consider all human endeavors as being subject to random environmental fluctuations.
More precisely, currently the human species is highly mobile. In this work, an attempt is made (1) to balance communities working cooperatively and cohesively with one another while preserving member ability to retain individuality and fostering an environment of cultural state diversity. We develop (2) constructive analytic algorithms that provide tools to study qualitative and quantitative properties of multicultural diverse dynamic social networks. Under network parametric space/set conditions (3) cohesion and co-existence of members of multicultural dynamic network are insured. The parametric conditions (4) are algebraically simple, easy to verify, and robust. Moreover, the presented study of parametric representations of cohesion, co-existence and consensus attributes and robustness of multicultural dynamic networks provides a quantitative tool for planning, policy and performance of human mobility processes for members of a multicultural dynamic network.
We develop and investigate (5) a deterministic dynamic multicultural network. To exhibit the significance of the analysis, ideas, the complexity and limitations, we present a specific prototype model. This serves to establish the framework for finding explicit sufficient conditions in terms of system parameters for studying a complex dynamic network. Further, we decompose the cultural state domain into invariant subsets (6) and consider the behavior of members within each cultural state subset. Moreover, we analyze the relative cultural affinity between individual members relative to the center of the social network. We then (7) outline the general method for investigating a multicultural cultural network. We also demonstrate the degree of conservatism of the estimates using Euler type numerical approximation schemes. We are then able to consider how changes in the various parametric effects are reflected on the dynamics of the network.
The deterministic multicultural dynamic model and analysis is extended (8) to a multicultural dynamic network under random environmental perturbations. We present a detailed prototype model for the purpose of investigation. Introducing the concept of stochastic cohesion and consensus in the context of probabilistic modes of convergence (9), the explicit sufficient conditions in terms of system parameters are given to exhibit the cohesive property of the stochastic network. The effects of random fluctuations are characterized.
We then extend the stochastic model (10) to a multicultural hybrid stochastic dynamic network model. By considering a hybrid dynamic, we can explore the properties of a multicultural dynamic under the influence of both continuous-time and discrete-time cultural dynamic systems. This model captures external influences and internal changes that may have an impact on the members and system parameters of the dynamic network. We extend the ideas of global cohesion and consensus to local cohesion and consensus (11). Again, the detailed study is focused on a prototype hybrid multicultural dynamic network. Using the ideas and tools developed from the stochastic network (12), we are able to establish conditions on the network parameters for which the cultural network is locally cohesive. Using Euler-Maruyama type numerical approximation schemes to model the network, we better understand to what extent the analytically developed estimates are feasible.
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Leung, Chi-chung. "Modelling complex network dynamics a statistical physics approach /." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38324611.
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Yokoyama, Keiko, and Yuji Yamamoto. "Common and Unique Network Dynamics in Football Games." Public Library of Science, 2011. http://hdl.handle.net/2237/15856.
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Li, Zhixiong. "The dynamics of export channels : a network approach." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296887.
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Spencer, Matthew. "Evolving complex network models of functional connectivity dynamics." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590143.
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Functional connectivity networks describe how regions of the brain interact. The timing, location, and frequency of these interactions inform about memory, decision making, motor movement, affective states, and more. However, while these interactions are well described as networks, these networks, like many others throughout nature, are constantly changing. Complex network evolution poses a highly dimensional problem but also contains much information about the system in question. In this thesis, a recent class of evolving complex network models was explored and extended to capture the functional connectivity dynamics observed in neuronal networks. Functional connectivity was investigated through data- and model-driven techniques at the cellular level, with cultures of cortical neurones on multi-electrode arrays, and at the whole-brain level, with electroencephalography. At the neuronal level, complex spatial dependencies were identified in bursts of excitation and two novel network models, the Starburst model and the Excitation Flow model, are used to capture the resulting functional connectivity. At the whole-brain level, functional connectivity dynamics were used to perform single-trial classification of intentional motor movement. Again, spatiotemporal dependencies were identified and used to present three novel techniques for modelling the network dynamics. The first two techniques decomposed networks into network templates (one model-based and one spectral-based) and modelled the dynamics with hidden Markov models. The final technique was a generalised evolving version of the Starburst model. The hidden Markov model of spectrally decomposed networks was shown to classify motor intentions with an accuracy around 80%. Firstly, this thesis shows that time plays an important role in the production of the complex network topologies observed in functional connectivity, both at the cellular and whole-brain leve1. Further, it is shown that evolving complex network models are very useful tools for modelling these topologies and that the network dynamics can be used to uncover features that are crucial to identifying functional states.
