During the last decade, Network Science has become one of the most active fields in applied physics and mathematics, since it allows the analysis of a diversity of social, biological and technological systems [24]. From the diversity of applications of Network Science, in this Opinion paper we are concerned about its potential to analyse one of the most extended group sports, Football (soccer in U.S. terminology) [29], since it allows addressing different aspects of the team organization (...) and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences “can not be analysed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole” or, in the classical words of after-match interviews “it’s not just me, it’s the team”. The recent ability of obtaining datasets of all events occurring during a match, including the position of the players and the interactions between them has opened the door to new kind of studies where it is possible to analyse and quantify the behaviour of a team as a whole, together with the role of a single player [17]. Under this framework, the organization of a team can be considered as the result of the interaction between its players, creating a network based on passes. In this way, we can create passing networks, which are directed (i.e.,... (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 143-162, January 2022.

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system, making process and representation inextricably connected. Here, we consider how the structure found across phonological networks of several languages from different (...) language families may influence language processing as we age and experience diseases that affect cognition during the typical and atypical acquisition of new words, during typical perception and production of speech in adults, and during language change over time. We conclude that the network science approach may not only provide insights into specific language processes but also provide a way to connect the work from these domains, which are becoming increasingly balkanized. (shrink)

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. (...) To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches. (shrink)

The increasing frequency and scope of financial crises has made global financial stability one of the major concerns of economic policy and decision makers. Under this highly complex environment, supervision of the financial system has to be thought of as a systemic task, focusing not only on the strength of the institutions but also on the interdependent relations among them, unraveling the structure and dynamic of the system as a whole. In recent years, network science has emerged as (...) a leading tool for the investigation of complex systems. Here we review several applications of network science in finance and economics, and discuss existing challenges and future directions which will substantiate network science as a key tool for financial academics, practitioners, and policy and decision makers. (shrink)

Behavioural Network Science provides a comprehensive introduction to network science for social and behavioral researchers and students. It is a self-contained guide to the fundamentals of network science, beginning with principles of representing and making networks, network metrics, and network evolution. It then delves into specific applications of network science to behavioral research including language evolution, learning, memory, aging, creativity, conspiracies, group problem-solving, opinion polarization, and social conflict. Within each application, (...) theoretical aspects surrounding a core problem are discussed, providing readers with an intuitive understanding of how network science can be employed to quantify and generate behavioural phenomena. Accompanying online R code allows readers to replicate simulations and generate figures. This book is an essential reference for graduate students and researchers in cognitive sciences, behavioural economics, and health and social policy seeking a nuanced understanding of the intersection between network science and behavioural research. (shrink)

This article maps a controversy in network science over the last 15 years, dividing the field about the epistemic status of a central notion, scale-freeness. The article accounts for the two main disputes, in 2005 and in 2018, as they unfolded in academic publications and on social media. This article analyzes the conflict, and the reasons why it reignited in 2018, to the surprise of many. It is argued that the concept of complex networks is shared by the (...) distinct subcultures of theorists and experimentalists; and that these subcultures have incompatible approaches to knowledge: nomothetic and idiographic. Following Galison, this article contends that network science is a trading zone where theorists and experimentalists can trade knowledge across the epistemic divide. (shrink)

In the last 20 years network science has become an independent scientific field. We argue that by building network models network scientists are able to tame the vagueness of propositions about complex systems and networks, that is, to make these propositions precise. This makes it possible to study important vague properties such as modularity, near-decomposability, scale-freeness or being a small world. Using an epistemic model of network science, we systematically analyse the specific nature of (...) network models and the logic behind the taming mechanism. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 78-92, January 2022.

Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

The associative theory of creativity states that creativity is associated with differences in the structure of semantic memory, whereas the executive theory of creativity emphasises the role of top-down control for creative thought. For a powerful test of these accounts, individual semantic memory structure was modelled with a novel method based on semantic relatedness judgements and different criteria for network filtering were compared. The executive account was supported by a correlation between creative ability and broad retrieval ability. The associative (...) account was independently supported, when network filtering was based on a relatedness threshold, but not when it was based on a fixed edge number or on the analysis of weighted networks. In the former case, creative ability was associated with shorter average path lengths and higher clustering of the network, suggesting that the semantic networks of creative people show higher small-worldness. (shrink)

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied (...) perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (shrink)

Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences. Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available. It is one of the developing, (...) cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society. The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings. Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts. (shrink)

