When Self-Organised Criticality (SOC) was first introduced in 1987 by Bak, Tang, and Wiesenfeld, it was suggested to be the explanation of the fractal structures surrounding us everywhere in space and time. The very poetic intuitive appeal of the combination of terms self-organisation and criticality, meant that the field gained immediate attention. The excitement was not lowered much by the fact that the claimed 1/f and fractal behaviour were soon realised in reality not to be present in the sandpile model used by the authors to introduce their research agenda. Nor did the lack of power laws in experiments on real piles of sand deter investigators from interpreting pieces of power laws observed in various theoretical models and physical systems as evidence of SOC being essentially everywhere. This led rapidly to a strong polarisation between two camps. On the one side there was the group of researchers who did not worry about the lack of a reasonably precise exclusive definition of the SOC concept and therefore tended to use SOC as synonymous with snippets of power laws, rendering the term fairly meaningless. The other camp maintained that SOC was not to be taken seriously. They arrived at this conclusion through a mixture of factors including the observation that SOC was ill defined, not demonstrated convincingly in models, and absent from experiments on sandpiles. The debate sometimes reflected a reaction in response to bruises received during fierce exchanges at meetings as much as a reaction to scientific evidence.

Dissipation is a major theme in SOC for several reasons. Like every relaxation process, avalanching in a sandpile generally can be seen as a form of dissipation, quite literally so for the sand grains that dissipate potential energy in the BTW Model. In that sense, sandpile models are inherently dissipative. Yet, their dynamics can be expressed in terms of variables, which are conserved under the bulk dynamics (also ‘local’ dynamics), such as the number of slope units in the Abelian BTW Model.

The models described in the present chapter, however, go a step further by obeying dynamical rules without local bulk conservation (also ‘local’ conservation), although all models develop towards a stationary state even in the non-conserved variable, i.e. overall there is asymptotic conservation on average. The observation of scale-free dissipation in turbulence triggered the development of the Forest Fire Model (Sec. 5.1), i.e. it was explicitly designed in a dissipative fashion. The situation is somewhat similar for the OFC Model (Sec. 5.3) which incorporates a dissipation parameter α, whereas in the BS Model (Sec. 5.4) dissipation is a necessary by-product. Both the OFC Model and the BS Model are examples of models driven by extremal dynamics, which consists of identifying the ‘weakest link’ among all sites and starting relaxation from there.

In the light of Hwa and Kardar's (1989a) work (Sec. 9.2.2), which suggested that scale invariant phenomena arise naturally, even generically in the presence of bulk conservation, the existence of non-conservative SOC models is particularly important.

This chapter argues for connecting models of several kinds of macro- and microprocesses as they affect structure and dynamics in the globalization of networks of trade. The purpose is to explore multiple levels of structure, process, and adaptation and to loosen assumptions about determinacy in models of networks and globalization. As do many models of emergence, it questions the notions of inevitability that too often surround studies of globalization. Particularly useful for comparison of cases are the models of “world system” developed by Modelski and Thompson (1996, see Devezas and Modelski, 2008). These focus on national policy-driven innovation and processes of European “evolutionary learning” that began in the 1400s. They put into context the models that focus on core-periphery structure as developed by Braudel (1973), or the “world-system” core-periphery model that for Wallerstein (1974) begins in the 1600s. Study of structures of core-periphery in world systems can benefit from added dimensions, improved measurement of network structure, and understanding the effects of periodic crises in terms of historical dynamics.

An unexpected outcome of this survey for issues of policy is that it develops a deeper historical understanding of how certain kinds of exchange systems develop several kinds of inequalities that are inimical to the concept of fair pricing in the operation of market equilibria, even in the absence of economic oligopolies (monopoly, duopoly) and oligopsonies (monopsony, duopsony). These include longstanding militaristic state-policy domination of international exchange, resultant structural inequality in international trade networks, and cyclical events within polities, that in periods of resource scarcity relative to population, create periods of extreme deflation of wages relative to extremes in elite dominance over wealth-generating property ownership.

