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Analysing Networks of Networks
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Abstract
We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors' social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.
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The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor‐attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all‐male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia.
multiplex is a computer program that provides algebraic tools for the analysis of multiple network structures within the R environment. Apart from the possibility to create and manipulate multivariate data representing multiplex, signed, and two-mode networks, this package offers a collection of functions that deal with algebraic systems - such as the partially ordered semigroup, and balance or cluster semirings - their decomposition, and the enumeration of bundle patterns occurring at different levels of the network. Moreover, through Galois derivations between families of the pairs of subsets in different domains it is possible to analyze affiliation networks with an algebraic approach. Visualization of multigraphs, different forms of bipartite graphs, inclusion lattices, Cayley graphs is supported as well with related packages.
This paper proposes a mixed-method sociosemantic network analysis of meaning structures in practice. While social and institutional fields impose meaning structures , to achieve practical goals, field participants gather in groups and locally produce idiocultures of their own. Such idiocultures are difficult to capture structurally; hence, the impact of practice on meaning structures is underrated. To account for this impact, we automatically map local meaning structures-ensembles of semantic associations embedded in specific social groups-to identify the focal elements of these meaning structures, and qualitatively examine contextual usage of such elements. Employing a combination of ethnographic and social network data on two St. Petersburg art collectives, we find the seemingly field-imposed meaning structures to be instantiated differently, depending on group practice. Moreover, we find meaning structures to emerge from group practice and even change the field-wide meaning structures.
There is a tendency to consider covert networks as separate from overt networks. Drawing on data from the Democratic Republic of the Congo, we demonstrate that this is not the case and identify how covert and overt networks are mutually constitutive. While most studies of African brokers have relied on network metaphors like ‘Big Men’ and ‘social membranes’, we consider the embeddedness of ‘covert’ networks in ‘overt’ networks explicitly. We perform two analyses on a large original dataset encompassing 396 partially overlapping ego-nets obtained from a hybrid link-tracing design. An ego-net analysis reveals a large degree of homophily and a deep embeddedness of the different networks. A multilevel exponential random graph model fitted to the reconstructed network of a 110-node subset shows that demobilised combatants are the actors likely to broker between armed groups, state forces, and civilian blocs, suggesting their capacity to broker peace or foment war.
Complex social-ecological interactions underpin many environmental problems. To help capture this complexity, we advance an interdisciplinary network modeling framework to identify important relationships between people and nature that can influence environmental conditions. Drawing on comprehensive social and ecological data from five coral reef fishing communities in Kenya; including interviews with 648 fishers, underwater visual census data of reef ecosystem condition, and time-series landings data; we show that positive ecological conditions are associated with ‘social-ecological network closure’ – i.e., fully linked and thus closed network structures between social actors and ecological resources. Our results suggest that when fishers facing common dilemmas form cooperative communication ties with direct resource competitors, they may achieve positive gains in reef fish biomass and functional richness. Our work provides key empirical insight to a growing body of research on social-ecological alignment, and helps to advance an integrative framework that can be applied empirically in different social-ecological contexts.
When people engage in a first pro-environmental behaviour (PEB1; for example, conserving energy at home), are they more or less likely (positive and negative spillover, respectively) to engage in other pro-environmental behaviours (‘PEB2’; for example, conserving water at home)? We examined evidence for spillover using a meta-analysis of interventions. We coded 22 studies and unpublished data that fulfilled the following criteria: used experimental or quasi-experimental design, showed change in a PEB1 and measured at least one PEB2. Analysis of the 77 effect sizes found in these studies showed that the overall spillover from a PEB1 was positive, though small, on the intention to perform a PEB2 (pooled mean effect size estimate d+ = 0.17). However, the spillover
effect was negative and small for actual behaviour (d+= −0.03) and policy support (d+ = −0.01) for PEB2. Positive spillover was most likely when interventions targeted intrinsic motivation and when PEB1 and PEB2 were similar. Future research in the area should target and measure spillover processes, collect larger samples and statistically test for spillover in more consistent ways.
