Interdependent policy instrument preferences: a two-mode network approach

In policymaking, actors are likely to take the preferences of others into account when strategically positioning themselves. However, there is a lack of research that conceives of policy preferences as an interdependent system. In order to analyse interdependencies, we link actors to their policy preferences in water protection, which results in an actor-instrument network. As actors exhibit multiple preferences, a complex two-mode network between actors and policies emerges. We analyse whether actors exhibit interdependent preference profiles given shared policy objectives or social interactions among them. By fitting an exponential random graph model to the actor-instrument network, we find considerable clustering, meaning that actors tend to exhibit preferences for multiple policy instruments in common. Actors tend to exhibit interdependent policy preferences when they are interconnected, that is, they collaborate with each other. By contrast, actors are less likely to share policy preferences when a conflict line divides them

Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology

This paper studies multilateral cooperation networks among organizations and work on a two-mode representation to study the decision to participate in a consortium. Our objective is to explain the underlying processes that give rise to multilateral collaboration networks. Particularly, we are interested in how heterogeneity in organizations' attributes plays a part and in the geographical dimension of this formation process. We use the data on project proposals submitted to the 7th Framework Program (FP) in the area of Life sciences, Biotechnology and Biochemistry for Sustainable Non-Food. We employ exponential random graph models (p* models) (Frank and Strauss, 1986 ; Wasserman and Pattison, 1996) with node attributes (Agneessens et al., 2004), and we make use of extensions for affiliation networks (Wang et al., 2009). These models do not only enable handling variability in consortium sizes but also relax the assumption on tie/triad independence. We obtained some preliminary results indicating institutional types as a source of heterogeneity affecting participation decisions. Also, these initial results point out that organizations take their potential partners' participations in other projects into account in giving their decision ; organizations located in the core European countries tend to participate in the same project ; the tendency to preserve the composition of a consortium across projects and the tendency of organizations with the same institutional type to co-participate are not significant

A statistical model for brain networks inferred from large-scale electrophysiological signals

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modeling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. We adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. We validated this approach in a dataset of 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. Synthetic networks generated by this model configuration replicated the characteristic differences found in brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. These findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

Building models for social space: neighourhood-based models for social networks and affiliation structures

We propose a quantitative relational framework for social space. We suggest that social space cannot be specified simply in geographical, network or sociocultural terms but, rather, requires an understanding of the interdependence of relationships among different types of social entities, such as persons, groups, sociocultural resources and places. We also suggest that social space cannot be regarded as fixed: unlike the Euclidean space of Newtonian mechanics, social space is constructed, at least in part, by the social processes that it supports. In the general stochastic relational framework that we propose, relationships among social entities are regarded as the fundamental elements of social space and observed relational entities are viewed as the outcome of processes that occur in overlapping local relational neighbourhoods. Each neighbourhood corresponds to a subset of possible relational entities and is conceived as a possible site of social interaction. We show how special cases of this framework yield hierarchies of models for social networks and for affiliation structures. We also sketch some next steps in the development of this framework.Nous proposons un cadre pour une analyse quantitative relationnelle de l’espace social. Nous suggérons que l’espace social ne peut pas être défini simplement en termes géographiques ou socio-culturels mais que cette définition suppose de comprendre l’interdépendance entre différents types d’entités sociales telles que des personnes, des groupes, des ressources et des positions socio-culturelles. Nous suggérons également que l’espace social ne peut pas être vu comme figé : à la différence de l’espace euclidien de la mécanique newtonienne, l’espace social est construit au moins en partie par le processus social dont il est le support. Dans le modèle stochastique général que nous proposons, les relations entre entités sociales sont considérées comme les éléments fonda-mentaux de l’espace social et les échanges observés sont conçus comme les produits de processus qui agissent dans des voisinages relationnels qui se recouvrent. Chaque voisi-nage correspond à un ensemble d’entités rela-tionnelles et est conçu comme un lieu d’interactions sociales. Nous montrons comment des spécifications particulières de ce cadre théorique produisent des hiérarchies de modèles pour les réseaux sociaux et pour les structures d’affiliation. Nous évoquons également de futurs développements de ce cadre

The analysis of multilevel networks in organizations: models and empirical tests

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordStudies of social networks in organizations confront analytical challenges posed by the multilevel effects of hierarchical relations between organizational subunits on the presence or absence of informal network relations among organizational members. Conventional multilevel models may be usefully adopted to control for generic forms of non-independence between tie variables defined at multiple levels of analysis. Such models, however, are unable to identify the specific multilevel dependence mechanisms generating the observed network data. This is the basic difference between multilevel analysis of networks and the analysis of multilevel networks. The aim of this article is to show how recently derived multilevel exponential random graph models (MERGMs) may be specified and estimated to address the problems posed by the analysis of multilevel networks in organizations. We illustrate our methodological proposal using data on hierarchical subordination and informal communication relations between top managers in a multiunit industrial group. We discuss the implications of our results in the broader context of current theories of organizations as connected multilevel systems.Swiss National Science Foundation (SNSF

Policy Coordination in an Ecology of Water Management Games

Policy outcomes in all but the simplest policy systems emerge from a complex of ecology of games featuring multiple actors, policy institutions, and issues, and not just single policies operating in isolation. This paper updates Long\u27s (1958) ecology of games framework with Scharpf\u27s (1997) actor-centered institutionalism to analyze the coordinating roles of actors and institutions on the context of the ecology of water management games in the San Francisco Bay. Actors participating in multiple institutions are analyzed using exponential random graph models for bipartite networks representing different assumptions about policy behavior, including geographic constraints. We find that policy coordination is facilitated mostly by Federal and state agencies, and collaborative institutions that span across geographic boundaries. Network configurations associated with closure show the most significant departures from the predicted model values, consistent with the Berardo and Scholz (2010) risk hypothesis that closure is important for solving cooperation problems

Acquisition Risks in a World of Joint Capabilities: Evaluating Complex Configurations

Naval Postgraduate School Acquisition Research Progra