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.

Network science: roots and perspectives

Graphite is a soft, easy to break material, whose physical properties are rooted in the weak coupling between the layers its atoms form. In contrast diamond, whose atoms are arranged in a face-centered cubic lattice, is the hardest material we know on Earth. But the same atoms can also form a hollow sphere, called buckyball, or a cylinder that goes by the name of carbon nanotube. These materials have such fascinating chemical properties that their discovery was honored with a chemistry Nobel Prize in 1996. When these atoms form stacked sheets of linked hexagonal, pentagonal, or heptagonal rings, we call them graphene, with their own impressive list of properties and potential applications, from nano-transistors to gas detectors, a reason why this material swept the 2010 physics Nobel Prize.

What is fascinating about this list is that each of these materials, with such diverse appearance and physical and chemical characteristics, is made of the same atom: carbon. Yet, at the end there is only one difference between graphite, graphene, diamond, buckyballs, carbon nanotubes, and many other allotropes of carbon: the shape of the network that links the carbon atoms to each other. Which is an observation that increasingly resonates well beyond physics and chemistry: when it comes to the society, the economy, or even our cells, some tough, others malleable, some alive while others barely hanging on, many of their characteristics are rooted in the structure of the network that waves their building blocks together.

In the middle of a world economic crisis, studies of biological crisis-survival strategies, which have proven their efficiency over a billion years of evolution, may provide novel ideas and solutions. In this chapter, we summarize our knowledge and the latest results which show that the “crisis of cells,” called stress in biology, and its extreme form, programmed cell death (also known as apoptosis), induce a decrease in the inter-modular contacts of protein–protein interaction networks of yeast and human cells, respectively. Moreover, programmed cell death leads to the decomposition of several key modules of human cells. This network disintegration fits well with the general scheme of network topological phase transitions upon a decrease of resources (and/or an increase of perturbations, stress). The modular disassembly resembles changes to social networks in crisis-like situations. Thus, the re-association of cellular networks after stress and the necessity of mobile, intermodular elements (“creative elements”) for this process of “modular evolution” may give us adaptable schemes for crisis-survival strategies, proving the old observation that a crisis is not a disaster but a challenge providing a chance for development.

How can biological networks help our coping with crisis situations and our understanding of social networks?

From the second half of the twentieth century, the increasing complexity and the information-richness of our everyday life have got closer and closer to the limits of the information handling capacity of our logical, left-hemisphere brain. The Western civilization has been increasingly using (and actually, over-using) logical thinking from the very beginning and, especially, from the Enlightenment of the eighteenth century, signaling the birth of modern scientific thinking. Our schools socialize us and our children to accommodate a conceptual framework, which makes the logical assessment of the changing situations a Pavlovian conditioning of modern times.

Researchers interested in exploring the role played, within policy networks, by organizations representing collective and/or public interests are faced with an array of options which is as impressive as potentially confusing. They may concentrate on the organizations considered most influential in a certain policy domain (e.g. Laumann and Knoke, 1987), or they may look at the entire field of organizations operating on a certain issue or set of linked issues (e.g. Diani, 1995); they may focus on organizations that are closest to the model of the social movement (e.g. Amenta et al., 1992) or, instead, pay attention to public interest groups in the broadest sense, sometimes without even acknowledging any meaningful difference between social movement organizations and interest groups (e.g. Burstein and Linton, 2002).

While each of these strategies may offer valuable insights, it is important to be explicit regarding the types of actors whose role in policy-making we want to explore, and on the relations that they entertain to other actors with similar agendas. Although their internal structure may substantially differ, organizations representing public interests are not usually strong enough to be able to operate entirely on their own. In most cases, they are involved in some kind of alliance and cooperation with cognate organizations. Even if they operate mostly independently, they are nonetheless located within broader organizational fields. This has some important consequences for the analysis of policy networks.

This study proposes a rationale by which researchers can use network analysis tools to improve policies and guidelines for the formation of teams within a particular community. The study begins by defining voluntary collaborative project teams (VCPTs) as teams where members have semi-autonomous discretion over whether to join or participate. It is argued that, with reference to VCPTs, those making policies or guidelines appear to face both an information disadvantage and possess an information advantage relative to individual team members, stemming from differing positions regarding emergent qualities of teams. While team members have access to team-specific information, policy-makers can use information regarding norms within the community as a whole. It is then argued that network analysis is a useful tool for uncovering these community-wide tendencies. An example analysis is then performed using exponential random graph modeling of a network to uncover the underlying logics of team construction within nanoHub.org, an online community that fosters collaboration amongst nanoscientists. Results suggest that the technique is promising, as a normative tendency which undermines team performance is uncovered.

Introduction

As many industries migrate towards a network-based production implemented by short-term, fluid teams which are permeable, interconnected, and modular (Schilling and Steensma, 2001), the performance of teams where members relatively freely choose their collaborators is becoming increasingly important. Settings in which semi-autonomous team assembly is particularly prevalent include the development of software in the open source movement, collaboration in large online multiplayer games, scientific co-authorship, and artistic collaborations (Amaral, 2005; Guimerà et al., 2005; Uzzi and Spiro, 2005).