Results for 'networks'

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  1. Community Networks and the Evolution of Civic Intelligence.Douglas Schuler - 2010 - AI and Society 25 (3):291-307.
    Although the intrinsic physicality of human beings has not changed in millennia, the species has managed to profoundly reconstitute the physical and social world it inhabits. Although the word “profound” is insufficient to describe the vast changes our world has undergone, it is sufficiently neutral to encompass both the opportunities—and the challenges—that our age provides. It is a premise of my work that technology, particularly information and communication technology (ICT), offers spectacular opportunities for humankind to address its collective problems. The (...)
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  2. Network Representation and Complex Systems.Charles Rathkopf - 2018 - Synthese (1).
    In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...)
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  3. Networks Are Not ‘Hidden Rules’.Mark S. Seidenberg & Jeffrey L. Elman - 1999 - Trends in Cognitive Sciences 3 (8):288-289.
  4.  2
    Affordances of the Networked Image.Centre for the Study of the Networked Image, Geoff Cox, Annet Dekker, Andrew Dewdney & Katrina Sluis - 2021 - Nordic Journal of Aesthetics 30 (61-62):40-45.
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  5. Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.Patrick Grim, Daniel J. Singer, Steven Fisher, Aaron Bramson, William J. Berger, Christopher Reade, Carissa Flocken & Adam Sales - 2013 - Episteme 10 (4):441-464.
    A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...)
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  6.  67
    Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity.Sara Green, Maria Şerban, Raphael Scholl, Nicholaos Jones, Ingo Brigandt & William Bechtel - 2018 - Synthese 195 (4):1751-1777.
    The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span (...)
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  7.  23
    Cognitive Network Science: A Review of Research on Cognition Through the Lens of Network Representations, Processes, and Dynamics. [REVIEW]Cynthia S. Q. Siew, Dirk U. Wulff, Nicole M. Beckage & Yoed N. Kenett - 2019 - Complexity 2019:1-24.
    Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive (...)
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  8. Social Networking Technology and the Virtues.Shannon Vallor - 2010 - Ethics and Information Technology 12 (2):157-170.
    This paper argues in favor of more widespread and systematic applications of a virtue-based normative framework to questions about the ethical impact of information technologies, and social networking technologies in particular. The first stage of the argument identifies several distinctive features of virtue ethics that make it uniquely suited to the domain of IT ethics, while remaining complementary to other normative approaches. I also note its potential to reconcile a number of significant methodological conflicts and debates in the existing literature, (...)
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  9.  25
    The Network Approach to Psychopathology: A Review of the Literature 2008–2018 and an Agenda for Future Research.Donald J. Robinaugh, Ria H. A. Hoekstra, Emma R. Toner & Denny Borsboom - 2019 - Psychological Medicine:1-14.
    The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention (...)
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  10.  16
    Hierarchies, Networks, and Causality: The Applied Evolutionary Epistemological Approach.Nathalie Gontier - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (2):313-334.
    Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...)
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  11. Vulnerability in Social Epistemic Networks.Emily Sullivan, Max Sondag, Ignaz Rutter, Wouter Meulemans, Scott Cunningham, Bettina Speckmann & Mark Alfano - 2020 - International Journal of Philosophical Studies 28 (5):1-23.
    Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In (...)
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  12.  11
    Active Networks and Active Network Management a Proactive Management Framework: Solution Manual.Stephen F. Bush & Amit B. Kulkarni - 2001 - Kluwer Academic/Plenum Publishers.
    Active networking is an exciting new paradigm in digital networking that has the potential to revolutionize the manner in which communication takes place. It is an emerging technology, one in which new ideas are constantly being formulated and new topics of research are springing up even as this book is being written. This technology is very likely to appeal to a broad spectrum of users from academia and industry. Therefore, this book was written in a way that enables all these (...)
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  13.  70
    Social Network Size in Humans.R. A. Hill & R. I. M. Dunbar - 2003 - Human Nature 14 (1):53-72.
