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The central claim of this work is that the diverse and fast-growing environmental justice movement in the United States embodies the newly emerging concepts and practices of critical pluralism, so this chapter turns to the environmental movement for examples of this in practice. Specifically, it looks at the new network structures and processes that have been adopted by grassroots environmental movements, which make connections, and create solidarity out of an understanding and respect for both differences and similarities, and work from a variety of places with a wide array of tactics. The chapter begins by exploring the value of difference (plurality) in the movement, and continues by examining the bases of the environmental justice movement in a number of pre-existing social and political networks, looking at how these networks link issues and establish alliances among diverse groups, how they form in order to deal with environmental issues of varying dimensions, and how they are structured organizationally. The author also examines some of the reasons why this form of organizing is a tactical strength, as it confronts (mirrors and maps) itself onto changing practices in capital movement and political oversight. Finally, in an initial attempt to evaluate the network form, some of the difficulties in, and criticisms of, networking as a social-movement strategy are explored.

The authors start by pointing out that as institutional forms for the governance of polities on a massive scale in the USA and the EU, networks face serious and potentially disabling challenges in terms of legitimacy. Network forms of governance tend to advantage experts or sophisticated actors who are familiar with all levels of government in federal systems, as opposed to ordinary citizens. To ignore the normative questions about input legitimacy to which network governance gives rise to risks weakening the democratic foundations of the federalist contracts that bind together levels of governance, public, quasi‐public, and private actors, and states and citizens in Europe and America. The argument is presented in four sections: first, an explanation is given of why network forms of governance have proliferated and the legitimacy issues that arise are considered, yielding a set of assessment criteria to be applied in framing analyses of legitimacy in different, real‐world political systems; second, an examination is made of networks in the EU; third, an assessment is offered of network structures in American governance; last, network structures in the two polities are compared and contrasted, highlighting both critical issues and network features with implications for legitimacy, as well as ways in which the democratic deficit that plagues network forms of governance might be closed.

This chapter reviews the consequences of network structure for community robustness in front of perturbations such as species extinctions, habitat loss, and biological invasions. The nested, heterogeneous structure of mutualistic networks allows a higher number of coexisting species, a higher robustness to perturbations, and a higher rate of return to equilibrium. In spatially extended models, such network architecture increases spatial heterogeneity. When looking at species identities, however, there is a phylogenetic signal on network patterns. This implies that coextinction cascades following species extinctions tend to involve phylogenetically similar species, therefore leading to a higher loss of taxonomic diversity and a biased pruning of the evolutionary tree. Regarding the implications of network structure for coevolution, models suggest that even very infrequent pairwise coevolutionary events might trigger evolutionary changes in other species, increasing the cohesiveness of the interaction network.

Mutualisms can involve dozens, even hundreds, of species and this complexity has precluded a serious community-wide approach to plant–animal interactions. The most straightforward way to describe such an interacting community is with a network of interactions. In this approach, species are represented as nodes of two types: plants and animals. This chapter provides the tools and concepts for characterizing mutualistic networks and placing them into a broad context. This serves as a background with which to understand the structure of mutualistic networks. The discussions cover a network approach to complex systems, measures of network structure, models of network buildup, and ecological networks.

This chapter presents a detailed comparative analysis of firms and network structures in Germany and Great Britain. It focuses on the degree of ownership concentration, the different types of owners, and the differences in the configuration of corporate networks, as well as competition and cooperation in the network. The concept of network configuration is explained, and national networks are compared in terms of ownership concentration and the overlap of capital networks and interlocking directorates. The overlap of interlocks and ownership and the mutual reinforcement of bureaucratic power (management) and power conveyed by ownership characterize the specific control structure of German capitalism.

This chapter discusses the structure of mutualistic networks. Despite their apparent complexity, mutualistic networks show repeated, universal structural patterns independent of species composition, size, and other ecological details. First, mutualistic networks are very heterogeneous: whereas the majority of species have only one or a few interactions, a few species are much more connected than expected by chance. Second, mutualistic networks are highly nested, that is, specialists interact with well-defined subsets of the species generalists interact with. Third, mutualistic networks are built on weak, asymmetric interactions among species. Fourth, mutualistic networks have a strong small-world property; that is, they simultaneously have a short path length among any pair of species and a high clustering coefficient. Finally, these networks are significantly modular—that is, there are small groups of species with morphological convergence of traits that interact strongly among themselves and more loosely with species from other modules. These modules can be regarded as the basic building blocks of mutualistic networks and their coevolutionary units.