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Leung, Chi-chung, and 梁志聰. "Modelling complex network dynamics: a statistical physics approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38324611.
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Toyoglu, Hunkar. "A system dynamics based multi user network game." Thesis, Monterey California. Naval Postgraduate School, 1999. http://hdl.handle.net/10945/13566.
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We developed a multi-user computer network simulation game model as a decision support tool in a manufacturing and distribution system. The model, written in Powersim(registered) software package, based on system dynamics theories. The game is a "dynamic business environment" in which the outcome is determined by interactions within and between the players in the framework of the industrial system. This game can accommodate simultaneous play by a maximum of seven players. Management's job in the game is to employ its company's resources and to manage its operations in such a way as to minimize the inventory fluctuations and costs. The purpose of this decision support tool is to provide hypothetical business scenarios in which players-managers-can practice decision-making processes in their companies. The simulation game, built in this thesis, can Support planning, decision-making, and policy-setting processes by analyzing the effect of changes in the operations and resources that impact inventory level and cost and by providing a means to test and resent the proposed policies under different scenarios.
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Stella, Massimo. "Network structure and dynamics of empirical multiplex systems." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/419488/.
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Interactions are important, since they can influence and shape a variety of real-world networked systems. Interactions can have a multi-relational nature, i.e. being of different type. Different layers of interactions can look very different from each other, motivating the need for developing multi-layer network models and metrics in network science. This thesis aims at developing novel multiplex frameworks for the quantitative investigation of two real-world systems: (i) the web of relationships between words in the human mind and (ii) the ecological interactions among animals in ecosystems. Despite being different in nature, both the mental lexicon of words and ecosystems can be represented as a multiplex network, where nodes represent distinct entities (e.g. words or animal groups) interacting on different layers in different ways (e.g. words being semantically and/or phonologically similar; animal species eating or parasitising each other). In both the considered systems, interactions crucially determine function and dynamics of a variety of processes. In the mental lexicon, individual interactions have been shown to influence both language acquisition and usage. In Part I of this thesis, I show that the structure of the phonological layer reflects constraints related to language use. I proceed by introducing the framework of multiplex lexical networks for quantifying, for the first time, the influence that phonology and semantics combined can have on (i) word acquisition of toddlers and (ii) word processing of adults. Results highlight phenomena that are not observable in single-layer networks. In toddlers word learning strategies based on the whole multiplex structure match empirical word learning significantly better than strategies based on individual layers, indicating that multiplexity is important for early word acquisition. At later ages the multiplex structure evolves by displaying an early, explosive emergence of a multiplex network core of words, which facilitates mental navigation and increases robustness against cognitive impairments. The second part of this thesis focuses on ecosystems, where interactions encapsulated in food webs or host-parasite networks greatly influence species extinction. In Part II of this thesis, I introduce the framework of ecological multiplex or “ecomultiplex” networks for combining predator-prey and host-parasite contact interactions as two layers of a network representing trophic links in a given ecosystem. I show that host-parasite interactions can dramatically increase the susceptibility of ecosystems to a parasite pandemic compared to models based on single-layer trophic networks only. Results of the ecomultiplex model are tested against empirical findings from field work in Brazilian ecosystems, finding agreement between my theoretical results and empirical data. Furthermore, by considering the multi-relational nature of trophic interactions, I quantitatively show that generalist top predators might accelerate parasite spread rather than hampering it, thus providing a theoretical explanation to recent empirical findings. Both in the mental lexicon and ecosystems, multiplexity influences structure, dynamics and function in ways not yet accounted for in the literature. This thesis aims to fill this gap by suggesting multiplex frameworks suitable for quantitative testing of empirical conjectures, while opening new modelling challenges at the interface of physics, network science and other disciplines.