Energy security of natural gas supplies in Europe is becoming a key concern. As demand increases, infrastructure development focuses on extending the capacity of the pipeline system. While conventional approaches focus mainly on source dependence, we argue for a network perspective to also consider risks associated with transit countries, by borrowing methods from ecological food web analysis. We develop methods to estimate the exposure and dominance of each country, by using network datasets from the present pipeline system, and future scenarios of 2020 and 2030. We have found that future scenarios will not increase the robustness of the system. Pipeline development to 2030 will shift the relative weight of energy security concerns away from source to transit countries. The dominance of politically unstable countries will increase. The exposure will be slightly redistributed by improving the security of already secure countries, and increasing the exposure of those countries that are already in a vulnerable position.

Introduction

During the first days of 2009 a dispute between Russia and Ukraine led to a closure of major gas pipelines, and the worst dropout of the natural gas supply in Europe so far (Pirani et al., 2009). Supply to 18 countries was disrupted, and some areas with limited reserves and a lack of alternative supply channels were left without heating amidst a bone-chilling cold snap. Initial cuts affected the supplies to Ukrainian consumption (January 1), while deliveries to Europe were reduced drastically on January 6 (e.g. Italy experienced losses of 25% towards its needs and decided to increase imports from Libya, Norway, and The Netherlands; Hungarian consumption was cut off by 40%).

Introduction: applied network science

This volume provides an overview of network science applied to social policy problems. Network science is arguably the most dynamic and interdisciplinary field that has grown up to address problems of an increasingly interconnected world. Social problems transgress disciplinary boundaries, especially with the ever-increasing complexity of our globally interconnected world society. It is natural that with complex problems such as loss in ecological diversity, economic crisis, spread of epidemics, and the safety of our food supply, we turn to a field of research that focuses on explaining complex dynamics, and that is inherently interdisciplinary itself.

Networks have become part of our everyday experience as we routinely use online social network services, we hear reports on the operations of terrorist networks, and we speculate on the six degrees of separation to celebrities and presidents. Less manifestly, we rely on vast and complex infrastructural networks of electric power distribution, Internet data routing, or financial transfers. We only ponder the complexity of these systems when we are faced with avalanche-like dynamics in their collapse, as major blackouts, system stoppages, or financial meltdowns.

With networks on the collective mind, there is ample interest in tools to understand and manage complex network systems of social ties. At the time of writing this introduction (in October 2011), there were eighteen applications available on Facebook to visualize one's social network. The popularity of such software tools shows our fascination with the interesting new perspective that the graphic visualization of friendship ties provides.

Introduction

State agencies responsible for managing various risks in social life issue advisories to the public to prevent and mitigate various hazards. In this chapter we will investigate how information about a common foodborne health hazard, known as Campylobacter, spread once it was delivered to a random sample of individuals in France. The Campylobacter is most commonly found in chicken meat and causes diarrhea, abdominal pain, and fever. The illness normally lasts a week but in rare cases patients can develop an auto-immune disorder, called Guillain-Barré syndrome, that leads to paralysis and can be deadly. Campylobacter, together with Salmonella, is responsible for more that eighty percent of foodborne illnesses in France and strikes over 20,000 people each year. People can take simple steps to avoid infection by cleaning their hands, knives, cutting boards, and other food items touched by raw chicken meat and by cooking the meat thoroughly.

In this chapter we build two different network models to see how the information about Campylobacter diffuses in society, by mapping onto various network structures the data we gathered with three waves of surveys. In these models the spread of information depends on two sets of factors. Firstly, each person has a set of individual properties that influences their propensity to transmit the information to or to receive the information from someone they know. Second, each person is connected to others in ways that also affect transmission. There are three aspects of these social ties that matter.