This paper elaborates on two theories of social influence processes to multiplex network structures. First, cohesion influence is based on mutual communication made by different types of relations, and second comparison influence that is built on contrasting types of tie. While a system of bundles with a mutual character constitutes the setting for a multiplex network exposure measure within cohesion, comparison influence is defined algebraically through classes of actors in terms of a weakly balanced semiring structure that considers positive, negative, and also ambivalent types of tie. A case study with these approaches is made on an entrepreneurial community network with formal business relations, informal friendship ties, and perceived competition among the firms, and the methods are validated with the Sampson Monastery data set.
Social relations are multiplex by nature: actors in a group are tied together by various types of relationships. To understand and explain group processes it is, therefore, important to study multiple social networks simultaneously in a given group. However, with multiplexity the complexity of data also increases. Although some multivariate network methods (e.g. Exponential Random Graph Models, Stochastic Actor-oriented Models) allow to jointly analyze multiple networks, modeling becomes complicated when it focuses on more than a few (2–4) network dimensions. In such cases, dimension reduction methods are called for to obtain a manageable set of variables. Drawing on existing statistical methods and measures, we propose a procedure to reduce the dimensions of multiplex network data measured in multiple groups. We achieve this by clustering the networks using their pairwise similarities, and constructing composite network measures as combinations of the networks in each resulting cluster. The procedure is demonstrated on a dataset of 21 interpersonal network dimensions in 18 Hungarian high-school classrooms. The results indicate that the network items organize into three well-interpretable clusters: positive, negative, and social role attributions. We show that the composite networks defined on these three relationship groups overlap but do not fully coincide with the network measures most often used in adolescent research, such as friendship and dislike.
When environmental processes cut across socioeconomic boundaries, traditional top-down government approaches struggle to effectively manage and conserve ecosystems. In such cases, governance arrangements that foster multiactor collaboration are needed. The effectiveness of such arrangements, however, depends on how well any ecological interdependencies across governed ecosystems are aligned with patterns of collaboration. This inherent interdisciplinary and complex problem has impeded progress in developing a better understanding of how to govern ecosystems for conservation in an increasingly interconnected world. We argue for the development of empirically informed theories, which are not only able to transcend disciplinary boundaries, but are also explicit in taking these complex social-ecological interdependences into account. We show how this emerging research frontier can be significantly improved by incorporating recent advances in stochastic modeling of multilevel social networks. An empirical case study from an agricultural landscape in Madagascar is reanalyzed to demonstrate these improvements.
Analytical modeling of customer preferences in product design is inherently difficult as it faces challenges in modeling heterogeneous human behavior and product offerings. In this paper, the customer-product interactions are viewed as a complex socio-technical system and analyzed using social network theory and techniques. We propose a Multidimensional Customer-Product Network (MCPN) framework, where separate networks of “customers” and “products” are simultaneously modeled, and multiple types of relations, such as consideration and purchase, product associations, and customer social networks are considered. We start with the simplest unimodal network configuration where customer cross-shopping behaviors and product similarities are analyzed to inform designers about the implied product competition, market segmentation, and product positions in the market. We then progressively extend the network to a multidimensional structure that integrates customer preference decisions with product feature similarities to enable the modeling of preference heterogeneity, product association and decision dependency. Finally, social influences on new product adoption are analyzed in the same framework by introducing customer-customer relations together with other product-product and customer-product relations. Beyond the traditional network descriptive analysis, we employ the Exponential Random Graph Model (ERGM) as a unified statistical inference framework for analyzing multiple relations in MCPN to support engineering design decisions. Our approach broadens the traditional utility-based logit approaches by considering the dependency among product choices and the “irrationality” of customer behavior induced by social influence. While this paper is focused on presenting the conceptual framework of the proposed methodology, examples on customer vehicle preferences are presented to illustrate the progressive development of the MCPN framework from a simple unimodal configuration to a complex multidimensional structure.