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Balaam, Andy. "Exploring developmental dynamics in evolved neural network controllers." Thesis, University of Sussex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426199.
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Hui, Zi. "Spatial structure of complex network and diffusion dynamics." Thesis, Le Mans, 2013. http://www.theses.fr/2013LEMA1005/document.
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Dans le développement récent des sciences de réseau, réseaux contraints spatiales sont devenues un objet d'une enquête approfondie. Spatiales des réseaux de contraintes sont intégrées dans l'espace de configuration. Leurs structures et les dynamiques sont influencées par la distance spatiale. Ceci est prouvé par les données empiriques de plus en plus sur des systèmes réels montrant des lois exponentielles ou de distribution d'énergie distance spatiale de liens. Dans cette thèse, nous nous concentrons sur la structure de réseau spatial avec une distribution en loi de puissance spatiale. Plusieurs mécanismes de formation de la structure et de la dynamique de diffusion sur ces réseaux sont pris en considération. D'abord, nous proposons un réseau évolutif construit en l'espace de configuration d'un mécanisme de concurrence entre le degré et les préférences de distance spatiale. Ce mécanisme est décrit par un a^'fc- + (1 — a)^'lL_,1, où ki est le degré du noeud i et rni est la distance spatiale entre les noeuds n et i. En réglant le paramètre a, le réseau peut être fait pour changer en continu à partir du réseau spatiale entraînée (a = 0) pour le réseau sans échelle (a = 1). La structure topologique de notre modèle est comparé aux données empiriques de réseau de courrier électronique avec un bon accord. Sur cette base, nous nous concentrons sur la dynamique de diffusion sur le réseau axé sur spatiale (a — 0). Le premier modèle, nous avons utilisé est fréquemment employée dans l'étude de la propagation de l'épidémie: ['spatiale susceptible-infecté-susceptible (SIS) modèle. Ici, le taux de propagation entre deux noeuds connectés est inversement proportionnelle à leur distance spatiale. Le résultat montre que la diffusion efficace de temps augmente avec l'augmentation de a. L'existence d'seuil épidémique générique est observée, dont la valeur dépend du paramètre a Le seuil épidémique maximum et le ratio minimum fixe de noeuds infectés localiser simultanément dans le intervalle 1.5 < a < 2.Puisque le réseau spatiale axée a bien défini la distance spatiale, ce modèle offre une occasion d'étudier la dynamique de diffusion en utilisant les techniques habituelles de la mécanique statistique. Tout d'abord, compte tenu du fait que la diffusion est anormale en général en raison de l'importante long plage de propagation, nous introduisons un coefficient de diffusion composite qui est la somme de la diffusion d'habitude constante D des lois de la Fick appliqué sur différentes distances de transfert possibles sur le réseau. Comme prévu, ce coefficient composite diminue avec l'augmentation de a. et est une bonne mesure de l'efficacité de la diffusion. Notre seconde approche pour cette diffusion anormale est de calculer le déplacement quadratique moyen (l²) à identifier une constante de diffusion D' et le degré de la anomalousness y avec l'aide de la loi de puissance (l²) = 4D'ty. D' comportements de la même manière que D, i.e.. elle diminue avec l'augmentation de a. y est inférieur à l'unité (subdiffusion) et tend à un (diffusion normale) que a augmenteIn the recent development of network sciences, spatial constrained networks have become an object of extensive investigation. Spatial constrained networks are embedded in configuration space. Their structures and dynamics are influenced by spatial distance. This is proved by more and more empirical data on real Systems showing exponential or power laws spatial distance distribution of links. In this dissertation, we focus on the structure of spatial network with power law spatial distribution. Several mechanisms of structure formation and diffusion dynamics on these networks are considered. First we propose an evolutionary network constructed in the configuration space with a competing mechanism between the degree and the spatial distance