The formation mechanism of innovation networks is one of the core issues in the current research of innovation networks, and proximity plays an important role in the formation and development of innovation networks; however, which proximity is more important and how different proximities interact remain to be further researched. This study conducts a social network analysis and adopts a spatial interaction model to examine innovation networks among 290 Chinese cities. The results reveal that, first, the (...) class='Hi'>hierarchical characteristics of Chinese cities’ innovation networks reflect a core periphery structure and the spatial patterns of large dispersion and small agglomeration. Further, bound by the Hu line, the hierarchy is high in the east and low in the west. Second, geographical, institutional, and cognitive proximities positively affect Chinese cities’ innovation networking. Cognitive proximity, particularly, has the highest impact. Geographical proximity reinforces the effect of institutional proximity, and thus, their interactions are complementary. (shrink)

The missing ingredients in efforts to develop neural networks and artificial intelligence (AI) that can emulate human intelligence have been the evolutionary processes of performing tasks at increased orders of hierarchical complexity. Stacked neural networks based on the Model of Hierarchical Complexity could emulate evolution's actual learning processes and behavioral reinforcement. Theoretically, this should result in stability and reduce certain programming demands. The eventual success of such methods begs questions of humans' survival in the face of (...) androids of superior intelligence and physical composition. These raise future moral questions worthy of speculation. (shrink)

The human race has been socially organizing itself for probably about 1.8 million years. The first form of human organization was the hunter-gatherer, which was the form of organization in which man lived for about 99 % of his history. This mode of life caused humans to organize themselves into small groups and lead a nomadic life. The nomadic life ensured that these groups had no possessions and no wealth could be accumulated. In this manner, this form of human organization (...) ensured egalitarianism, a form of primitive communism. This changed dramatically with the development of agriculture in the so-called Neolithic Revolution in 10,000 BC. With the development of agriculture and the consequent development of technology, man became sedentary, the first towns were formed, and the class system made its appearance, that is, the separation between an administrative class and a productive class. The egalitarian and communist primitive society gave way to an increasingly centralized and hierarchical society. From network theory, they have been insisting for years on the benefits of the so-called scale-free networks, networks governed by certain privileged nodes called hubs. These networks are said to be slightly vulnerable and highly robust to errors. In this work, we perform a deep network analysis of a group of centralized-hierarchical networks and a group of decentralized-distributed networks. This work provides clear and compelling evidence that, contrary to what is maintained, decentralized-distributed networks are the least vulnerable and most robust networks, and are the networks that ensure an equitable and equal distribution of power and influence among their members. These results are analyzed in the context of network theory, and are discussed in relation to the theories of Jeremy Rifkin, Ernst Schumacher, Alexander Bogdanov and Aldous Huxley. (shrink)

Considering the importance of China’s digital economy, industrial Internet, and high-quality development, this study analyzed China’s urban network from the perspective of the communications technology service industry. Three sub-networks and a comprehensive network were constructed. The density, centrality, and cohesive subgroups of the above network were identified. The results show that: cohesion of urban networks in China is weak and resource sharing is low. From west to east, the urban network forms a multilevel diamond structure in the periphery, (...) a parallelogram structure in the semiperiphery, and a triangle structure in the center. The spatial distribution of cohesive subgroups is scattered, disobeying the first law of geography. By constructing sub-networks and a comprehensive network, the subnetworks that dominate China’s urban networks were identified and their typical characteristics described. This study clarifies the technical support pattern behind China’s digital economy development and industrial internet construction and provides a basis for policy-makers to optimize the country’s high-quality development in the future. (shrink)

Leader-managers face a myriad of competing values when they engage in ethical decision-making. Few studies help us understand why certain reasons for action are justified, taking precedence over others when people choose to respond to an ethical dilemma. To help address this matter we began with a qualitative approach to disclose leader-managers' moral motives when they decide to address a work-related ethical dilemma. One hundred and nine military officers were asked to provide their reasons for taking action, justifications of their (...) reasons, and to explain these justifications. We used network analysis techniques to identify a hierarchical motive structure. The motive structure is a cognitive map that identifies ethical motives and perceptions of how these ethical motives relate to each other. The motives identified represent classic conceptualizations of moral behavior; namely, virtue theories, consequentialism, and deontological theories, along with another category that expressed the emotional significance of the moral judgment, which we refer to as emotional empiricism. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel (...) capabilities of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process and particle swarm optimization BP neural network is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual (...) experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network. (shrink)

