Socio-Economic Review

Socio-Economic Review Advance Access originally published online on March 12, 2008
Socio-Economic Review 2008 6(3):395-426; doi:10.1093/ser/mwn006

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Transforming socio-economics with a new epistemology

Rogers Hollingsworth¹ and Karl H. Müller²

¹ University of Wisconsin, Office 4126 Mosse Building, 455 North Park Street, Madison, Wisconsin 53706, USA
² Vienna Institute of Social Scientific Documentation and Methodology (WISDOM), Maria Theresienstrasse 9/5, A-1090 Vienna, Austria

Correspondence: hollingsjr{at}aol.com

This paper argues that a new scientific framework (Science II) has been slowly emerging, rivaling the Descartes–Newtonian perspective (Science I) dominant for several hundred years. The Science II framework places a great deal of emphasis on evolution, dynamism, chance and/or pattern recognition. As both cause and effect of the new perspective, scholars in the physical, biological and social sciences are increasingly addressing common problems, borrowing insights from and interacting with each other. The epistemology of Science II has enormous potential for understanding problems of fundamental interest to socio-economists. The paper focuses on five useful concepts in the framework of Science II: self-organizing processes, complex networks, power-law distributions, the general binding problem and multi-level analysis.

Key Words: complex networks • power-law distributions • multi-level analysis • interdisciplinarity • epistemology • inequality • socio-economics

JEL classification: A14 sociology of economics, D85 network formation and analysis, Y80 related disciplines

	1. Introduction

TOP 1. Introduction 2. The rivalry of... 3. Socio-economics in the... 4. Concepts useful to... 4.1 Self-organizing processes 5. Concluding observations Funding Notes Acknowledgements References

 
For several hundred years, the dominant framework shaping Western science has been the Descartes–Newtonian ‘paradigm’. Historically, this framework—with its epistemology—has been powerful in shaping the thinking of both natural and social scientists. Yet, an alternative view of explaining reality has slowly been emerging, and the influence of this new perspective is rapidly diffusing. In the following discussion, we focus on these perspectives, especially the more recent one, and suggest its potential for enriching the field of socio-economics.

The current status of socio-economics can be crudely summarized as follows: socio-economics has been quite strong in empirical and comparative analyses and in relentless criticism of the neoclassical paradigm. However, socio-economics has remained relatively weak in developing a comprehensive theoretical alternative to the dominant neoclassical framework. We suggest that the emerging alternative perspective for conducting science heightens the potential for an enriched socio-economic research agenda and for richer exchange between natural and social scientists.

Despite the strong theoretical commitment to interdisciplinarity in socio-economics, borrowing knowledge from other disciplines has remained as difficult for socio-economists as for those in most other scientific fields. Learning from other disciplines confronts two different types of errors. Using terminology from statistical test-theory, borrowing from other disciplines can produce -type errors by accepting analogies, methods or models which are highly questionable and generate no cognitive value, let alone surplus value. On the other hand, learning from other disciplines may also generate β-type errors by rejecting highly appropriate and very fruitful analogies, models or methods from other fields. Socio-economists, like those in most other scientific fields, very seldom commit -type errors, but like most other fields, have a high propensity for β-type errors due to the overall complexity of the scientific landscape. Yet, using perspectives developing in both the social and the natural sciences, socio-economists have the potential to uncover new models and concepts with which to engage in theory construction independent of the classical Descartian–Newtonian paradigm, and to advance the theoretical insights of their natural science colleagues. In short, we are at a moment when there is potential for serious convergence of interests among social and natural scientists.