preferences. This mechanism is described by a ki + (1 — a), where ki is the degree of node i and rni is the spatial distance between nodes n and i. By adjusting parameter a, the network can be made to change continuously from the spatial driven network (a = 0) to the scale-free network (a = 1). The topological structure of our model is compared to the empirical data from email network with good agreement. On this basis, we focus on the diffusion dynamics on spatial driven network (a = 0). The first model we used is frequently employed in the study of epidemie spreading : the spatial susceptible-infected-susceptible (SIS) model. Here the spreading rate between two connected nodes is inversely proportional to their spatial distance. The result shows that the effective spreading time increases with increasing a. The existence of generic epidemic threshold is observed, whose value dépends on parameter a. The maximum épidemic threshold and the minimum stationary ratio of infected nodes simultaneously locate in the interval 1.5 < a < 2. Since the spatial driven network has well defined spatial distance, this model offers an occasion to study the diffusion dynamics by using the usual techniques of statistical mechanics. First, considering the fact that the diffusion is anomalous in general due to the important long-range spreading, we introduce a composite diffusion coefficient which is the sum of the usual diffusion constant D of the Fick's laws applied over different possible transfer distances on the network. As expected, this composite coefficient decreases with increasing a and is a good measure of the efficiency of the diffusion. Our second approach to this anomalous diffusion is to calculate the mean square displacement (l²) to identify a diffusion constant D' and the degree of thé anomalousness y with the help of the power law {l²} = 4D'ty. D' behaviors in the same way as D, i.e., it decreases with increasing a. y is smaller than unity (subdiffusion) and tends to one (normal diffusion) as a increases
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Karlsson, Mattias P. "Network dynamics in the hippocampus during spatial learning." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3324622.
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Amaducci, Matteo. "Design of Boolean network robots for dynamics tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3444/.
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Borge, Holthoefer Javier. "Semantic networks and cognitive dynamics." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/31937.
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Seguint una concepció clàssica de la Intel•ligència Artificial (aquella que es posava com horitzó una definició dels mecanismes cognitius i la seva implementació en computadors), aquesta tesi s'endinsa en el problema de l'organització del coneixement. En especial, es posa atenció a la memòria semàntica i el coneixement lingüístic, intentant esbrinar de quina forma emergeixen les relacions semàntiques entre paraules. Per assolir aquests objectius es recorre a tres fonts principals: la utilització de dades empíriques provinents de la psicolingüística i la neuropsicologia; l'ús de la metodologia de sistemes complexes (física estadística) per la construcció de models i simulació de dinàmiques; i finalment l'aprofitament de les tecnologies al nostre abast tant per l'obtenció de noves dades (Internet) com una capacitat d'emmagatzemament suficient i velocitat de processament per al tractament de dades massives.
D'aquest punt de vista arrelat en la Ciència Cognitiva en poden sorgir aplicacions fortament vinculades a problemes vigents en l'àmbit de Ciències de la Computació, com són l'extracció d'informació no supervisada, l'enriquiment de bases de dades i recursos lingüístics electrònics (Wikipedia, WordNet, etc.) i la millora de sistemes de consulta (query-based systems).
Al Capítol 2 s'estableixen les bases metodològiques que han servit per construir la resta del treball.
El Capítol 3 es dedica a aclarir (i) quina mena de dades s'han emprat (i s'empren) en l'estudi a gran escala del llenguatge i els fenòmens cognitius que l'envolten; i (ii) es revisen els treballs més destacables que s'han fet fins al moment actual al voltant del llenguatge i la cognició.
