The Relevance of Network Models within the Juridic Discourse. Empirical, Sociological, and Epistemological Perspectives

2009 ◽  
Vol 10 (4) ◽  
pp. 383-394 ◽  
Author(s):  
Ino Augsberg
Keyword(s):  
Network Model ◽  
Career Planning ◽  
Cultural Theory ◽  
Network Models ◽  
International Terrorism ◽  
Wide Sense ◽  
Bull Market ◽  
Techno Music ◽  
Epistemological Perspectives

A spectre is haunting contemporary theoretical debates: the spectre of Network. Whether it is about techno-music or football, whether the focus is on the architecture of poststructuralist thinking, international terrorism or individual career-planning, there is one common feature of all debates: the network model. There seems to be hardly a cultural theory – in a wide sense – still able to go on operating without using (at the very least) its heuristic effects. Meanwhile, even the entertainment and the advertising industries have discovered the concept. We need not wonder, then, that it isbull marketfor the model on the specific juridic field too.

scholarly journals Likelihood-based approach to discriminate mixtures of network models that vary in time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi A. Arnold ◽  
Raul J. Mondragón ◽  
Richard G. Clegg
Keyword(s):  
Network Model ◽  
Change Point ◽  
Network Science ◽  
Preferential Attachment ◽  
Network Models ◽  
Explanatory Models ◽  
Growth Mechanisms ◽  
Artificial Data ◽  
Network Growth ◽  
Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

Can Network Models Represent Personality Structure and Processes Better than Trait Models Do?

2012 ◽  
Vol 26 (4) ◽  
pp. 444-445 ◽  
Author(s):  
Tobias Rothmund ◽  
Anna Baumert ◽  
Manfred Schmitt
Keyword(s):  
Network Model ◽  
Explanatory Power ◽  
Network Models ◽  
Personality Structure ◽  
Psychological Processes ◽  
Target Article ◽  
Flexible Framework ◽  
Trait Model ◽  
Better Than

We argue that replacing the trait model with the network model proposed in the target article would be immature for three reasons. (i) If properly specified and grounded in substantive theories, the classic state–trait model provides a flexible framework for the description and explanation of person × situation transactions. (ii) Without additional substantive theories, the network model cannot guide the identification of personality components. (iii) Without assumptions about psychological processes that account for causal links among personality components, the concept of equilibrium has merely descriptive value and lacks explanatory power. Copyright © 2012 John Wiley & Sons, Ltd.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals Emerging, crystalizing, and changing psychological contracts over time: Introducing the iPC-network model

2019 ◽  
Author(s):  
Tim Vantilborgh
Keyword(s):  
Network Model ◽  
Psychological Contract ◽  
Dynamic Network ◽  
Network Models ◽  
Psychological Contracts ◽  
Exchange Relationships ◽  
Contract Breach ◽  
Alternative Approach ◽  
The Individual ◽  
Over Time

This chapter introduces the individual Psychological Contract (iPC) network model as an alternative approach to study psychological contracts. This model departs from the basic idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network, in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, I propose that these networks evolve over time towards attractor states, both at the level of the network structure and at the level of the nodes (i.e., breach and fulfilment attractor states). I highlight how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explain how it may advance scholars understanding of exchange relationships. In particular, I illustrate how iPC-network models allow researchers to study the actual exchanges in the psychological contract over time, while acknowledging its idiosyncratic nature. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and explains how content and process of the psychological contract continuously influence each other.

scholarly journals A configurable model of the synaptic proteome reveals the molecular mechanisms of disease co-morbidity

2020 ◽  
Author(s):  
Oksana Sorokina ◽  
Colin Mclean ◽  
Mike DR Croning ◽  
Katharina F Heil ◽  
Emilia Wysocka ◽  
...  
Keyword(s):  
Network Model ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Network Models ◽  
Disease Association ◽  
Synaptic Proteins ◽  
Protein Interaction Data ◽  
Co Morbidity ◽  
Genes Encoding ◽  
Protein Components