	2. The rivalry of two scientific perspectives

TOP 1. Introduction 2. The rivalry of... 3. Socio-economics in the... 4. Concepts useful to... 4.1 Self-organizing processes 5. Concluding observations Funding Notes Acknowledgements References

 
A fundamental reorganization and reconfiguration of scientific knowledge is presently well underway. For several hundred years, much of Western science was influenced by a fundamental distinction, a leading metaphor and a dominant paradigm. The core paradigm was based on the Principia Mathematica by Isaac Newton. The fundamental distinction and leading metaphor for the Newtonian paradigm had been proposed by René Descartes in Meditationes de Prima Philosophia, in which he offered two ontological kingdoms, res extensa for the natural world, and the other for mental substances (res cogitans). Descartian dualism paved the way for separating science into two fundamentally different cultures, operating on two sets of principles. With increasing differentiation of scientific disciplines, many philosophers and scientists such as Wilhelm Dilthey, Max Weber and C.P. Snow bemoaned the high-cognitive distance between the natural and the human sciences.

For Descartes, the dominant metaphor for the natural world was the machine. His metaphor eventually became generalized as a view about the entire world, a description of how the world operates and a prescription for studying it. Accepting his mechanistic view, scholars have often adopted a simplified view of the world, of the relation of parts to wholes and of causes to effects. Borrowing the machine metaphor, scholars have extended the metaphor to that of engineer, watchmaker, designer or social planner.

The leading epistemological vision within the Science I paradigm lies in its heavy emphasis on reductionism: societies are believed to be built up from individuals, individuals from cells and their neural organization, cells from molecules, molecules from atoms, etc. An epistemological assumption was that the behaviour of large interactive systems could be understood by analysing elements separately and studying microscopic mechanisms individually. Moreover, the dominant theory in the Newton–Descartes perspective lay in the identification and clustering of universal laws. This view approaches the world as made up of variables, linked by differential equations that are described like laws of motion, subject to noise and random variation. There is a widely shared view that we can easily design new institutions, even transfer them from one society to another. Policy makers draw up complex plans for altering national economies or for nation and state building; natural scientists talk about genetic engineering. Even though the visions are often grand, the mentality has been that of the engineer with an emphasis on efficiency and redundancy: design projects simply and keep interactions among the necessary parts to a minimum. In very complex and sophisticated projects, there is some feedback modelling or other elements from control theory, but at all stages, irrelevancy is to be avoided.

Contrary to the Cartesian design, a massive reconfiguration of science has been slowly emerging for approximately 150 years, starting with the Darwinian revolution in the nineteenth century, accelerating from the 1950s onwards towards a new science regime here labelled Science II. The newer framework is not grounded on engineering and clockwork metaphors, but is primarily concerned with an effort to comprehend both the natural and the social world in terms of evolution and complex adaptive systems which tend to be self organizing. While a view of the world shaped by the influence of Newton and Descartes is comparatively tidy and predictable, the new scientific configuration emphasizes the complexity and unpredictability of the world, open to many more possibilities (Kauffman, 1993; Bak, 1997; Prigogine and Stengers, 1997; Crouch, 2005).

The new perspective became increasingly widespread after physicists and computer scientists demonstrated in the 1960s and 1970s that even simple equations can produce results which are complex, surprising and unpredictable. Advances in genetics, neurosciences, computer science and other fields have led to a conception of science which increasingly emphasizes the important role of chance in explaining phenomena (Edelman and Gally, 2001). The cherished notions of general laws or axioms have been replaced in Science II by notions like pattern formation and/or pattern recognition (Barabási, 2002). Over the past two decades, specialists in discipline after discipline have increasingly recognized that the world is far more complex than hitherto recognized. In the words of economist Brian Arthur, more and more scientists realize ‘that logic and philosophy are messy, that language is messy, that chemical kinetics is messy, that physics is messy and finally that the economy is messy’ (quoted in Waldrop, 1992, p. 329). The emerging perspective, rapidly diffusing across academic disciplines, suggests that the world does not change in predictable ways (Mayr, 1991; Wallerstein, 2004; Bak, 1997; Arthur et al., 1997).