Entrepreneurial groups face a twin challenge: recognizing new ideas and implementing them. Recent research suggests that connectivity reaching outside the group channels new ideas, while closure makes it possible to act on them. By contrast, we argue that entrepreneurship is not about importing ideas but about generating new knowledge by recombining resources. In contrast to the brokerage-plus- closure perspective, we develop a concept of structural folding and identify a distinctive network position, structural fold, at the overlap of cohesive group structures. Actors at the structural fold are multiple insiders, participating in dense cohesive ties that provide close familiarity with the operations of both groups. Structural folding provides familiar access to diverse resources. Firstly, we test whether structural folding contributes to higher group performance. Secondly, because entrepreneurship is a process of generative disruption, we test structural folding's contribution to group instability. Thirdly, we move from dynamic methods to historical network analysis and demonstrate that coherence is a property of interwoven lineages of cohesion that are built up through an ongoing pattern of separation and reunification. Business groups use this pattern of interweaving to manage instability while benefiting from structural folding. To study the evolution of business groups, we construct a dataset that records personnel ties among the largest 1,696 Hungarian enterprises from 1987-2001.

The study of networks in ecology is rapidly expanding. Although network thinking is by no means new to ecologists, cross-fertilization from other fields, ranging from computer science to sociology, has recently furthered the field significantly. Here we examine some of the applications of network science to ecology, with an emphasis on its potential to contribute to the preservation of biodiversity, an issue that has relevant social and policy implications. Two different forms in which ecological networks may appear are used: food webs and signed digraphs of dynamical systems. In the former, networks represent energy flow transfers from producers to consumers, while in the latter what is depicted is the effect that populations exert on each other.

The main objective is to enlighten as to how applying network science can contribute to some central questions concerning biodiversity, such as the identification of keystone species, the response of population to environmental perturbations, the robustness or inertia of the system to external events in the form of loss of species and links that alter population dynamics.

Biodiversity and the network perspective in ecology

In the last decade biodiversity loss has become of major concern (Loreau et al., 2001; Ceballos and Ehrlich, 2002; Pimm et al., 2006). In the face of this crisis, policies aimed at preserving biodiversity have been called for (Westman, 1990). To shape effective management strategies, a great deal of effort is required in a diversity of fields (Peuhkuri and Jokinen, 1999), prominantly, ecology.

Businesses of all kinds usually try to participate in the regulation of their own activities as much as they can. One way of participating in this regulatory activity is to exercise control on State institutions which solve conflicts among businesses and discipline entrepreneurs. This participation can lead to institutional capture. In this chapter we describe and show the contribution of network analysis in measuring the level of institutional capture in a specific case. We analyze a network of business people acting as lay judges in a judicial institution, the main first-level commercial court in France. This court handles commercial litigation as well as bankruptcies. Courts are not static institutions making atemporal and purely rational decisions. They are contested terrain, the object of broader conflicts that occur outside courthouses. We use a longitudinal study of advice networks among these 240 lay judges at the Commercial Court of Paris (CCP) – judges who are elected by their business community at the local chamber of commerce – to examine a few characteristics of such an institutional capture, in particular an invisible mechanism through which the banking industry manages to dominate this court. Results illustrate the value of network studies for a renewed attention to the inner workings of institutions and for the protection of public interest in the regulation of capitalist economies, in which boundaries between private and public sectors are blurred.

Joint governance and institutional capture

Business usually tries as much as it can to participate in the governance of its markets. In this chapter, we look at an extreme case of how business gets organized, collectively, to do so by capturing a judicial institution. The case is that of French commercial courts, a four and a half century-old institution. In France, the State has long been sharing its own judiciary power with the local business community. Business succeeded in 1563 in negotiating what could be called a “joint governance” agreement with public authorities: this agreement created a special jurisdiction for commerce in which judges are lay, voluntary judges, i.e. elected business people who are not remunerated for the job. French commercial courts are truly judicial, first-level courts.