The balance between births and deaths in an age-structured population is strongly influenced by the spatial distribution of sub-populations. Our aim was to describe the demographic process of a fish population in an hierarchical dendritic river network, by taking into account the possible movements of individuals. We tried also to quantify the effect of river network changes (damming or channelling) on the global fish population dynamics. The Salmo trutta life pattern was taken as an example for.We proposed a model (...) which includes the demographic and the migration processes, considering migration fast compared to demography. The population was divided into three age-classes and subdivided into fifteen spatial patches, thus having 45 state variables. Both processes were described by means of constant transfer coefficients, so we were dealing with a linear system of difference equations. The discrete case of the variable aggregation method allowed the study of the system through the dominant elements of a much simpler linear system with only three global variables: the total number of individuals in each age-class. (shrink)

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and (...) a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. (shrink)

We review the psychological literature on the organization of valence, discussing theoretical perspectives that favor a single dimension of valence, multiple valence dimensions, and positivity and negativity as dynamic and flexible properties of mental experience that are contingent upon context. Turning to the neuroscience literature that spans three levels of analysis, we discuss how positivity and negativity can be represented in the brain. We show that the evidence points toward both separable and overlapping brain systems that support affective processes depending (...) on the level of resolution studied. We move from large-scale brain networks that underlie generalized processing, to functionally specific subcircuits, finally to intraregional neuronal distributions, where the organization and interaction across levels allow for multiple types of valence and mixed evaluations. (shrink)

This article presents a two level hierarchical forecasting model developed in a consulting project for a Brazilian magazine publishing company. The first level uses a VARMA model and considers econometric variables. The second level takes into account qualitative aspects of each publication issue, and is based on polynomial networks generated by Genetic Programming (GP).

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%. (...) Frequency of contact between network members was primarily determined by two classes of variable: passive factors (distance, work colleague, overseas) and active factors (emotional closeness, genetic relatedness). Controlling for the influence of passive factors on contact rates allowed the hierarchical structure of human social groups to be delimited. These findings suggest that there may be cognitive constraints on network size. (shrink)

This article uses the concept of partial organization to examine how organizing principles can facilitate the effective operation of networked forms of corruption. We analyze the case study of a corruption network in the South Korean maritime industry in terms of how it operated by selectively appropriating practices normally associated with formal bureaucratic organizations. Our findings show that organizational elements built into the corruption network enabled coordination of corruption activities and served to distort and override practices within member organizations. The (...) network was primarily organized through the hierarchical organization of a bounded and controlled set of members and, to a lesser extent, through processes of monitoring and sanctions. Given its clandestine nature, the network avoided the use of explicit rules to govern behavior, instead relying on habituated routines to ensure consistent and predictable action from members. We find that organizational elements were rescinded when the corruption network was exposed after the sinking of a passenger ferry, the Sewol. By rolling back its hierarchical organization and reverting to core relationships, the corruption network sought to preserve its center. The article illustrates the explanatory value of studying how the activities of corruption networks are enabled and adapt to existential challenges through partial organization. (shrink)

Natural languages vary widely in the degree to which they make use of nested compositional structure in their grammars. It has long been noted by linguists that the languages historically spoken in small communities develop much deeper levels of compositional embedding than those spoken by larger groups. Recently, this observation has been confirmed by a robust statistical analysis of the World Atlas of Language Structures. In order to examine this connection mechanistically, we propose an agent‐based model that accounts for key (...) cultural evolutionary features of language transfer and language change. We identify transitivity as a physical parameter of social networks critical for the evolution of compositional structure and the hierarchical patterning of scale‐free distributions as inhibitory. (shrink)

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that (...) it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. (shrink)

Network analysis is a fundamental approach to the study of social structure. This chapter traces its development, distinguishing characteristics, and analytic principles. It emphasizes the intellectual unity of three research traditions: the anthropological concept of the social network, the sociological conception of social structure as social network, and structural explanations of political processes. Network analysts criticize the normative, categorical, dyadic, and bounded-group emphases prevalent in many sociological analyses. They claim that the most direct way to study a social system is (...) to analyze the pattern of ties linking its members. By analyzing complex hierarchical structures of asymmetric ties, they study power, stratification, and structural change. (shrink)