Al Capítol 4 s'introdueix el Random Inheritance Model, que representa un intent per comprendre com emergeixen la similitud semàntica entre paraules i les categories semàntiques. Els resultats es comparen amb dades empíriques basades en les respostes de subjectes humans.
Al Capítol 5 presentem un model de degradació semàntica que emula processos neurodegeneratius i prediu acuradament, a nivell qualitatiu, les observacions experimentals amb malalts d'Alzheimer que s'han fet en l'àmbit de la neuropsicologia. En aquests processos degeneratius convergeixen interessos multidisciplinars, que van de la mateixa cognició al fenomen de percolació en física estadística.
El Capítol 6 queda finalment dedicat a una reflexió global d'aquesta memòria.Following a classical conception of Artificial Intelligence (one that aims a definition of cognitive mechanisms and their implementation in computers), this thesis explores the problem of knowledge organization. In particular, it draws attention to the linguistic and semantic memory, trying to find out how semantic relations emerge between words. To achieve these objectives, we rely on three main sources: use of empirical data from psycholinguistics and neuropsychology; the use of complex systems (statistical physics) methodology to build and simulate dynamic models; and finally the utilization of technologies at our disposal both for obtaining new data (Internet) as well as sufficient storage capacity and processing speed for massive data manipulation. From this point of view, rooted in Cognitive Science, many applications may arise, some of them strongly linked to current problems in the field of Computer Science, such as unsupervised information extraction, enrichment of databases and language electronic resources (Wikipedia, WordNet, etc.). and improve consultation systems (query-based systems). In Chapter 2 the methodologies that have helped build the rest of the work are established. Chapter 3 is devoted to clarify (i) the kind of data that have been used in the large-scale study of language and cognitive phenomena around it, and (ii) review some of the major contributions to the date about language and cognition. In Chapter 4 the Random Inheritance Model is introduced, which represents an attempt to understand how does semantic similarity between words and semantic categories emerge. Results are compared with empirical data obtained from responses with human subjects. In Chapter 5 we present a model of semantic degradation which emulates neurodegenerative processes, and predicts experimental observations from Alzheimer's Disease patients in the field of neuropsychology. In the study of such degenerative processes different multidisciplinary interests converge, ranging from cognition itself to percolation theory in statistical physics. Chapter 6 is finally devoted to a global reflection of this memory.
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BELLENZIER, LUCIA. "Dynamics in Financial Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/49111.
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The aim of this work is to examine the dynamics in financial networks.
We propose two cases of study: the network of world's stock exchanges and the network of Interlocking Directorates in Italy.
In the first case we study the dynamics on the network and in the second one the dynamics
of the network, i.e. in the first case the network
topology does not change and we study the dynamic information
that passes through the structure, instead, in the second case,
we study how the network topology evolves over time.
In 'Prices-Quakes Shaking the World's Stock Exchanges' (2011) Vitting Andersen et al. propose a model
of the World's Stock Exchanges that predicts how an individual stock exchange should be priced in terms of
performance of the global exchange market. In the present work this model is adopted to describe the financial network.
Understanding how disruption can propagate across financial markets is indeed of the utmost importance,
hence our aim is to study the dynamics of the World's Stock Exchange network,
inter alia we are interested in the study of the avalanches of price, disturbances propagating in the world financial network of stock exchanges.
In fact the model has a direct correspondence to models of earth tectonic plate movements developed
in physics. In tectonic plate movement stresses are slowly build up over centuries only to be released in a quick snap,
lasting from seconds to at most some few minutes, that we feel as an earth quake.
The main idea is to describe a similar slow build-up of 'stresses' in the world's financial
network of stock exchanges where stresses can be thought of arising from for example
business cycles in the real economy. Just like earthquakes such a slow build up of 'stress' is
then followed by a quick release in terms of domino effects where the major part of the
world's stock exchanges resonate with big up or down price movements.
The main innovative part of the model that we will introduce is therefore a separation of
time scales just as seen in earthquakes.