Multigraphs are graphs where multiple edges and edge loops are permitted. The main purpose of this article is to show the versatility of a multigraph approach when analysing social networks. Multigraph data structures are described and it is exemplified how they naturally occur in many contexts but also how they can be constructed by different kinds of aggregation in graphs. Special attention is given to a random multigraph model based on independent edge assignments to sites of vertex pairs and some useful measures of the local and global structure under this model are presented. Further, it is shown how some general measures of simplicity and complexity of multigraphs are easily handled under the presented model. © 2015, Japan Society of Histological Documentation. All rights reserved.
This article explores the theoretical implications of developing multidimensional social networks that include nonhuman technological elements. Using ideas from actor-network theory and sociomateriality, we develop a typology for multidimensional networks that includes multiple kinds of nodes and multiple kinds of relations. This typology includes traditional types of nodes, like people, and traditional types of relations, like “shares information with,” along with types of nodes that are technological artifacts, like databases, and types of nonhuman relations, like embodiment. In this way, technology is moved inside the social network and becomes an inherent part of it. An illustrative case shows how the inclusion of nonhuman artifacts and relations in the networks of an automobile design firm significantly changes our understanding of the emergent dynamics in this sociomaterial network. These results are extended by an exploration of how to develop multidimensional, multitheoretical, and multilevel models that include technological artifacts and relations.
We study the evolution of the network of foreign direct investment (FDI)
in the international electricity industry during the period 1994-2003.
We assume that the ties in the network of investment relations between
countries are created and deleted in continuous time, according to a
conditional Gibbs distribution. This assumption allows us to take
simultaneously into account the aggregate predictions of the
well-established gravity model of international trade as well as local
dependencies between network ties connecting the countries in our
sample. According to the modified version of the gravity model that we
specify, the probability of observing an investment tie between two
countries depends on the mass of the economies involved, their physical
distance, and the tendency of the network to self-organize into local
configurations of network ties. While the limiting distribution of the
data generating process is an exponential random graph model, we do not
assume the system to be in equilibrium. We find evidence of the effects
of the standard gravity model of international trade on evolution of the
global FDI network. However, we also provide evidence of significant
dyadic and extra-dyadic dependencies between investment ties that are
typically ignored in available research. We show that local dependencies
between national electricity industries are sufficient for explaining
global properties of the network of foreign direct investments. We also
show, however, that network dependencies vary significantly over time
giving rise to a time-heterogeneous localized process of network
evolution.
Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (0125 and 0120) to demonstrate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.
This paper presents and tests a formal mathematical model for the analysis of informant responses to systematic interview questions. We assume a situation in which the ethnographer does not know how much each informant knows about the cultural domain under consideration nor the answers to the questions. The model simultaneously provides an estimate of the cultural competence or knowledge of each informant and an estimate of the correct answer to each question asked of the informant. The model currently handles true-false, multiple-choice, andfill-in-the-blank type question formats. In familiar cultural domains the model produces good results from as few as four informants. The paper includes a table showing the number of informants needed to provide stated levels of confidence given the mean level of knowledge among the informants. Implications are discussed.
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Standard models of diffusion assume spatial and temporal homogeneity. This article develops a class of diffusion models that incorporate spatial and temporal heterogeneity by turning to the individual level within an event-history framework. These models permit the analyst to represent social structural relations thought to channel diffusion, and to model decay in the influence of events over time. Heterogeneous diffusion models are applied to a reanalysis of data reported in Coleman, Katz, and Menzel's classic diffusion study. Network centrality and local structures of influence based on cohesive relations and structural equivalence are all shown to channel the diffusion of tetracycline.
This paper attempts to further theoretical and empirical understanding of adherent and constituent mobilization by proposing and analyzing frame alignment as a conceptual bridge linking social psychological and resource mobilization views on movement participation. Extension of Goffman's (1974) frame analytic perspective provides the conceptual/theoretical framework; field research on two religious movements, the peace movement, and several neighborhood movements provide the primary empirical base. Four frame alignment processes are identified and elaborated: frame bridging, frame amplification, frame extension, and frame transformation. The basic underlying premise is that frame alignment, of one variety or another, is a necessary condition for participation, whatever its nature or intensity, and that it is typically an interactional and ongoing accomplishment. The paper concludes with an elaboration of several sets of theoretical and research implications.