I explored the relationship between the “Black Axe” Confraternity and cybercrime, with a particular emphasis on the structural dynamics of the Business Email Compromise (BEC) schemes. I investigated whether a conventional hierarchical system governs the membership and remuneration for BEC roles as perpetrators by interviewing an accused “leader” of the “Black Axe” affiliated cybercriminal incarcerated in a prominent Western nation. I supplemented the analysis of interview data with insights from tapped phone records monitored by a law enforcement entity. I (...) merged Actor-network theory and Social Network theory as analytical frameworks and thematically analyzed data to produce six overarching themes: (1) The fluidity of Structure and Adaptive Roles in BEC, (2) Challenges in Visualizing Criminal Networks, (3) Globalization and Transnational Dimensions, (4) Social and Cultural Influences, (5) Internal Cybersecurity Threats and Money Laundering, and (6) Remuneration and Influences of Cryptocurrency, casting a brighter light on the topic. Unlike traditional organized crime, BEC scammers have adopted a nonhierarchical model that is flexible and fluid. I found no evidence of a rigid hierarchy dictating positions and remuneration for BEC roles. Instead, I observed that cybercriminals involved in BEC activities functioned horizontally, promoting fluidity, maneuverability, collaboration, and specialization across various facets of their illicit pursuits. I also highlighted the cross-border links BEC offenders have with other criminal actors facilitated by Black Axe organizational machinery. My research adds value to the existing body of knowledge, utilizing a unique dataset comprising direct testimonies of a high-profile BEC offender affiliated with the ‘hard–to–access,’ Black Axe gang. (shrink)

Autocatalytic networks have been used to model the emergence of self‐organizing structure capable of sustaining life and undergoing biological evolution. Here, we model the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations (MRs) of knowledge and experiences play the role of catalytic molecules, and interactions among them (e.g., the forging of new associations) play the role of reactions and result in representational redescription. The approach tags MRs with their source, that is, whether they were acquired through (...) social learning, individual learning (of pre‐existing information), or creative thought (resulting in the generation of new information). This makes it possible to model how cognitive structure emerges and to trace lineages of cumulative culture step by step. We develop a formal representation of the cultural transition from Oldowan to Acheulean tool technology using Reflexively Autocatalytic and Food set generated (RAF) networks. Unlike more primitive Oldowan stone tools, the Acheulean hand axe required not only the capacity to envision and bring into being something that did not yet exist, but hierarchically structured thought and action, and the generation of new MRs: the concepts EDGING, THINNING, SHAPING, and a meta‐concept, HAND AXE. We show how this constituted a key transition toward the emergence of semantic networks that were self‐organizing, self‐sustaining, and autocatalytic, and we discuss how such networks replicated through social interaction. The model provides a promising approach to unraveling one of the greatest anthropological mysteries: that of why development of the Acheulean hand axe was followed by over a million years of cultural stasis. (shrink)

This paper provides an analytical overview of the apostolic networks in Britain. There are several ways to understand the success of the apostolic networks. They can be seen as an extreme answer to an extreme problem an answer to a profound secularisation leading to declining church attendance and influence within society. The apostolic networks appear to fit into post-modern society better than conventional denominational structures. In terms of their organization, despite appearances, these networks are however (...) class='Hi'>hierarchical like the denominational structures which they challenge. The difference is in the characteristic informality with which these networks can collaborate with each other even when they grow. (shrink)

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant (...) to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: (i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; (ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and (iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task. (shrink)

This chapter discusses the limitations of approaches to modelling and handling evidential issues using hierarchical network representations. Such models of evidential inference rest on the compound proposition that real-world evidential inference usually or always consists of propositional ‘atoms’ that are linked together by nomological entities of some kind, entities that are often — but not always — called ‘generalisations’. These sorts of models or representations of evidential inference are referred to as ‘network-and-generalisation’ models of evidential inference. It is argued (...) that for certain important problems, especially where these concern meaning and human understanding, these need to be complemented by other methods. (shrink)

Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of (...) approximation, hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. In order to perform the epistemic function Winsberg Model-based reasoning in scientific discovery. Kluwer Academic/plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: holistic validation :253–262, 2010; in Carrier M, Nordmann A Science in the context of application. Springer, Berlin, pp 115–130, 2011), and model coherence. As it turns out, the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic‐level concepts (carrot). A feature‐based attractor network with a single layer of semantic features developed representations of both basic‐level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by (...) the flat attractor network. Experiment and Simulation 2 show that, as with basic‐level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. (shrink)

