Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.
The gains achieved by the reengineering of the planet have not been without costs, and as we enter the twenty-first century these costs are mounting rapidly. It is now widely apparent that humanity’s use of the biosphere is not sustainable. Human-induced climate change is the best-known environmental threat but it is just one of a long list of challenges that already take a severe toll on people and the planet. Some 10 to 20 percent of dryland ecosystems are degraded and unable to meet the needs of people living in them; most marine fisheries are either on the verge of overharvesting or have already collapsed; billions of people face problems of water scarcity and poor water quality; and more than half of the world’s ecosystem services (such as benefits ecosystems provide by purifying water, protecting coasts from storms, or helping to lessen the magnitude of floods) have been degraded. What has gone wrong, and how can it be fixed? Without doubt, a part of the problem is that the human species is living beyond its means on a planet with finite resources: demand is out of balance with supply. Unless we use resources far more efficiently (and produce much less pollution) we will continue to erode the resource base on which our survival ultimately depends.
This ESDN Quarterly Report (QR) provides a condensed overview of the concept of resilience. Despite the complexity of the theory behind resilience, this QR tries to communicate the main notions behind this concept in a way that is understandable and not overly technical. The intention of this QR is to serve as a guide through the concept of resilience. The report does not aim at being exhaustive but intends to provide an overview on the links which are particularly relevant for sustainable development (SD) in general and SD governance in particular. A multitude of diverse sources have been used, mainly from the academic literature. It has to be mentioned the significant and decisive role that the Resilience Alliance has in providing extensive knowledge: the website that they are running, is an exceptionally good source of information for those who are interested and want to deepen their knowledge of resilience. Additionally, among all the scientific publications cited throughout the report, a special mention goes to the book by Walker and Salt (2006) entitled “Resilience thinking: sustaining ecosystems and people in a changing world”, which is very much suggested as a practical source of information on resilience.
Alternative agricultural systems that emphasize ecological and community resilience provide a bridge between traditional agriculture and natural resource management. These can be referred to as agri-ecological systems and include systems such as Organic Agriculture, Biodynamics, Community Supported Agriculture (CSA), Permaculture, Farmers Markets and Community Gardens. This paper reports on current research by the author to explore a range of these systems and how they contribute to agri-ecological and community resilience. For example, resiliency can be seen as a system’s ability to adapt and respond to external impacts on a system, and farmers markets show resiliency to sudden market changes (such as price or consumer preferences toward organics, through direct sale and the involvement of a range of consumers and producers offering a broad range of organic produce). That is, this paper reviews these alternative approaches to food production in relation to key concepts from ecological systems thinking, such as ecological resilience, biodiversity and holism. More specifically, the paper explores how agri-ecological systems contribute to more sustainable and resilient communities, through community development processes such as relationship building, genuine participation, inclusiveness, resource mobilization and creating space for knowledge sharing. The paper concludes by comparing ecological systems models to agri-ecological systems, and suggests how ecological systems theories and concepts might contribute to thinking about the future of community-based agri-ecological resilience.
Panarchy provides a heuristic to characterize the cross-scale dynamics of social-ecological systems and a framework for how governance institutions should behave to be compatible with the ecosystems they manage. Managing for resilience will likely require reform of law to account for the dynamics of social-ecological systems and achieve a substantive mandate that accommodates the need for adaptation. In this paper, we suggest expansive legal reform by identifying the principles of reflexive law as a possible mechanism for achieving a shift to resilience-based governance and leveraging cross-scale dynamics to provide resilience-based responses to increasingly challenging environmental conditions.
Complex systems science has contributed to our understanding of ecology in important areas such as food webs, patch dynamics and population fluctuations. This has been achieved through the use of simple measures that can capture the difference between order and disorder and simple models with local interactions that can generate surprising behaviour at larger scales. However, close examination reveals that commonly applied definitions of complexity fail to accommodate some key features of ecological systems, a fact that will limit the contribution of complex systems science to ecology. We highlight these features of ecological complexity—such as diversity, cross-scale interactions, memory and environmental variability—that continue to challenge classical complex systems science. Further advances in these areas will be necessary before complex systems science can be widely applied to understand the dynamics of ecological systems.
It is a common observation that complex systems have a nested or hierarchical structure: they consist of subsystems, which themselves consist of subsystems, and so on, until the simplest components we know, elementary particles. It is also generally accepted that the simpler, smaller components appeared before the more complex, composite systems. Thus, evolution tends to produce more complex systems, gradually adding more levels to the hierarchy. For example, elementary particles evolved subsequently into atoms, molecules, cells, multicellular organisms, and societies of organisms. These discrete steps, characterized by the emergence of a higher level of complexity, may be called “evolutionary transitions“. The logic behind this sequential complexification appears obvious: you can only build a higher order system from simpler systems after these building blocks have evolved themselves. The issue becomes more complicated when you start looking for the precise mechanisms behind these evolutionary transitions, and try to understand which levels have appeared at what moment, and why.