Historical studies on the relationship between science and diplomacy tend to focus on events since World War II and on initiatives for the maintenance of peace or to achieve cooperation over contentious matters. This article presents the case of José Vicente Barbosa du Bocage (1823–1907), a Portuguese zoologist who had formal diplomatic responsibilities in a context of competition for the colonization of Africa in the nineteenth century. He used his knowledge in African geography to implement colonial and diplomatic strategies (...) that aimed at outcompeting rival powers. The development of a network of actors with scientific, colonial, and diplomatic expertise was crucial for the negotiations that involved the partition of the Congo basin, which resulted in victories for Portugal that surpassed the country's marginal political relevance at the international level and had long‐lasting consequences. (shrink)

Many Big Science projects and networks experience conflict. A plethora of disciplines have examined conflict causes in science collaboration and Big Science, contributing to a more nuanced understanding of why conflicts emerge. Yet, so far, there is no theoretical model that explains which mechanisms connect conflict cause and outbreak in Big Science. Drawing on interdisciplinary literature on science collaboration and Big Science as well as on scholarship on strategic action fields (SAFs), I address this (...) blind spot by proposing a model that outlines which mechanisms induce and fuel conflict in Big Science projects and networks. Five interlinked mechanisms – attribution of threat or opportunity, mobilization of resources, coalition-building, boundary deactivation and innovative action – are central to it. Tracing these mechanisms in conflictual episodes which emerged in three typical, yet most-different, Big Science cases – the International Experimental Thermonuclear Reactor (ITER), the Human Brain Project (HBP) and the Thirty Meter Telescope (TMT) – this study also provides a proof of concept for the model. (shrink)

The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks have made their way into (...) the social sciences, raising the question whether they can avoid controversial ontological assumptions. ANNs are largely distinguished from other statistical and machine learning techniques by being a representation-learning technique. That is, researchers can let the neural networks select which features of the data to use for internal representation instead of imposing their preconceptions. On this basis, I argue that neural networks can avoid ontological assumptions to a greater degree than common statistical models in the social sciences. I then go on, however, to establish that ANNs are not ontologically innocent either. The use of ANNs in the social sciences introduces ontological assumptions typically in at least two ways, via the input and via the architecture. (shrink)

This article situates actor-network theory in the history of evolutionary anthropology. In the 1980s, this attempt at explaining the social through the mediation of nonhumans received important impulses from Bruno Latour’s conversations with primatologist Shirley Strum. In a re-articulation of social evolutionism, they proposed that the utilization of objects distinguished humans from baboons and that the use of a growing number of objects set industrialized human populations apart from hunter-gatherers, enabling the formation of larger collectives. While Strum’s and Latour’s (...) early work presented baboons as almost human and suggested that we moderns had never been modern, the Anthropocene has reawakened curiosity about the original question of anthropology: how do modern humans, including modern scientists, differ from premoderns and animals? This 18th-century question is gaining new significance and urgency as we recognize our transmutation into a super-dominant species. But the answer might not lie solely in the use of more objects. (shrink)

To understand how compliance develops both in everyday and corporate environments, it is crucial to understand how different mechanisms work together to shape individuals’ (non)compliant behavior. Existing compliance studies typically focus on a subset of theories (i.e., rational choice theories, social theories, legitimacy theories, capacity theories, and opportunity theories) to understand how key variables from one or several of these theories shape individual compliance. The present study provides a first integrated understanding of compliance, rooted in complexity science, in which (...) key elements from these theories are considered simultaneously, and their relations to compliance and each other are explored using network analysis. This approach is developed by analyzing online survey data (_N_ = 562) about compliance with COVID-19 mitigation measures. Traditional regression analysis shows that elements from nearly all major compliance theories (except for social theories) are associated with compliance. The network analysis revealed groupings and interconnections of variables that did not track the existing compliance theories and point to a complexity overlooked in existing compliance research. These findings demonstrate a fundamentally different perspective on compliance, which moves away from traditional narrow, non-network approaches. Instead, they showcase a complexity science understanding of compliance, in which compliance is understood as a network of interacting variables derived from different theories that interact with compliance. This points to a new research agenda that is oriented on mapping compliance networks, and testing and modelling how regulatory and management interventions interact with each other and compliance within such networks. (shrink)