Due to their non-hierarchical structure, socio-technical networks are prone to the occurrence of the problem of many hands. In the present paper an approach is introduced in which people’s opinions on responsibility are empirically traced. The approach is based on the Rawlsian concept of Wide Reflective Equilibrium (WRE) in which people’s considered judgments on a case are reflectively weighed against moral principles and background theories, ideally leading to a state of equilibrium. Application of the method to a hypothetical (...) case with an artificially constructed network showed that it is possible to uncover the relevant data to assess a consensus amongst people in terms of their individual WRE. It appeared that the moral background theories people endorse are not predictive for their actual distribution of responsibilities but that they indicate ways of reasoning and justifying outcomes. Two ways of ascribing responsibilities were discerned, corresponding to two requirements of a desirable responsibility distribution: fairness and completeness. Applying the method triggered learning effects, both with regard to conceptual clarification and moral considerations, and in the sense that it led to some convergence of opinions. It is recommended to apply the method to a real engineering case in order to see whether this approach leads to an overlapping consensus on a responsibility distribution which is justifiable to all and in which no responsibilities are left unfulfilled, therewith trying to contribute to the solution of the problem of many hands. (shrink)

In mammals, a network of cellular circadian clocks organizes physiology and behavior along the 24‐h day cycle. The traditional hierarchical model of circadian clock organization with a central pacemaker and peripheral slave oscillators has recently been challenged by studies combining tissue‐specific mouse mutants with transcriptome analyses. First, a surprisingly small number of tissue rhythms are lost when only local clocks are ablated and, second, transcriptional circadian rhythms appear to be regulated by a complex mix of local and systemic factors. (...) As reviewed here, these findings suggest a more integrated model of clock network interaction with the central pacemaker as the main source of behavioral and systemic–physiological rhythms and peripheral clocks controlling some local rhythms while at the same time acting as gatekeepers that temporally adjust cellular responses to external stimuli. (shrink)

Research on urban agglomerations from the perspective of network spatial structure is important to promote their sustainable development. Based on online and traditional data, this paper first improves three aspects of the traditional spatial gravity model—city quality, the gravitation coefficient, and city distance—considering urban center functional intensity and population mobility tendencies. The resulting improved directional gravity model is applied to analyze the structure of the city network for two urban agglomerations in China, the Beijing-Tianjin-Hebei urban agglomeration and the Yangtze River (...) Delta urban agglomeration. The results of the study are as follows: the existing urban connections have obvious hierarchies and imbalances, with the YRDUA urban hierarchical connections being of larger scale. Cities are closely connected, but city networks are unbalanced, though the YRDUA has more balanced urban development. Each node city has a clear radiation range limit, and spatial distance remains an important constraint on urban connections. The backbone network of the BTHUA has a triangular shape and trends toward a “sparse north and dense south,” while the YRDUA is characterized by multiple axes and an overall distribution that trends toward a “dense north and sparse south.” Cities with poor comprehensive strength are more likely to be captured, forming an attract and be attracted relationship. The BTHUA and the YRDUA each form three communities. (shrink)

Guanxi (literally interpersonal connections) is in essence a network of resource coalition-based stakeholders sharing resources for survival, and it plays a key role in achieving business success in China. However, the salience of guanxi stakeholders varies: not all guanxi relationships are necessary, and among the necessary guanxi participants, not all are equally important. A hierarchical stakeholder model of guanxi is developed drawing upon Mitchell et al.’s (1997) stakeholder salience theory and Anderson’s (1982) constituency theory. As an application of instrumental (...) stakeholder theory, the model dimensionalizes the notion of stakeholder salience, and distinguishes between and among internal and external guanxi, core, major, and peripheral guanxi, and primary and secondary guanxi stakeholders. Guanxi management principles are developed based on a hierarchy of guanxi priorities and management specializations. The goal of this application of instrumental stakeholder theory is to construct, for Western business firms in China, a means to reliably identify guanxi partners by employing the principles of effective guanxi. These principles are described in the form of testable propositions that advance social scientific research in this area of international business ethics. (shrink)