One of the most important questions for public policy that arose from the financial crisis that began in the United States in 2007 is the effect of executive pay on risk taking. The regulatory implications of this claim have been significant. Federal Reserve Chairman Ben Bernanke described the Fed's efforts to develop rules that will “ask or tell banks to structure their compensation, not just at the top but down much further, in a way that is consistent with safety and soundness – which means that payments, bonuses and so on should be tied to performance and should not induce excessive risk” (Wall Street Journal, 5/13/2009). The Dodd-Frank Wall Street and Consumer Finance Act, passed in July 2010, included several provisions to give shareholders a say over pay, and to strengthen the risk and compensation practices of boards of directors. Sheila Bair, chairman of the FDIC, said: “This proposed rule will help address a key safety and soundness issue which contributed to the recent financial crisis: that poorly designed compensation structures can misalign incentives and induce excessive risk-taking within financial organizations.”

The justification for imposing risk on executives (and workers in general) derives from the wide acceptance of the principal-agent model that informs law and economics and managerial practice. The standard principal-agent model recognizes that the effort of the executive (the agent) is not observable by owners (the principal), and thus compensation contracts use incentive schemes to motivate executives who otherwise would exert less than the contracted effort.

In the first part of the chapter, four ideal-typical corruption transactions are explicated in terms of the principal-agent-client model: bribery and extortion are described as two different types of agent-client relationship, while embezzlement and fraud, as two different types of principal-agent relationship. The main idea is to describe these elementary corruption transactions as simple directed graphs. The next section of the chapter takes into consideration different kinds of possible motivation (such as the reduction of risk or transaction costs) of the principals, agents, and clients, in order to embed their corruption transactions in various kinds of personal, business, political, and other institutional networks.

In the second part of the chapter some typical and stable network configurations are presented, based on recent empirical corruption research carried out in Hungary. Certain corruption cases (such as party financing or granting of permits) are analyzed in detail, and are described as complex and multiple networks. The chapter concludes by showing some signs of the evolution of corruption networks in Hungary in terms of the number of actors, the complexity of network configurations, the level of personal or institutional embeddedness, and the multiplicity of relationships.

Introduction

The study consists of two main parts. In the first part, the concept and ideal-types of corruption are defined. The various types of elemental corruption transactions are differentiated in terms of the principal-agent-client model, illustrating them through directed graphs. We distinguish two subtypes of both the agent–client relationship and the principal–agent relationship: bribery and extortion in the former case, embezzlement and fraud in the latter. To conclude the first part, we attempt to delineate the motivational mechanisms that encourage participants in corruption scenarios to embed their transactions in various types of personal, business, political, and other institutional networks. With the help of these networks, those involved are often able to decrease the transaction costs and risks associated with corrupt dealings.

There is an acclaimed tradition in the history and sociology of science that emphasizes the role of the individual genius in scientific discovery (Merton, 1968; Bowler and Morus, 2005). This tradition focuses on the guiding contributions of solitary authors, such as Newton and Einstein, and can be seen broadly in the tendency to equate great ideas with particular names; for example: the Heisenberg uncertainty principle, Euclidean geometry, Nash equilibrium, and Kantian ethics. The role of individual contributions is also celebrated through science's award-granting institutions, like the Nobel Prize Foundation (English, 2005).

However, several studies have explored the evident shift in science from this individual-based model of scientific advance to a collaborative model. By building on classic work by Harriet Zuckerman and Robert K. Merton, many authors have established a rising propensity for teamwork in samples of several research fields; with some studies going back a century (Collins, 1998; Cronin et al., 2003; Merton, 1973a; Jones, 2005). For example, Derek de Solla Price examined the change in team size in chemistry from 1910 to 1960, forecasting that by 1980 zero percent of the papers would be written by solo authors (de Solla Price, 1963). According to our research, the mean team size for papers written in chemistry had grown to nearly 3.7 contributors by the year 2000. Recently, Adams et al. (2005) established that teamwork has been increasing over time across broader sets of fields among the most competitive U.S. research universities.