We first replicate the results of the model using empirical data using as source Bloomberg.
We then verify that the price dynamics can indeed be described in terms of avalanche dynamics,
and we find that such dynamics show power law behavior in agreement with what is found in other
Self Organized Critical (SOC) models.
We extend such studies and give a quantitative as well as qualitative
description of the details in the dynamics of the propagation of "price-quakes" (avalanches).
The second case is the Interlocking Directorates in Italy, i.e. the situation that occurs when a person affiliated with one
company sits on the board of directors of another organization, analyzed by using network theory.
We first analyzed the Italian case in order to investigate the presence of a persistent core. Applying the same methodology
used by Milakovic et al., from 1998 to 2010 we found quite different results:
the persistent sub-graphs are not connected and so we could not find a core. Instead in the German stock exchange Milakovic et al.
have found a small core of directors densely connected among themselves.
In order to capture the persistent structure of the Interlocks Network we
propose a different approach that allows us to assess the stability of links between companies in Italy.
We describe the dynamic board networks by means of a static graph in which an edge is
related with the persistence over time of an interlock between two companies.
The results lead to affirm that in the Italian board network a set of stable links is observable,
nevertheless a presence of a large turnover between the directors. There are strong ties among firms in the overall period.
Most of them are ties due to the ownership of family firms: Berlusconi, Benetton, Agnelli, Caltagirone, De Benedetti.
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Blood, Ellery A. "From Static to Dynamic Electric Power Network State Estimation: The Role of Bus Component Dynamics." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/57.
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This thesis addresses the challenge of accurately and robustly estimating the network state on an electric power network despite its large size, infrequent measurement updates, and high likelihood of corrupted data. This is especially important as electrical transmission operators are increasingly being asked to operate the networks at their maximum allowable capacity. Accurate knowledge of the state is necessary to ensure adequate margin to these operating limits should a fault occur.
This thesis provides the following contributions. 1. Models describing the dynamics of slow machinery attached to and coupled via the electric power network were used to allow dynamic state estimation. 2. The detail of the coupled dynamic network model was evaluated to determine the level of modeling complexity required to achieve significant state estimation performance gains. 3. Improvements to bad data detection and identification by using information from the dynamic state estimator were demonstrated and evaluated. 4. The improvements to network static observability were discussed and evaluated.
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Choo, Kiam. "Learning hyperparameters for neural network models using Hamiltonian dynamics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0008/MQ53385.pdf.
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Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.
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Braack, Alexandrina [Verfasser]. "Modelling and Analysis of Financial Network Dynamics / Alexandrina Braack." Kiel : Universitätsbibliothek Kiel, 2017. http://d-nb.info/1131629280/34.
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Darby, Frances. "Managing child health : the network dynamics of social exchange." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437626.
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NUNES, VIVIAN DE ARAUJO DORNELAS. "EFFECTS OF CONTACT NETWORK RANDOMNESS ON MULTIPLE OPINION DYNAMICS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30466@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
PROGRAMA DE EXCELENCIA ACADEMICA
Muitas vezes enfrentamos o desafio de escolher entre diferentes opções com atratividade semelhante como, por exemplo, na escolha de um candidato parlamentar, na escolha de um filme ou ao comprar um produto no supermercado. A fim de estudar a distribuição das preferências em tais situações, podemos considerar dinâmicas de opinião (com diversas opções possíveis, contemplando também os casos em que há indecisão) em redes. Neste trabalho, utilizamos duas dinâmicas distintas: uma envolvendo o contágio direto de cada sítio para a sua vizinhança (regra A) e a outra onde a opinião de cada sítio é definida pela maioria relativa local (regra B). A topologia da rede de contatos pode ter um efeito importante sobre a distribuição final de opiniões. Utilizamos as redes de Watts-Strogatz e, em particular, estamos interessados em investigar a contribuição da aleatoriedade p da rede no resultado final das dinâmicas. Dependendo das propriedades estruturais da rede e das condições iniciais, podemos ter diferentes resultados finais: equipartição de preferências, consenso e situações onde a indecisão é relevante. O papel da aleatoriedade da rede é não trivial: para um número pequeno de opiniões, as regras A e B (esta última com atualização síncrona) apresentam um valor ótimo de p, onde o predomínio da opinião vencedora é máximo. Já para a regra da pluralidade com atualização assíncrona, o aumento do número de atalhos pode até mesmo promover situações de consenso. Além disso, as duas dinâmicas (e seus diferentes modos de atualização) coincidem para baixa desordem da rede, mas diferem para graus de desordem maiores. Observaremos também que a quantidade de iniciadores diminui a fração da opinião vencedora para todas as dinâmicas e atenua o máximo local que aparece na região de mundo pequeno.