From the viewpoint of information transaction models in linguistic pragmatics, expressions of linguistic politeness (LP) induce costs upon speakers. That speakers regularly “pay" such cost is what formal models of LP typically explain either by individual-level _strategic_ considerations (e.g., the speaker’s aim of avoiding a face-threat to the hearer) or community-level _conventional_ considerations (e.g., the use of LP as a relation-acknowledging device). Because these explanations are compatible, as each relates to the speaker and hearer’s social relation, we combine them into (...) a single game-theoretical model enriched by three types of social network structures (ring-shaped, star-shaped, and complete). Using simulation studies of (single and repeated) speech acts of requesting, we let the degree of LP be determined by (i) the degree of social imposition associated with a request, (ii) the number of interlocutors’ past interactions, and (iii) the relative importance of strategic and conventional considerations. The greatest average optimal degree of LP is obtained in the star-shaped network, which intuitively corresponds to a power-centered, hierarchical society. (shrink)

Lonely students typically underperform academically. According to several studies, the COVID-19 pandemic is an important risk factor for increases in loneliness, as the contact restrictions and the switch to mainly online classes potentially burden the students. The previously familiar academic environment, as well as the exchange with peers and lecturers on site, were no longer made available. In our cross-sectional study, we examine factors that could potentially counteract the development of higher education student loneliness during the COVID-19 pandemic from a (...) social network perspective. During the semester, N = 283 students from across all institutional faculties of a German comprehensive university took part in an online survey. We surveyed their social and emotional experiences of loneliness, their self-reported digital information-sharing behavior, and their current egocentric networks. Here, we distinguished between close online contacts and close offline contacts. In addition, we derived the interconnectedness and heterogeneity of students’ contacts. To obtain the latter, we used a novel two-step method combining t-distributed stochastic neighbor embedding and cluster analysis. We explored the associations of the aforementioned predictors on social and emotional loneliness separately using two hierarchical multiple linear regression models. Our results suggest that social loneliness is strongly related to digital information-sharing behavior and the network structure of close contacts. In particular, high information-sharing behavior, high number of close contacts, a highly interconnected network, and a homogeneous structure of close contacts were associated with low social loneliness. Emotional loneliness, on the other hand, was mainly related to network homogeneity, in the sense that students with homogeneous close contacts networks experienced low emotional loneliness. Overall, our study highlights the central role of students’ close social network on feelings of loneliness in the context of COVID-19 restrictions. Limitations and implications are discussed. (shrink)

The search for a systems‐level picture of metabolism as a web of molecular interactions provides a paradigmatic example of how the methods used to characterize a system can bias the interpretation of its functional meaning. Metabolic maps have been analyzed using novel techniques from network theory, revealing some non‐trivial, functionally relevant properties. These include a small‐world structure and hierarchical modularity. However, as discussed here, some of these properties might actually result from an inappropriate way of defining network interactions. Starting (...) from the so‐called bipartite organization of metabolism, where the two meaningful subsets (reactions and metabolites) are considered, most current works use only one of the subsets by means of so‐called graph projections. Unfortunately, projected graphs often ignore relevant biological and chemical constraints, thus leading to statistical artifacts. Some of these drawbacks and alternative approaches need to be properly addressed. (shrink)

The main assumption of this presentation is that networking can be conceived in terms of an effect of apparatuses. It is characterized by hierarchical observations, normalizing judgements, examinations. From this point of view, networking is not an inter-disciplinary system or – even – a-disciplinary, but it is in fact a discipline. Then, given that one of the main aspects of networking is that we are completely merged with it, it is quite difficult to consider it as a discipline with (...) a critical eye; and we can perceive its consequences mainly within the educational system. The aim of this presentation is, first, to describe networking as a contemporary practice, coextensive with a specific disciplinary system. In this case, I would like to demonstrate how networking (specifically its most massive and less ingenuous branch, social networking) is one of the most powerful apparatus for contemporary subjectivation processes. Following Deleuze, it will be pointed out how networking is based on a peculiar regime of signs that reduces drastically the processes of subjectivation it determines, to the scarce possibilities offered by the apparatus. Second, some exit strategies from the (social) networking apparatus are suggested, built on three keywords: anomie, anonymity and untimeliness. They are based on the assumption that – against a continual push towards the presence or the telepresence – the absence can be a concrete contemporary kind of process of subjectivation. (shrink)