    This paper examines social network size in contemporary Western society based on the exchange of Christmas cards. Maximum network size averaged 153.5 individuals, with a mean network size of 124.9 for those individuals explicitly contacted; these values are remarkably close to the group size of 150 predicted for humans on the basis of the size of their neocortex. Age, household type, and the relationship to the individual influence network structure, although the proportion of kin remained relatively constant at around 21%. (...)
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  14.  32
    Analysing Network Models to Make Discoveries About Biological Mechanisms.William Bechtel - 2019 - British Journal for the Philosophy of Science 70 (2):459-484.
    Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. (...)
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  15.  29
    Talent Flow Network, the Life Cycle of Firms, and Their Innovations.Bo Sun, Ao Ruan, Biyu Peng & Wenzhu Lu - 2022 - Frontiers in Psychology 13:788515.
    This paper explores how talent flow network and the firm life cycle affect the innovative performances of firms. We first established an interorganizational talent flow network with the occupational mobility data available from the public resumes on LinkedIn China. Thereafter, this information was combined with the financial data of China’s listed companies to develop a unique dataset for the time period between 2000 and 2015. The empirical results indicate the following: (1) The breadth and depth of firms’ embedding in the (...)
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  16.  5
    The Network Self: Relation, Process, and Personal Identity.Kathleen Wallace - 2019 - Routledge.
    The concept of a relational self has been prominent in feminism, communitarianism, narrative self theories, and social network theories, and has been important to theorizing about practical dimensions of selfhood. However, it has been largely ignored in traditional philosophical theories of personal identity, which have been dominated by psychological and animal theories of the self. This book offers a systematic treatment of the notion of the self as constituted by social, cultural, political, and biological relations. The author's account incorporates practical (...)
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  17.  34
    Networks of Giving and Receiving in an Organizational Context: Dependent Rational Animals and MacIntyrean Business Ethics.Caleb Bernacchio - 2018 - Business Ethics Quarterly 28 (4):377-400.
    ABSTRACT:Alasdair MacIntyre’sAfter Virtuehas made a significant impact within business ethics. This impact has centered upon applications of the virtues-goods-practices-institutions schema. In this article, I develop an extension of the practices-institutions schema, drawing upon MacIntyre’s later text,Dependent Rational Animals. Two key concepts drawn from this text are “networks of giving and receiving” and “the virtues of acknowledged dependence.” Networks of giving and receiving are non-calculative relationships that enable participants to cope with vulnerability. These relationships are sustained by the virtues (...)
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  18. Epistemic Advantage on the Margin: A Network Standpoint Epistemology.Jingyi Wu - 2022 - Philosophy and Phenomenological Research:1-23.
    ​I use network models to simulate social learning situations in which the dominant group ignores or devalues testimony from the marginalized group. I find that the marginalized group ends up with several epistemic advantages due to testimonial ignoration and devaluation. The results provide one possible explanation for a key claim of standpoint epistemology, the inversion thesis, by casting it as a consequence of another key claim of the theory, the unidirectional failure of testimonial reciprocity. Moreover, the results complicate the understanding (...)
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  19. Artificial Neural Network for Forecasting Car Mileage Per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 124:51-59.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...)
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  20.  28
    Beyond Networks: Mechanism and Process in Evo-Devo.James DiFrisco & Johannes Jaeger - 2019 - Biology and Philosophy 34 (6):54.
    Explanation in terms of gene regulatory networks has become standard practice in evolutionary developmental biology. In this paper, we argue that GRNs fail to provide a robust, mechanistic, and dynamic understanding of the developmental processes underlying the genotype–phenotype map. Explanations based on GRNs are limited by three main problems: the problem of genetic determinism, the problem of correspondence between network structure and function, and the problem of diachronicity, as in the unfolding of causal interactions over time. Overcoming these problems (...)