People often face the challenge of choosing amongst different options with similar attractiveness, such as when choosing a parliamentary candidate, a movie or buying a product in the supermarket. In order to study the distribution of preferences in such situations, we can consider opinion dynamics (where different options are available as well as the undecided state) in network. In this work, we use two different opinion dynamics: one involving the direct contagion from each site to its neighborhood (rule A) and another where the opinion of each site is defined by the local relative majority (rule B). The contact network topology can have a important effect in the final distribution of opinions. We use the Watts-Strogatz network and, in particular, we are interested in investigating the contribution of the network randomness p in the output of the dynamics. Depending on the structural properties of the network and the initial conditions, the final distribution can be: equipartition of preferences, consensus and situations where indecision is relevant. The role of network randomness is nontrivial: for a small number of opinions, the rules A and B (the latter with synchronous update) present an optimum value of p, where the predominance of a winning opinion is maximal. Moreover, for the plurality rule with asynchronous update, the increase of the number of shortcuts can even promote consensus situations. Furthermore, both dynamics coincide for small disorder of the network, but differ for larger disorder. Also we observe that the number of initiators decreases the value of the winning fraction in all types of dynamics and attenuates the local maximum that appears in the small-world region.
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Perkins, Matthew. "Differential dynamics of network states| implications for task switching." Thesis, Icahn School of Medicine at Mount Sinai, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10099545.
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A change in a stimulus response relationship implies that there has been a change in the internal state of the relevant behavior-generating network. Frequently, network states are persistent, biasing responses for some time following stimulus exposure. This benefits subsequent behavioral performance when the same stimulus is re-encountered. Alternatively, it can also negatively impact initiation of a second (distinct) task, i.e. there can be a task-switch cost. Recently, work from a few invertebrate model systems has begun to determine how experience dependent network states are mediated on a cellular/molecular level. A fundamental question I have addressed is, does the establishment of one network-state remove a prior state, or can two network states overlap and interact? In this thesis I provide data that indicate that in the feeding circuit of Aplysia, network states that promote incompatible behaviors can indeed overlap. In addition, I describe a novel role for a cyclic nucleotide gated ion-current, as supporting an experience dependent network state through a persistent modulation of cell excitability.
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Connor, Dustin Thomas. "A computational investigation of neural dynamics and network structure." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/8983.
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With the overall goal of illuminating the relationship between neural dynamics and neural network structure, this thesis presents a) a computer model of a network infrastructure capable of global broadcast and competition, and b) a study of various convergence properties of spike-timing dependent plasticity (STDP) in a recurrent neural network. The first part of the thesis explores the parameter space of a possible Global Neuronal Workspace (GNW) realised in a novel computational network model using stochastic connectivity. The structure of this model is analysed in light of the characteristic dynamics of a GNW: broadcast, reverberation, and competition. It is found even with careful consideration of the balance between excitation and inhibition, the structural choices do not allow agreement with the GNW dynamics, and the implications of this are addressed. An additional level of competition – access competition – is added, discussed, and found to be more conducive to winner-takes-all competition. The second part of the thesis investigates the formation of synaptic structure due to neural and synaptic dynamics. From previous theoretical and modelling work, it is predicted that homogeneous stimulation in a recurrent neural network with STDP will create a self-stabilising equilibrium amongst synaptic weights, while heterogeneous stimulation will induce structured synaptic changes. A new factor in modulating the synaptic weight equilibrium is suggested from the experimental evidence presented: anti-correlation due to inhibitory neurons. It is observed that the synaptic equilibrium creates competition amongst synapses, and those specifically stimulated during heterogeneous stimulation win out. Further investigation is carried out in order to assess the effect that more complex STDP rules would have on synaptic dynamics, varying parameters of a trace STDP model. There is little qualitative effect on synaptic dynamics under low frequency (< 25Hz) conditions, justifying the use of simple STDP until further experimental or theoretical evidence suggests otherwise.