AbstractSynapses contain highly complex proteomes which control synaptic transmission, cognition and behaviour. Genes encoding synaptic proteins are associated with neuronal disorders many of which show clinical co-morbidity. Our hypothesis is that there is mechanistic overlap that is emergent from the network properties of the molecular complex. To test this requires a detailed and comprehensive molecular network model.We integrated 57 published synaptic proteomic datasets obtained between 2000 and 2019 that describe over 7000 proteins. The complexity of the postsynaptic proteome is reaching an asymptote with a core set of ~3000 proteins, with less data on the presynaptic terminal, where each new study reveals new components in its landscape. To complete the network, we added direct protein-protein interaction data and functional metadata including disease association.The resulting amalgamated molecular interaction network model is embedded into a SQLite database. The database is highly flexible allowing the widest range of queries to derive custom network models based on meta-data including species, disease association, synaptic compartment, brain region, and method of extraction.This network model enables us to perform in-depth analyses that dissect molecular pathways of multiple diseases revealing shared and unique protein components. We can clearly identify common and unique molecular profiles for co-morbid neurological disorders such as Schizophrenia and Bipolar Disorder and even disease comorbidities which span biological systems such as the intersection of Alzheimer’s Disease with Hypertension.

scholarly journals Network Models of Post-traumatic Stress Disorder: A Meta-analysis

2020 ◽  
Author(s):  
Adela-Maria Isvoranu ◽  
Sacha Epskamp ◽  
Mike W.-L. Cheung
Keyword(s):  
Network Model ◽  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Stress Disorder ◽  
Ptsd Symptom ◽  
Network Models ◽  
Post Traumatic Stress ◽  
Study Heterogeneity ◽  
Post Traumatic ◽  
Network Studies

Post-traumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account, and applied this framework to the first-ever meta-analytic study of network models. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561, and estimated a single pooled network model underlying the datasets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (1) While several clear symptom-links and interpretable clusters can be identified in the network, most symptoms feature very similar levels of centrality. To this end, aiming to identify central symptoms in PTSD symptom networks may not be fruitful. (2) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (3) Nonetheless, we found evidence that networks estimated from single studies may give rise to generalizable results, as our results aligned with previous descriptive analyses of reported network studies, and network models estimated from single samples lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics.

scholarly journals Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Dongge Cui ◽  
Chuanqu Zhu ◽  
Qingfeng Li ◽  
Qiyun Huang ◽  
Qi Luo
Keyword(s):  
Network Model ◽  
Network Models ◽  
Particle Swarm Algorithm ◽  
Neural Network Models ◽  
Foundation Pit ◽  
Bp Network ◽  
Deformation Prediction ◽  
Application Range ◽  
Limited Applicability ◽  
Bp Model

Deformation prediction is significant to the safety of foundation pits. Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. Combining a small amount of measured data during the excavation of a bottomless foundation pit in a Changsha subway station, the calculations based on the PSO-GM model, the PSO-BP network model, and the PSO-GM-BP model compared. The results show that both the GM (1, 1) and BP neural network models can predict accurate results. The prediction optimized by the particle swarm algorithm is more accurate and has more substantial applicability. Due to its reliable accuracy and wide application range, the PSO-GM-BP model can effectively guide the construction of foundation pits, and it also has certain reference significance for other engineering applications.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

scholarly journals Impact analysis of two kinds of failure strategies in Beijing road transportation network

2018 ◽  
Vol 29 (01) ◽  
pp. 1850005
Author(s):  
Zundong Zhang ◽  
Xiaoyang Xu ◽  
Zhaoran Zhang ◽  
Huijuan Zhou
Keyword(s):  
Network Model ◽  
Transportation Network ◽  
Dynamic Network ◽  
Network Models ◽  
Global Network ◽  
Statistical Characteristics ◽  
Road Transportation ◽  
Road Infrastructure ◽  
Urban Road ◽  
Infrastructure Network

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

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