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  21. Networks in Cognitive Science.Andrea Baronchelli, Ramon Ferrer-I.-Cancho, Romualdo Pastor-Satorras, Nick Chater & Morten H. Christiansen - 2013 - Trends in Cognitive Sciences 17 (7):348-360.
  22.  47
    Social Networkers' Attitudes Toward Direct-to-Consumer Personal Genome Testing.Amy McGuire, Christina Diaz, Tao Wang & Susan Hilsenbeck - 2009 - American Journal of Bioethics 9 (6-7):3-10.
    Purpose: This study explores social networkers' interest in and attitudes toward personal genome testing (PGT), focusing on expectations related to the clinical integration of PGT results. Methods: An online survey of 1,087 social networking users was conducted to assess 1) use and interest in PGT; 2) attitudes toward PGT companies and test results; and 3) expectations for the clinical integration of PGT. Descriptive statistics were calculated to summarize respondents' characteristics and responses. Results: Six percent of respondents have used PGT, 64% (...)
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  23. Artificial Neural Network for Predicting Car Performance Using JNN.Awni Ahmed Al-Mobayed, Youssef Mahmoud Al-Madhoun, Mohammed Nasser Al-Shuwaikh & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):139-145.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...)
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  24.  8
    Brain Networks of Emotional Prosody Processing.Didier Grandjean - 2020 - Emotion Review 13 (1):34-43.
    The processing of emotional nonlinguistic information in speech is defined as emotional prosody. This auditory nonlinguistic information is essential in the decoding of social interactions and in our capacity to adapt and react adequately by taking into account contextual information. An integrated model is proposed at the functional and brain levels, encompassing 5 main systems that involve cortical and subcortical neural networks relevant for the processing of emotional prosody in its major dimensions, including perception and sound organization; related action (...)
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  25.  33
    Network Modularity as a Foundation for Neural Reuse.Matthew L. Stanley, Bryce Gessell & Felipe De Brigard - 2019 - Philosophy of Science 86 (1):23-46.
    The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network (...)
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  26.  85
    Probabilistic Logics and Probabilistic Networks.Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson - 2010 - Dordrecht, Netherland: Synthese Library.
    Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.
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  27.  15
    Brain Networks, Emotion Components, and Appraised Relevance.David Sander, Didier Grandjean & Klaus R. Scherer - 2018 - Emotion Review 10 (3):238-241.
    Modeling emotion processes remains a conceptual and methodological challenge in affective sciences. In responding to the other target articles in this special section on “Emotion and the Brain” and the comments on our article, we address the issue of potentially separate brain networks subserving the functions of the different emotion components. In particular, we discuss the suggested role of component synchronization in producing information integration for the dynamic emergence of a coherent emotion process, as well as the links between (...)
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  28. Bayesian Networks and the Problem of Unreliable Instruments.Luc Bovens & Stephan Hartmann - 2002 - Philosophy of Science 69 (1):29-72.
    We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some (...)
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  29.  23
    Social Networks, Support Cliques, and Kinship.R. I. M. Dunbar & M. Spoors - 1995 - Human Nature 6 (3):273-290.
    Data on the number of adults that an individual contacts at least once a month in a set of British populations yield estimates of network sizes that correspond closely to those of the typical “sympathy group” size in humans. Men and women do not differ in their total network size, but women have more females and more kin in their networks than men do. Kin account for a significantly higher proportion of network members than would be expected by chance. (...)
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  30.  45
    Network Hubs in the Human Brain.Martijn P. van den Heuvel & Olaf Sporns - 2013 - Trends in Cognitive Sciences 17 (12):683-696.
  31.  56
    Social Network Analysis and Critical Realism.Hubert Buch-Hansen - 2014 - Journal for the Theory of Social Behaviour 44 (3):306-325.