SummaryIn 1684, during the Edo period (1603–1868), the imperial court of Japan passed a reform act that resulted in a new almanac called the Jôkyô almanac. This was the first reform in more than eight hundred years, and marked a departure from the past practice of adopting almanacs from China. Yet, the reform was complicated, and it was achieved after decades through the efforts of Shibukawa Harumi (1639–1715). How was the reform accomplished, and why was it significant? In this study, (...) I will focus on Harumi's role in the reform. I propose to examine the reform as the culmination of a process deeply linked to Harumi's political connections and his intellectual growth especially in the field of calendrical sciences. I will show that this approach of looking at the reform by analyzing different aspects of Harumi's life has implications for understanding trends in society in seventeenth-century Japan. First, despite the ban on Christianity, intellectuals like Harumi assimilated European sciences in unique ways. Second, Harumi's activities exemplified how intellectual ties were increasingly embedded within political networks. Overall, I will argue that the reform of the almanac, viewed as a process surrounding the core figure of Harumi, was driven by scientific goals and personal ambitions. (shrink)

According to Mednick’s (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related (...) to each other by their association correlations - overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick’s (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. (shrink)

Science and technology policy in the developing world involves special problems since much of the financial support for S&T originates outside the countries where research is done. The development of information for policy and strategic planning decisions is therefore critical for national research policymakers, international organizations, and donors. However, prior attempts have been plagued by serious methodological problems. We describe a multifaceted approach for generating systematic information on scientific and technological institutions in developing countries based on the concept of (...) the research system as a multiorganizational network. By providing an account of how this approach was implemented in a three-country study we hope to shed light on several related problems in developing information for policy. First, how can relevant actors in research systems be identified? Second, how can a national research system be systematically surveyed? Third, how can system-wide estimates of organizational linkages and performance be obtained? (shrink)

Our purpose in the present work is to survey some of the formulations that the theme of networks has received in contemporary philosophy of science over a period spanning twelve decades, from the end of the 19th century up to the present time. The proposal advanced herein is to interpret the evolution of this theme in four stages: first, one that goes from a metaphor or expressive image to a notion aspiring at implementation, but still having a virtual character, (...) in the orthodox view of theories; second, a shift towards networks viewed as explicit, with the notion still having an enormous metaphorical power, as in Neurath and Quine; third, from this towards a concept amenable to implementation, as in Thagard, but still in search of an epistemological rationale, supposedly provided by BonJour and Dancy; and finally, as a notion fully endowed with a precise structuralist implementation, in the scope of the structuralist metatheory of Balzer-Moulines-Sneed. The interpretive framework adopted in this exercise in the history of philosophy of science is a modified version of Gerald Holton's thematic model, adapted to the history of philosophy instead of its original domain, the history of scientific ideas. In our version of the thematic model, we posit two main dimensions for the appraisal of any given philosophical concept, besides the thematic dimension: the metaphorical one and the dimension of its systematic implementation. (shrink)

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 of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Most studies of René Descartes’s legacy have focused on the novelty of his ideas, but little has been done to uncover the conditions that allowed these ideas to spread. Seventeenth-century Europe was already a small world—it presented a high degree of connectedness with a few brokers bridging otherwise disparate regions. A communication network known as the Republic of Letters enabled scholars to trade ideas—including Descartes’s—by means of correspondence. This article offers an analysis—both qualitative and quantitative—of a corpus of letters (...) written during Descartes’s lifetime and mentioning his name. The aim is to unveil the factors that drove the diffusion of Descartes’s ideas. The results are twofold. First, a close reading of the letters reveals that they were not used to create awareness about Descartes and his works but rather to discuss his ideas. Second, a network analysis of the letters shows that ideas do not spread like viruses, thus undermining the ‘social contagion’ model, and that weak ties are not as effective in promoting innovations as they are in circulating information. (shrink)

This article is intended as a contribution to the current debates about the relationship between politics and the philosophy of science in the Vienna Circle. I reconsider this issue by shifting the focus from philosophy of science as theory to philosophy of science as practice. From this perspective I take as a starting point the Vienna Circle’s scientific world-conception and emphasize its practical nature: I reinterpret its tenets as a set of recommendations that express the particular epistemological (...) attitude in which both the Vienna Circle’s (doing) philosophy of science and its political engagement were rooted. -/- Regarding politics, and referring to new primary sources, I reconstruct how the scientific world-conception placed the Vienna Circle within a neoliberal-socialist political network that pursued concrete political aims. In light of my reconstruction I shall argue that neither the Vienna Circle’s alleged ethical noncognitivism nor its alleged adhesion to the Weberian ideal of a value-free science rules out the possibility of ascribing to the Vienna Circle a politically engaged philosophy of science: the case of the Vienna Circle shows how philosophy of science, as a public activity, can itself become a form of political engagement, even without necessarily entailing a theory of objective values. (shrink)