Regime shifts from one ecological state to another are often portrayed as sudden, dramatic, and difficult to reverse. Yet many regime shifts unfold slowly and imperceptibly after a tipping point has been exceeded, especially at regional and global scales. These long, smooth transitions between equilibrium states are easy to miss, ignore, or deny, confounding management and governance. However, slow responses by ecosystems after transgressing a dangerous threshold also affords borrowed time – a window of opportunity to return to safer conditions before the new state eventually locks in and equilibrates. In this context, the most important challenge is a social one: convincing enough people to confront business-as-usual before time runs out to reverse unwanted regime shifts even after they have already begun.
In complex systems the degree of homogeneity (vs. Heterogeneity) and connectivity (vs. Modularity) determines whether or not there is a phase transition from one state to another. These are called critical transitions, and there are current efforts to understand both what factors are signicant in causing these transitions and what factors are significant in predicting the fragility of these systems, or the susceptibility to the induction of a phase transition by some external shock. Complex phenomena in a wide range of fields can be studied using these ideas combined with the idea of critical slowing down. Approaches to complex systems in several examples will be discussed, with a focus on living systems.
The term Anthropocene, proposed and increasingly employed to denote the current interval of anthropogenic global environmental change, may be discussed on stratigraphic grounds. A case can be made for its consideration as a formal epoch in that, since the start of the Industrial Revolution, Earth has endured changes sufficient to leave a global stratigraphic signature distinct from that of the Holocene or of previous Pleistocene interglacial phases, encompassing novel biotic, sedimentary, and geochemical change. These changes, although likely only in their initial phases, are sufficiently distinct and robustly established for suggestions of a Holocene–Anthropocene boundary in the recent historical past to be geologically reasonable. The boundary may be defined either via Global Stratigraphic Section and Point (“golden spike”) locations or by adopting a numerical date. Formal adoption of this term in the near future will largely depend on its utility, particularly to earth scientists working on late Holocene successions. This datum, from the perspective of the far future, will most probably approximate a distinctive stratigraphic boundary.
An increase in the frequency and intensity of environmental crises associated with accelerating human-induced global change is of substantial concern to policy makers. The potential impacts, especially on the poor, are exacerbated in an increasingly connected world that enables the emergence of crises that are coupled in time and space. We discuss two factors that can interact to contribute to such an increased concatenation of crises: (1) the increasing strength of global vs. local drivers of change, so that changes become increasingly synchronized; and (2) unprecedented potential for the propagation of crises, and an enhanced risk of management interventions in one region becoming drivers elsewhere, because of increased connectivity. We discuss the oil-food-financial crisis of 2007 to 2008 as an example of a concatenated crisis with origin and ultimate impacts in far removed parts of the globe. The potential for a future of concatenated shocks requires adaptations in science and governance including (a) an increased tolerance of uncertainty and surprise, (b) strengthening capacity for early detection and response to shocks, and (c) flexibility in response to enable adaptation and learning.
Under the rubric of ‘financial inclusion’, lending to the poor –in both the global North and global South –has become a highly lucrative and rapidly expanding industry since the 1990s. A key inquiry of this book is what is ‘the financial’ in which the poor are asked to join. Instead of embracing the mainstream position that financial inclusion is a natural, inevitable and mutually beneficial arrangement, Debtfare States and the Poverty Industry suggests that the structural violence inherent to neoliberalism and credit-led accumulation have created and normalized a reality in which the working poor can no longer afford to live without expensive credit. The book further transcends economic treatments of credit and debt by revealing how the poverty industry is inextricably linked to the social power of money, the paradoxes in credit-led accumulation, and ‘debtfarism’. The latter refers to rhetorical and regulatory forms of governance that mediate and facilitate the expansion of the poverty industry and the reliance of the poor on credit to augment/replace their wages. Through a historically grounded analysis, the author examines various dimensions of the poverty industry ranging from the credit card, payday loan, and student loan industries in the United States to micro-lending and low-income housing finance industries in Mexico. Providing a much-needed theorization of the politics of debt, Debtfare States and the Poverty Industry has wider implications of the increasing dependence of the poor on consumer credit across the globe.
This paper aims to describe the social studies of credit developed in France over the past dozen years. We argue that this French sociology of credit, mostly centered on France, can be useful for researchers analyzing other countries, with other institutional particularities, because it proposes a specific method and a specific way to raise questions: credit is mostly understood as a result of social interactions embedded in organizational and legal structures. French researchers also deeply analyze the consequences of the organization of the credit market for inequalities, social stratification, and people’s life experiences. The first part of the paper focuses on works that have examined credit as a social test, looking at the institutional, technical, and social frameworks of money lending. Then, credit is understood as a sociological experiment: how is it integrated into household economies? How do people use forms of credit? Finally, the third part concentrates on credit failure, when a bank loan becomes a debt. This aspect is mostly framed in French sociology as “over-indebtedness,” which is an administrative and a social category. Throughout the paper, we address credit as both a relationship and a practice. This approach is heuristic, as we seek to demonstrate, because it enables us to show that credit is a social and political issue.
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.
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.
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.
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.