    Social network analysis (SNA) is an increasingly popular approach that provides researchers with highly developed tools to map and analyze complexes of social relations. Although a number of network scholars have explicated the assumptions that underpin SNA, the approach has yet to be discussed in relation to established philosophies of science. This article argues that there is a tension between applied and methods-oriented SNA studies, on the one hand, and those addressing the social-theoretical nature and implications of networks, on (...)
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  32. Learning Networks and Connective Knowledge.Stephen Downes - 2010 - In Harrison Hao Yang & Steve Chi-Yin Yuen (eds.), Collective Intelligence and E-Learning 2.0: Implications of Web-Based Communities and Networking. IGI Global.
    The purpose of this chapter is to outline some of the thinking behind new e-learning technology, including e-portfolios and personal learning environments. Part of this thinking is centered around the theory of connectivism, which asserts that knowledge - and therefore the learning of knowledge - is distributive, that is, not located in any given place (and therefore not 'transferred' or 'transacted' per se) but rather consists of the network of connections formed from experience and interactions with a knowing community. And (...)
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  33.  24
    Using Network Models in Person-Centered Care in Psychiatry: How Perspectivism Could Help To Draw Boundaries.Nina de Boer, Daniel Kostić, Marcos Ross, Leon de Bruin & Gerrit Glas - forthcoming - Frontiers in Psychiatry, Section Psychopathology.
    In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can make more explicit (...)
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  34.  16
    Taxonomies, Networks, and Lexicons: A Study of Kuhn’s Post-‘Linguistic Turn’ Philosophy.Vincenzo Politi - 2021 - International Studies in the Philosophy of Science 33 (2):87-103.
    In his mature works, Kuhn abandons the concept of a paradigm and becomes more interested in the analysis of the conceptual structure of scientific theories. These changes are interpreted as resulting from a ‘linguistic turn’ that Kuhn underwent sometimes around the 1980s. Much of the philosophical discussions about Kuhn’s post-‘linguistic turn’ philosophy revolves around his views on taxonomic concepts. Apart from taxonomy, however, the mature Kuhn introduces other concepts, such as conceptual networks and lexicons. This article distinguishes these three (...)
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  35.  26
    A Network Model of the Emotional Brain.Luiz Pessoa - 2017 - Trends in Cognitive Sciences 21 (5):357-371.
  36.  79
    Prince of Networks: Bruno Latour and Metaphysics.Graham Harman - 2009 - re.press.
    Prince of Networks is the first treatment of Bruno Latour specifically as a philosopher. It has been eagerly awaited by readers of both Latour and Harman since their public discussion at the London School of Economics in February 2008. Part One covers four key works that display Latour’s underrated contributions to metaphysics: Irreductions, Science in Action, We Have Never Been Modern, and Pandora’s Hope. Harman contends that Latour is one of the central figures of contemporary philosophy, with a highly (...)
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  37. Self-Assembling Networks.Jeffrey A. Barrett, Brian Skyrms & Aydin Mohseni - 2019 - British Journal for the Philosophy of Science 70 (1):1-25.
    We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This (...)
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  38.  26
    Alliance Network Centrality, Board Composition, and Corporate Social Performance.Craig D. Macaulay, Orlando C. Richard, Mike W. Peng & Maria Hasenhuttl - 2018 - Journal of Business Ethics 151 (4):997-1008.
    What critical characteristics do firms have that determine the scale and scope of corporate social responsibility activities they undertake? This paper examines two disparate predictors of corporate social performance. First, using the lens of the resource-based view, we examine the role of alliance network centrality on corporate social performance. We find that centrality enhances corporate social performance. Second, we investigate how board composition affects corporate social performance. Specifically, drawing on stakeholder theory, we find that the percentage of female directors predicts (...)
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  39.  36
    Language Networks: Their Structure, Function, and Evolution.Ricard V. Solé, Bernat Corominas-Murtra, Sergi Valverde & Luc Steels - 2010 - Complexity 15 (6):20-26.