Sociologists have paid a great deal of attention to the idea that many aspects of human life are socially constructed. However, there has been far less attention to the concrete interactional processes by which this construction occurs. In particular, scholars have neglected how consensual meaning is constructed in verbal interaction. This article outlines nine generic construction tools used in everyday talk, based on a review and synthesis of past work. These tools fall into three general categories: building blocks, linking devices, and finishing devices. The authors argue that scholars must pay greater attention to the interactional nature of social construction, and discuss three interactional processes that are central to the social construction of meaning in talk: challenge, support, and non-response. The article presents concrete illustrations of these processes using examples from focus group discussions about gender and violence. These micro-interactional processes often reproduce, sometimes modify, and (more rarely) resist larger institutions and structures, and thus are indispensable to understanding social life.
Networks of several distinct types of social tie are aggregated by a dual model that partitions a population while simultaneously identifying patterns of relations. Concepts and algorithms are demonstrated in five case studies involving up to 100 persons and up to eight types of tie, over as many as 15 time periods. In each case the model identifies a concrete social structure. Role and position concepts are then identified and interpreted in terms of these new models of concrete social structure. Part II, to be published in the May issue of this Journal (Boorman and White 1976), will show how the operational meaning of role structures in small populations can be generated from the sociometric blockmodels of Part I.
In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions.
The acquisition of knowledge, the development of the individual's knowledge bases, does not occur in a vacuum. The social context, both social structure (the perceived regularities in the social network) and social knowledge, appears to be related to the individual's acquisition of knowledge. In this article, the relationship between the social world and individual knowledge acquisition is explored. A general theory is postulated and then formulated as a model. The basic theory is predicated on the assumption that social interaction is the driving force behind knowledge acquisition.A selection of results pertaining to social learning will be presented. These include, but are not limited to the following:•
Social structure affects knowledge acquisition; e.g., individuals in tightly knit social groups develop similar cognitive structures, but not necessarily similar evaluations of those structures.
•
Social knowledge evolves slowly and relative to shared experience.
•
Individual knowledge acquisition is limited; e.g., individuals and groups talk past each other because of the lack of critical paths in their respective knowledge bases.
A widely used approach to modeling discrete‐time network data assumes that discrete‐time network data are generated by an unobserved continuous‐time Markov process. While such models can capture a wide range of network phenomena and are popular in social network analysis, the models are based on the homogeneity assumption that all nodes share the same parameters. We remove the homogeneity assumption by allowing nodes to belong to unobserved subsets of nodes, called blocks, and assuming that nodes in the same block have the same parameters, whereas nodes in distinct blocks have distinct parameters. The resulting models capture unobserved heterogeneity across nodes and admit model‐based clustering of nodes based on network properties chosen by researchers. We develop Bayesian data‐augmentation methods and apply them to discrete‐time observations of an ownership network of non‐financial companies in Slovenia in its critical transition from a socialist economy to a market economy. We detect a small subset of shadow‐financial companies that outpaces others in terms of the rate of change and the desire to accumulate stocks of other companies.
In this paper, I argue that we can better understand the relationship between social structure and materiality by combining qualitative analysis of practices in shared physical space with statistical analysis. Drawing on the two-mode approach, I treat social and material structures together with the relationship between them as a two-level socio-material network. In a mixed method study, formalized ethnographic data on such networks in five European artistic collectives are subjected to multilevel exponential random graph modelling. It sheds light on how different kinds of interpersonal ties condition the engagement of individuals with similar materiality over time.
This paper explores the relations between social ties and cultural constructs in small groups. The analysis uses cross-sectional data comprising both social networks within three art groups and semantic networks based on verbal expressions of their members. We examine how positions of actors in the intragroup social networks associate with the properties of cultural constructs they create jointly with other group members accounting for different roles actors play in collective culture constructing. We find that social popularity rather hinders sharing of cultural concepts, while those individuals who socially bridge their groups come to share many concepts with others. Moreover, focusing and, especially, integration of cultural constructs, rather than mere ‘thickness’ of those, accompany intense interactions between the leaders and the followers.