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D'ERRICO, MARCO. "A network approach for opinion dynamics and price formation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/49777.
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"If men define situations as real, they are real in their consequences". W.I. Thomas and D.S. Thomas
In this work, we investigate the intertwined role of network interaction, opinion dynamics and price formation in a financial system. We propose a dynamical multi - agent framework where the interaction network and its topology, opinions and prices depend on one another, co - evolving in time. At first, we introduce some useful concepts in network theory and opinion dynamics. A method for classifying agents according to their topological role in the network is proposed. Second, we build on the existing literature on hetereogenous beliefs and evolutionary systems and provide a model with a specific update rule that leads to an evolving topology. The model is apt at describing social and behavioural phenomena that have recently received particular attention in the financial literature, such as hetereogeneous beliefs on market scenarios and the effects of the topology of interactions. We illustrate such dynamics via simulations, discussing the stylized facts that the model might be able to capture and we will discuss the use of social network data in order to calibrate the model. Third, we propose a model for formation of relative prices in a closed economy when agents have limited attention about a certain asset/sector.
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Halnes, Geir. "Biological network modelling : relating structure and dynamics to function in food webs and neural networks /." Uppsala : Dept. of Biometry and Engineering, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/2007113.pdf.
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Hori, Yukie. "Social networks in the network society : new dynamics of networking among women's organizations in Asia." Thesis, London School of Economics and Political Science (University of London), 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511798.
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This thesis focuses on the electronic network and its transformation in relation to women's organizations and their social context. Despite the fact that an expansion of electronic networking among women's organizations has been occurring for the last decade, there is little evidence to indicate how in practice information and communication technologies (ICTs) may be contributing to the advancement of women. The thesis investigates this issue by examining the transformation in a women's electronic network. It asks whether, how and to what extent the use of the computer-mediated communication (CMC) has transformed the goals, activities and members of the electronic network of women's organizations. The study investigates the potential that new ICTs offer to women who would otherwise be excluded from aspects of decision-making and global governance processes. The main focus of the theoretical development is derived from social constructivism. However, in view of the absence of a well-established theoretical framework that embraces ICTs, gender and development, the conceptual framework for this thesis is supplemented by several theoretical constructs. These are theoretical reflections on a technology-context framework as proposed by Houston and Jackson, a communication perspective drawn from collective action theory, and a perspective on gender and technology derived from Wajcman's technofeminism construct. A case study of an electronic network of women's organizations called the Asian Women's Resource Exchange serves as the focus for the empirical research which is principally based on a qualitative interview-based method. The results of the study indicate that the transformation of a women's electronic network is not a straightforward process and does not necessarily generate the expected results. Rather, the transformations that occur are the result of complex interactions between technology and the social context whereby women change their practices and norms by working collectively through the electronic network which, in turn, leads to often unexpected changes in their activities and their membership. Highlighting the electronic network as defined in this thesis as an agent of change yields insight into the dynamic nature of the network and offers a more comprehensive understanding of the reciprocal interactions between actors and their activities that are enabled by CMC.
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Ojha, Hem Raj. "Link Dynamics in Student Collaboration Networks using Schema Based Structured Network Models on Canvas LMS." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596154905454069.
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