  40.  14
    Network and Multilayer Network Approaches to Understanding Human Brain Dynamics.Sarah Feldt Muldoon & Danielle S. Bassett - 2016 - Philosophy of Science 83 (5):710-720.
    Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples (...)
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  41.  2
    Language Networks: The New Word Grammar.Richard A. Hudson - 2007 - Oxford University Press UK.
    This book argues that language is a network of concepts which in turn is part of the general cognitive network of the mind. It challenges the widely-held view that language is an innate mental module with its own special internal organization. It shows that language has the same internal organization as other areas of knowledge such as social relations and action schemas, and reveals the rich links between linguistic elements and contextual categories. Professor Hudson presents a new theory of how (...)
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  42.  37
    Corruption Networks and Implications for Ethical Corruption Reform.Richard P. Nielsen - 2003 - Journal of Business Ethics 42 (2):125 - 149.
    The problem this article focuses on is not the isolated individual act of corruption, but the systematic, pervasive sub-system of corruption that can and has existed across historical periods, geographic areas, and political-economic systems. It is important to first understand how corrupt and unethical subsystems operate, particularly their network nature, in order to reform and change them while not becoming what we are trying to change. Twelve key system elements are considered that include case examples from Asia, Latin America, the (...)
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  43. Learning From Multi-Stakeholder Networks: Issue-Focussed Stakeholder Management.Julia Roloff - 2008 - Journal of Business Ethics 82 (1):233-250.
    From an analysis of the role of companies in multi-stakeholder networks and a critical review of stakeholder theory, it is argued that companies practise two different types of stakeholder management: they focus on their organization’s welfare (organization- focussed stakeholder management) or on an issue that affects their relationship with other societal groups and organizations (issue-focussed stakeholder management). These two approaches supplement each other. It is demonstrated that issue-focussed stakeholder management dominates in multi-stakeholder networks, because it enables corporations to (...)
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  44.  2
    Network Growth Modeling to Capture Individual Lexical Learning.Nicole M. Beckage & Eliana Colunga - 2019 - Complexity 2019:1-17.
    Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between (...)
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  45. Semantic Networks.M. Ross Quillian - 1968 - In Marvin L. Minsky (ed.), Semantic Information Processing. MIT Press.
  46.  66
    The Explanatory Power of Network Models.Carl F. Craver - 2016 - Philosophy of Science 83 (5):698-709.
    Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.
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  47.  19
    Networked CSR Governance: A Whole Network Approach to Meta-Governance.Sandra Waddock & Laura Albareda - 2018 - Business and Society 57 (4):636-675.
    Meta-governance is Earth system governance for dealing with the global commons. This article develops a whole network approach to meta-governance to explore the potential for collective action for sustainable development by a loosely coupled network of networks. Networked corporate social responsibility governance has emerged around corporate sustainability and responsibility in the first years of the 21st century. Growing agreements and interactions among CSR initiatives suggest the development, structure, and governance of networked CSR governance as a network that can analytically (...)
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  48.  94
    Comorbidity: A Network Perspective.Angélique Oj Cramer, Lourens J. Waldorp, Han Lj van der Maas & Denny Borsboom - 2010 - Behavioral and Brain Sciences 33 (2-3):137-150.
    The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to (...)
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  49.  8
    Network Effects in a Bounded Confidence Model.Igor Douven & Rainer Hegselmann - 2022 - Studies in History and Philosophy of Science Part A 94:56-71.
  50.  48
    Disciplinary Networks and Bounding: Scientific Communication Between Science and Technology Studies and the History of Science. [REVIEW]Frédéric Vandermoere & Raf Vanderstraeten - 2012 - Minerva 50 (4):451-470.
    This article examines the communication networks within and between science and technology studies (STS) and the history of science. In particular, journal relatedness data are used to analyze some of the structural features of their disciplinary identities and relationships. The results first show that, although the history of science is more than half a century older than STS, the size of the STS network is more than twice that of the history of science network. Further, while a majority of (...)
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