The Politics of Misinformation is a critical examination of how and why the public has confidence in political progress and innovation even though most change is superficial. Concentrations of social and economic power produce illusions that create the impression of beneficial social change while erasing the possibility of such change. Language, bureaucratic authority, law, political parties, science, and other social institutions help to produce images that mislead both non-elite and elite, creating the appearance of rational democracy while at the same time obscuring structural inequality, discouraging critical evaluation of political policy, and thwarting involvement in democratic politics.
Our common assumption is that the acts of Homo sapiens are basically rational and that mistakes in reaching conclusions are the exception. On the contrary, mistakes are so common that rationality is probably the exception. The Marxist concept of false consciousness, meaning an erroneous assumption about the sources of one’s own thought, applies to the elite as much as to the masses. Political actions influence our well-being continuously and deeply and because they harm us in many instances, perhaps more often than they help us. Comforting illusions that protect us against despair and protect the status quo against effective protests are readily created and disseminated. The illusions are normally believed because it would be hard to live without them. Recent history reaffirms the illusions. They are partly a legacy of the nineteenth century, with its dramatic industrial revolution and its high-minded revolutions in France and in America acclaiming individual liberty and political independence. But the twentieth century, with its world wars, genocides, and other horrors, has been marked by regression rather than progress. The illusions are a fundamental instance of symbolic politics; they build an impression of beneficial social change even while typically erasing the possibility of change.
When sociology emerged as a discipline in the late nineteenth century, the problem of crowds constituted one of its key concerns. It was argued that crowds shook the foundations of society and led individuals into all sorts of irrational behaviour. Yet crowds were not just something to be fought in the street, they also formed a battleground over how sociology should be demarcated from related disciplines, most notably psychology. In The Politics of Crowds, Christian Borch traces sociological debates on crowds and masses from the birth of sociology until today, with a particular focus on the developments in France, Germany and the USA. The book is a refreshing alternative history of sociology and modern society, observed through society’s other, the crowd. Borch shows that the problem of crowds is not just of historical interest: even today the politics of sociology is intertwined with the politics of crowds.
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Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a “classic” approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (“micro”) to a different scale level (“macro”), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.
Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks’ evolution.
Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We ﬁnd that several real networks, such as the WorldWideWeb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.
The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative deﬁnition of community is not implemented in the algorithms, leading to an intrinsic difﬁculty in the interpretation of the results without any additional non-topological information. In this article we deal with this problem by showing how quantitative deﬁnitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identiﬁcation of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artiﬁcial and real-world graphs. In particular, we show how the algorithm applies to a network of scientiﬁc collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems.
Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network.
Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace, using in silico evolved networks.