Self organization is a dominant process in Science II; uncertainty is a basic epistemological assumption. Practitioners of Science II think in terms of probabilities. Physical and social phenomena are always evolving, but no one can predict the future. Systems have an inherently non-linear dynamic quality. The games in which actors are engaged are ever changing, and even in the same game, the rules keep evolving. There is a great deal of co-evolution in the world, and very small changes can have both big and long-term consequences. Science II analysts often engage in case studies over long periods of time and report a great deal of contingency and chance.¹

Two scientists whose work inspired and embodied the Science II paradigm were Charles Darwin, perhaps the greatest biologist and historian ever; and Ilya Prigogine, a twentieth-century physical chemist. Darwin and Prigogine emphasized the importance of dynamic analysis, the uniqueness of historical events, the irreversibility of social and natural processes and the difficulty of making successful predictions in complex systems. Both understood the importance of retrospective (i.e., historical) analysis to understand reality. In the Science II framework, scientists search for regularities within systems, but unlike neoclassical analysts, they view systems as tending to move away from an equilibrium, occasionally evolving into a new system. Science II rejects the idea that reality can be explained with determinism, linearity and certainty. Darwin and Prigogine's methodological and theoretical frameworks argued that historical analysis was central to scientific understanding (Prigogine and Stengers, 1997; Wallerstein, 2004). Prigogine's work advanced the idea that systems with a large number of interacting parts have adaptive self-organizing internal structures. The works of Darwin, Prigogine and others (e.g., John von Neumann) had enormous impact not only on socio-economics but also on biology, geology, meteorology and computer science. A central tenet of complex systems analysis is that large-scale collective behaviours result from repeated non-linear interactions among constituent parts, whereby wholes tend to be much more than the sum of their parts. Increasingly analysts maintain that such systems are not susceptible to mathematical analysis, but must be understood by letting them evolve—over time or with simulation analysis (Newman et al., 2006; Sornette, 2003; von Neumann, 1966; Mayr, 1991; Wolfram, 2002).

Numerous Science II analysts have observed that in many social and biological systems, some of the same functions are performed by similar structures (a world with a great deal of redundancy). Physicists and others point out that many systems are characterized by degeneracy—situations in which structurally different elements lead to the same output (Edelman and Gally, 2001). Similarly, more and more socio-economists are recognizing that the same type structure can be associated with a different output or performance, and across societies, different structures can be associated with the same output. Interesting recent examples are the German and American economies which were relatively stable but had different structures over the last 25 years. In some decades, one set of structures had much better performance, but over time the situation reversed, suggesting how the same performance can stem from different structures (Crouch, 2005).

Table 1 identifies some of the cognitive structure of the new science regime and emphasizes in an ideal-typical manner some of the basic differences between Science I and II. In contrast to Science I, one finds in Science II a nested structure of science which is neither hierarchical nor reductionist, an epistemological search for complex pattern construction across different scientific domains. Requirements for scientific explanation are much weaker than with the Descartes–Newtonian paradigm.

View this table: [in this window] [in a new window]   Table 1 Differences between Science I and Science II

 
We are not suggesting that the Science I perspective is no longer valid for scientific investigations. On the contrary, to address many problems, the Science I perspective will continue to be very valuable. But our argument is that Science II has considerable promise for the advancement of a socio-economic research agenda.

	3. Socio-economics in the framework of Science II

TOP 1. Introduction 2. The rivalry of... 3. Socio-economics in the... 4. Concepts useful to... 4.1 Self-organizing processes 5. Concluding observations Funding Notes Acknowledgements References

 
For several reasons, the potential for major theoretical advance in socio-economics has increased dramatically in recent decades. First, the cognitive distance in Science II between the natural sciences and socio-economics is diminishing, suggesting that socio-economists have the potential to contribute to and borrow from theoretical insights and models developed in a large variety of other fields. While socio-economics has had limited success in the area of theory construction, it has considerable potential to adapt to the rapidly developing new methods, models, concepts and other tools in what we label as a Science II perspective. Second, the stock of available models and mechanisms within the Science II framework is characterized by a steep increase in complexity and is focused predominantly on process dynamics and evolutionary perspectives quite compatible with socio-economists' interests in societal change and long-term trends. Third, interaction and communication among analysts in various fields of science is rapidly increasing due to the emergence of commonly shared information and communication technologies within the Science II framework.