Empirical studies such as Goyal et al. (J Polit Econ 114(2):403–412, 2006) or Newman (Proc Natl Acad Sci USA 101(Suppl. 1):5200–5205, 2004) show that scientific collaboration networks present a highly unequal and hierarchical distribution of links. This implies that some researchers can be much more active and productive than others and, consequently, they can enjoy a much better scientific reputation. One may think that big intrinsical differences among researchers can constitute the main driving force behind these inequalities. Nevertheless, (...) this model shows that, under specific circumstances, very similar individuals may self-organize themselves forming unequal and hierarchical structures. (shrink)

This conceptual article suggests a reexamination of current governance structures, specifically those of unitary boards after the financial crisis of 2008.We suggest that the existing governance structures are based on an outdated paradigm of business, rooted in economics. We propose an alternative paradigm, a more humanistic paradigm, which allows conceiving alternative, network-oriented governance structures. As hierarchical firms grow larger and more complex, the risk of failure increases from biases, errors, and missing data in communication and control systems. These problems (...) are exacerbated by information overload on senior managers, directors, and their respective regulators. In contrast to traditional corporate governance, network governance introduces a division of power via multiple boards, checks and balances, and active stakeholder engagement. We argue that those features could have prevented the stresses and failures of financial firms in 2008, since they were anticipated by both individuals within firms and external commentators. However, those exposed to risks possessed insufficient influence in either governing and/or regulating firms to take corrective action. (shrink)

ArgumentThis paper examines the field network – linking together lay observers in geographically distributed locations with a central figure who aggregated their locally produced observations into more general, regional knowledge – as a historically emergent mode of knowledge production. After discussing the significance of weather knowledge as a vital domain in which field networks have operated, it describes and analyzes how a more robust and systematized weather observing field network became established and maintained on the ground in the early (...) twentieth century. This case study, which examines two Kansas City-based local observer networks supervised by the same U.S. Weather Bureau office, demonstrates some of the key issues involved in maintaining field networks, such as the role of communications infrastructure, especially the telegraph, the procedures designed to make local observation more systematic and uniform, and the centralized, hierarchical power relations that underpinned even a low-status example of knowledge production on the periphery. (shrink)

The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar (...) correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity. (shrink)

We distinguish traditional cognition theories from hierarchically complex stacked neural networks that meet many of Newell's criteria. The latter are flexible and can learn anything that a person can learn, by using their mistakes and successes the same way humans do. Shortcomings are due largely to limitations of current technology.

The hierarchically nested structure is widely observed in a broad range of real systems, encompassing ecological networks, economic and trade networks, communication networks, among many others. However, there remain statistical challenges of the prevalence of nestedness. In response to this problem, we focus on the effect of incomplete information and the inputted matrix size, the role of network density and degree sequences, and the relevance of degree-degree correlation to conduct systematic research on the significance of the nested (...) structure according to multiplex world trade networks. Firstly, the nested structure can observe significantly when suffering incomplete information and varying inputted matrix size. Secondly, to analyze the role of network density and degree sequences in nested structure, we use “swappable rows, swappable columns” null model which conserves the network size and density, and “fixed in-degree, fixed out-degree” null model which not only preserves the network size and density but also keeps node degree to do randomization tests of nestedness. The randomizations of two null models remark that most nested structures are not determined by network density and degree sequences but closely related to them. Finally, we investigate degree-degree correlation in nested networks, and the results show that the nested structure is negatively related with degree-degree correlation of the network. Following the empirical analysis train of thought, we argue that nestedness is still a unique feature of the network. (shrink)

This work studies the interaction networks that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more (...) hierarchical structure than the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined. (shrink)

Systems biology has provided new resources for discovering and reasoning about mechanisms. In addition to generating databases of large bodies of data, systems biologists have introduced platforms such as Cytoscape to represent protein–protein interactions, gene interactions, and other data in networks. Networks are inherently flat structures. One can identify clusters of highly connected nodes, but network representations do not represent these clusters as at a higher level than their constituents. Mechanisms, however, are hierarchically organized: they can be decomposed (...) into their parts and their activities can be decomposed into component operations. A potent bridge between flat networks and hierarchical mechanisms is provided by biological ontologies, both those curated by hand such as Gene Ontology and those extracted directly from databases such as Network Extracted Ontology. I examine several examples in which by applying ontologies to networks, systems biologists generate new hypotheses about mechanisms and characterize these novel strategies for developing mechanistic explanations. (shrink)