The paper investigates the structure and functioning of the science and technology (S&T) system in India as it has evolved in the post-independence period (1947 onwards). The networks of entities involved in S&T actions, the paper argues, can be categorised, in terms of adopted approaches to agenda and priority setting and accounting for actions, into two streams. The origins and expansion of the two streams are traced. One, the ‘Elite’ stream (high profile and visibility linked to big industry), adopting (...) what the paper has generically termed the ‘Nehruvian’ model of development, is shown to have emerged as a dominant network. The other socially powerful ‘Subaltern’ stream (less visible, closer to ground realities and linked to village and cottage industry), adopting the ‘Gandhian’ model of development, still remains dispersed and outside the consideration of high-level decision-making bodies. The paper stresses the importance of moving the support and attention from the dominant stream to efforts that attempt a synthesis between the dominant and the subaltern. (shrink)

Deep Neural Networks (DNNs) are becoming increasingly important as scientific tools, as they excel in various scientific applications beyond what was considered possible. Yet from a certain vantage point, they are nothing but parametrized functions fθ(x)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{f}_{\varvec{\theta }}(\varvec{x})$$\end{document} of some data vector x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{x}$$\end{document}, and their ‘learning’ is nothing but an iterative, algorithmic fitting of the parameters to data. Hence, what could be special about (...) them as a scientific tool or model? I will here suggest an integrated perspective that mediates between extremes, by arguing that what makes DNNs in science special is their ability to develop functional concept proxies (FCPs): Substructures that occasionally provide them with abilities that correspond to those facilitated by concepts in human reasoning. Furthermore, I will argue that this introduces a problem that has so far barely been recognized by practitioners and philosophers alike: That DNNs may succeed on some vast and unwieldy data sets because they develop FCPs for features that are not transparent to human researchers. The resulting breach between scientific success and human understanding I call the ‘Actually Smart Hans Problem’. (shrink)

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 the journals in the STS network are connected by weak ties, about half of the history of science network consists of strong ties. The history of science network is thus more cohesive than the STS network. The relatively strong cohesion within the history of science network is associated with comparatively high degrees of intra-disciplinary communication, but comparatively weak ties to only a few related disciplines. The analysis also shows that very few members of the history of science cliques are situated on the shortest path between both specialties. Moreover, given the relatively impermeable nature of the history of science network, the latter partially depends on STS to reach some of the neighboring disciplines. (shrink)

Research in the social sciences received generous patronage in the late 1960s and early 1970s. Research was widely perceived as providing solutions to emerging social problems. That generosity came under increased contest in the late 1970s. Although these trends held true for all of the social sciences, this essay explores the various ways by which economists in particular reacted to and resisted the patronage cuts that were proposed in the first budgets of the Reagan administration. Economists’ response was three fold: (...) to engage in joint lobbying with other social scientists, to tap into their authority as a respected policy player, and to influence the types of research financed by the patron. With interviews of the former lobbyist for the social scientists, the former director of the Economics program for the National Science Foundation, and a review of the archival records of economists and their scholarly society, we discuss how economists have claimed entitlement to patronage in the closing decades of the twentieth century. We observe a dynamic and productive relationship between politicians and researchers mediated by the National Science Foundation, where civil servants, lobbyist and public minded scientists, and self-serving grantees trade roles. (shrink)

A popular line in philosophy championed by Jackson and his followers analyses concepts as networks of propositions. It takes even network-propositions characterizing ordinary empirically applicable concepts to be a priori, in contrast to statements of empirical science. This is meant to guarantee both the autonomy of conceptual analysis, and its substantial and informative character. It is argued here, to the contrary, that empirically applicable and entrenched concepts owe the acceptability of their own network precisely to its empirical (...) pedigree. Promoting an empirical proposition into a network proposition does not make it ultimately a priori: no matter how entrenched the network is, it owes its ultimate justification to its empirical pedigree. Autonomy from experience and informativeness clash with each other: the stipulative "knowledge" is a priori in a weak sense, but not informative and substantial, the factual knowledge is substantial but not a priori. (shrink)

Using data on the ‘career’ paths of one thousand ‘leading scientists’ from 1450 to 1900, what is conventionally called the ‘rise of modern science’ is mapped as a changing geography of scientific practice in urban networks. Four distinctive networks of scientific practice are identified. A primate network centred on Padua and central and northern Italy in the sixteenth century expands across the Alps to become a polycentric network in the seventeenth century, which in turn dissipates into a (...) weak polycentric network in the eighteenth century. The nineteenth century marks a huge change of scale as a primate network centred on Berlin and dominated by German-speaking universities. These geographies are interpreted as core-producing processes in Wallerstein’s modern world-system; the rise of modern scientific practice is central to the development of structures of knowledge that relate to, but do not mirror, material changes in the system. (shrink)

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and (...) errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia. (shrink)