This part discusses the fourteen extensions of Agent_Zero: endogenous destructive radii; age and impulse control; fight vs. flight; replication of the Latané–Darley experiment; introduction of memory; couplings (entanglement of passion and reason); endogenous dynamics of connection strength; growing the 2011 Arab Spring; jury processes; endogenous dynamics of network structure; multiple social levels; the 18th Brumaire of Agent_Zero; prices and seasonal economic cycles; and mutual escalation spirals. Each of these extensions is explained in detail. In particular, the affective, deliberative, and social components of Agent_Zero are modeled as independent; they all affect disposition and they are entangled. This part also presents examples involving the activation of the yellow spatial sites as well as violent occupation by Blue Agent_Zero actors.

This chapter quantifies the dynamics of a crossword puzzle by using a network structure to model it. Specifically, the chapter determines how the interaction between the structure of cells in the puzzle and the difficulty of the clues affects the puzzle's solvability. It first builds an iterative stochastic process that exactly describes the solution and obtains its deterministic approximation, which gives a very simple fixed-point equation to solve for the final solution proportion. The chapter then shows via simulation on actual crosswords from the Sunday edition of The New York Times that certain network properties inherent to actual crossword networks are important predictors of the final solution size of the puzzle.

A persistent challenge in evolutionary biology has been understanding how coevolution has produced complex webs of interacting species. This chapter investigates the suite of ecological, evolutionary, and coevolutionary mechanisms responsible for generating such network patterns. Although some predictors of a species position in a network of interactions have been studied—mainly species abundance and forbidden links—we are moving toward an integrative approach, where several variables are tested simultaneously within the framework of phylogenetically independent contrasts. This can help us quantify the relative contribution of different factors given that they are often correlated. Body size, phenological spread, regional abundance, and species abundance are significantly correlated with a species position in the network of interactions in the frugivore set. There is a phylogenetic signal in the position of a species in the network (e.g., its degree). Beyond this, the phylogenetic patterns of shared ancestry also play a key role in explaining the overall pattern of mutualistic associations between the two sets of species. This pattern of interaction is influenced mainly by the evolutionary history of the plants.

A network is, in its simplest form, a collection of points joined together in pairs by lines. In the jargon of the field the points are referred to as vertices or nodes and the lines are referred to as edges. Many objects of interest in the physical, biological, and social sciences can be thought of as networks and, as this book aims to show, thinking of them in this way can often lead to new and useful insights. This introductory chapter discusses why we are interested in networks and describes some specific networks of note. An overview of the subsequent chapters is presented.

Social networks are networks in which the vertices are people, or sometimes groups of people, and the edges represent some form of social interaction between them, such as friendship. This chapter begins with a short summary of the origins and research focus of the field of social networks. It then describes some of the techniques used to discover social network structure, including interviews and questionnaires, direct observation, and data from archival or third-party records.

This chapter aims to strengthen the empirical and conceptual understanding of the links between civic participation and the social network structure of social capital. It provides an analysis of original survey data that uses a position generator instrument, and considers some previous research.

One of the reasons for the large investment the scientific community has made in the study of social networks is their connection with the spread of disease. Diseases spread over networks of contacts between individuals: airborne diseases like influenza or tuberculosis are communicated when two people breathe the air in the same room; contagious diseases and parasites can be communicated when people touch; HIV and other sexually transmitted diseases are communicated when people have sex. The patterns of such contacts can be represented as networks and a good deal of effort has been devoted to empirical studies of these networks' structure. This chapter looks at the connections between network structure and disease dynamics and at mathematical theories that allow us to understand and predict the outcomes of epidemics. Exercises are provided at the end of the chapter.

New techniques for modelling social networks go beyond basic network visualization and node/graph-level measurements, and provide methods for testing the statistical significance of relationships between observed and hypothesized social network structures. This chapter focuses on the use of these methods for modelling exchange-based relationships that existed in the Kuril Islands of Far Eastern Russia in the North Pacific Ocean, where foraging groups lived for more than 4,000 years in an isolated environment prone to stochastic natural events and resource unpredictability.

People constantly interact with each other and their environment, and these interactions—with whom and with what they interact—are not random. Interactions at multiple levels (cellular, neurological, social, physical, environmental) shape one’s experiences and affect health and well-being. These interactions can be represented as a set of networks that feedback and influence other networks. Here we limit our scope to the complex relationship between human social networks and behavior, which frequently forms a feedback loop, and the effect of this relationship on population health outcomes. This chapter introduces traditional network analysis as it pertains to population health, explores examples of interactions between macro-level networks, and proposes future directions for network analysis