Breakthroughs have been made in algorithmic approaches to understanding how individuals in a group influence each other to reach a consensus. However, what happens to the group consensus if it depends on several statements, one of which is proven false? Here, we show how the existence of logical constraints on beliefs affect the collective convergence to a shared belief system and, in contrast, how an idiosyncratic set of arbitrarily linked beliefs held by a few may become held by many. © 2016, American Association for the Advancement of Science. All rights reserved.
Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.
The chapter presents a blockmodeling analysis of multilevel (inter-individual and inter-organizational) networks. Several approaches are presented and used to blockmodel such networks. Each blockmodel represents a system of roles (White HC, Boorman SA, Breiger RL, Am J Sociol 81:730–780, 1976) and therefore a form of division of work that is likely to change over time in fields of organized collective action. Using a case study, we show that while the systems of roles are quite similar at both levels (core-periphery-like structures with bridging cores interpreted in terms of division of work between actors’ and organizations’ specialties, location, status, etc.), the roles are performed by units with different characteristics at different levels. The added value of this true multilevel analysis is to show how groups at different levels are connected. In the empirical case analyzed in the chapter, the division of work at the level of individuals and the division of work at the level of laboratories can complement and strengthen each other for some segments of the population, while this reinforcement does not work for other segments. For the same roles, the mix of specialties at one level is different from the mix of specialties at the other level, notably because the two levels do not manage the same resources. Thus, this analysis tracks the meeting of top-down and bottom-up pressures towards structural alignment between levels.
Article Outline: Glossary Definition of the Subject Introduction Algebras from Networks Algebraic Structure Algebraic Analysis Algebraic and Network Mappings Examples Future Directions Bibliography © 2012 Springer Science+Business Media, LLC. All rights reserved.
We describe a methodology to examine bipartite relational data structures as exemplified in networks of corporate interlocking. These structures can be represented as bipartite graphs of directors and companies, but direct comparison of empirical datasets is often problematic because graphs have different numbers of nodes and different densities. We compare empirical bipartite graphs to simulated random graph distributions conditional on constraints implicit in the observed datasets. We examine bipartite graphs directly, rather than simply converting them to two 1-mode graphs, allowing investigation of bipartite statistics important to connection redundancy and bipartite connectivity. We introduce a new bipartite clustering coefficient that measures tendencies for localized bipartite cycles. This coefficient can be interpreted as an indicator of inter-company and inter-director closeness; but high levels of bipartite clustering have a cost for long range connectivity. We also investigate degree distributions, path lengths, and counts of localized subgraphs. Using this new approach, we compare global structural properties of US and Australian interlocking company directors. By comparing observed statistics against those from the simulations, we assess how the observed graphs are structured, and make comparisons between them relative to the simulated graph distributions. We conclude that the two networks share many similarities and some differences. Notably, both structures tend to be influenced by the clustering of directors on boards, more than by the accumulation of board seats by individual directors; that shared multiple board memberships (multiple interlocks) are an important feature of both infrastructures, detracting from global connectivity (but more so in the Australian case); and that company structural power may be relatively more diffuse in the US structure than in Australia.
The strength of weak ties is that they tend to be long - they connect socially distant locations, allowing information to diffuse rapidly. The authors test whether this "strength of weak ties" generalizes from simple to complex contagions. Complex contagions require social affirmation from multiple sources. Examples include the spread of high-risk social movements, avant garde fashions, and unproven technologies. Results show that as adoption thresholds increase, long ties can impede diffusion. Complex contagions depend primarily on the width of the bridges across a network, not just their length. Wide bridges are a characteristic feature of many spatial networks, which may account in part for the widely observed tendency for social movements to diffuse spatially.
A central part of relational ties between social actors is constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards.
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Mead's conception of the self is analyzed as an internal interaction process, with the “I” viewed as taking perspectives rather than being simply a biologic response. Conceptualizing the self as an intrapersonal process of interaction from differentiated perspectives permits a better understanding of false fronting, autonomy, and creativity. Emergence theory allows us to take a Phenomenological view of the self. Several levels of consciousness in Mead's theory are analyzed, and connections with Berger and Luckmann's conceptions of objectivation and the symbolic universe are suggested.
Contemporary environmental challenges call for new research approaches that include the human dimension when studying the natural environment. In spite of the recent development of several conceptual frameworks integrating human society with nature, there has been less methodological and theoretical progress on how to quantitatively study such social–ecological interdependencies. We propose a novel theoretical framework for addressing this gap that partly builds on the rapidly growing interdisciplinary research on complex networks. The framework makes it possible to unpack, define and formalize ways in which societies and nature are interdependent, and to empirically link this to specific governance challenges and opportunities using a range of theories from both the social and natural sciences in an integrated way. At the core of the framework is a set of basic building blocks (motifs) that each represents a simplified but non-trivial social–ecologicalsystems (SES) consisting of two social actors and two ecological resources. The set represents all possible patterns of interdependency in a SES. Each unique motif is characterized in terms of social and ecological connectivity, resource sharing, and resource substitutability. By aligning theoretical insights related to the management of common-pool resources, metapopulation dynamics, and the problem of fit in SES with the set of motifs, we demonstrate the multi-theoretical ability of the framework in a case study of a rural agricultural landscape in southern Madagascar. Several mechanisms explaining the inhabitants’ demonstrated ability to preserve their scattered forest patches in spite of strong pressures on land and forest resources are presented.
Two classes of network models are used to reanalyze a sociological classic often cited as evidence of social contagion in the diffusion of technological innovation: Medical Innovation. Debate between the cohesion and structural equivalence models poses the following question for study: Did the physicians resolve the uncertainty of adopting the new drug through conversations with colleagues (cohesion) or through their perception of the action proper for an occupant of their position in the social structure of colleagues (structural equivalence)? The alternative models are defined, compared, and tested. Four conclusions are drawn: (a) Contagion was not the dominant factor driving tetracyclene's diffusion. Where there is evidence of contagion, there is evidence of personal preferences at work.
Three‐way social network data occurs when every actor in a social network generates a digraph of the entire network. This paper presents a statistical model based on cultural consensus analysis for aggregating these separate digraphs into a single consensus digraph. In addition, the model allows estimation of separate hit and false alarm rates for each actor that can vary within each actor in different regions of the digraph. Several standard signal detection models are used to interpret the hit and false alarm parameters in terms of knowledge and response bias. A published three‐way data set by Kumbasar, Romney, and Batchelder (American Journal of Sociology, 1994) is analyzed, and the model reveals that both response bias and knowledge decrease with distance from ego.
George H. Mead and Alfred Schutz proposed foundations for an interpretative sociology from opposite standpoints. Mead accepted
the objective meaning structure a priori. His problem became therefore the explanation of the individuality and creativity
of human actors in his social behavioristic approach. In contrast, Schutz started from the subjective consciousness of an
isolated actor as a result of a phenomenological reduction. He was concerned with the problem of explaining the possibility
of this isolated actor’s perceiving other actors in their existence, their concreteness, and the motives for their behavior.
I treat these two approaches and their associated problems as equally relevant. My evaluation is based on their success in
solving their specific problems. The aim is to decide which of the two approaches provides the more adequate foundation for
an interpretative sociology.
A general model is presented for homogeneous, dichotomous items when the answer key is not known a priori. The model is structurally related to the two-class latent structure model with the roles of respondents and items interchanged. For very small sets of respondents, iterative maximum likelihood estimates of the parameters can be obtained by existing methods. For other situations, new estimation methods are developed and assessed with Monte Carlo data. The answer key can be accurately reconstructed with relatively small sets of respondents. The model is useful when a researcher wants to study objectively the knowledge possessed by members of a culturally coherent group that the researcher is not a member of.