For example, a number of socio-economists during the last decade have borrowed many conceptual issues high on the agenda of the life sciences. This is suggested by Colin Crouch's recent book Capitalist Diversity and Change: Recombinant Governance and Institutional Entrepreneurs (2005). Elsewhere, Robert Boyer focused on ‘hybridization’ and ‘endometabolism’ as driving forces for institutional change. Significantly, concepts like ‘recombinant’ and ‘endometabolism’, deeply embedded in the life sciences, are now incorporated into the scholarship of socio-economists (Boyer, 2005). Other socio-economists have also incorporated life-science perspectives into their writings—concepts such as self-organization, self-assembly, emergence, co-evolution, path-dependency, bifurcations, punctuated equilibrium and multi-level analysis come to mind.

Within socio-economics, we should reflect on whether the range of our current interdisciplinary interests is broad enough. Might we enhance our insights for a richer research programme if we broadened our perspectives and attempted to understand how certain key issues highly relevant for socio-economics—self-organizing processes, complex networks, power-law distributions, binding theory and multi-level analysis—have been used by scientists in fields outside the social sciences? In our own research, we are struck by the degree to which scientists in many fields—not just socio-economics—are wrestling with similar problems. It is important that scientists recognize that when they are addressing common problems, they can derive substantial benefits by engaging in mutual interactions.

3.1 Common metaphors
As we interact with natural scientists, we should recognize that many advances in knowledge have historically occurred by individuals thinking metaphorically across different scientific domains. Several recent Nobel laureates in the basic biological sciences (e.g., Günter Blobel, Roderick MacKinnon and John Walker) gained enormous insights for their work by borrowing metaphors from the social world. We are optimistic that if we strive to think metaphorically and borrow insights from colleagues in other fields, we too are likely to have richer perspectives about our own research problems. Useful tools for developing new insights, metaphors can be a vehicle by which ideas and models from one field may be transferred to another. But even though metaphorical thinking is an important tool in theory construction, ‘metaphors are never complete, precise, or literal mappings’ (Hodgson, 1999, p. 68). After a metaphor provides a new insight, we must then engage in rigorous and deep thinking in order to refine our new perspective (Edelman, 2006, pp. 58–59; Maasen and Weingart, 2000; Mayntz, 1992).

Highly creative and productive scholarship involves a trade-off between range and specificity. By enhancing the range of our knowledge, we increase the prospects of recognizing new patterns and of developing new insights about the world, but at the expense of in-depth understanding. Creativity emerges as a result of broad thinking, and usually requires integrating knowledge from diverse fields and investing in being rigorous and acquiring great depth in specific areas (Edelman, 2006; Hollingsworth, 2007).

3.2 Shared methods and models
We should be aware of an increasing number of common, cross-disciplinary methods and models within Science II. However, the transfer of models and methods is not restricted to a one-way flow from the natural to the social sciences or vice versa. A variety of flows from the social sciences to natural science immediately come to mind. The ideas of The Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern (1944), originally conceived as a radical revolution for economic theory, rapidly diffused into the biological and other sciences in the form of game theory. Mitman (1992) brilliantly demonstrated how entomologists analysing the social behaviour of insects borrowed models from social scientists who studied cooperation and business associations.

Some socio-economists are working to integrate evolutionary neuroscience into the discipline of economics, in an effort to understand human sociability from the standpoint of physiology. There is also a burgeoning literature in the related field of neuroeconomics which is having a ‘revolutionary’ impact on long-held assumptions of economists about human decision making (Cohen, 2005; Camerer et al., 2005; Cassidy, 2006). Entire new institutes and research programmes are emerging which focus on problems of common interest to both natural and social scientists. One of the most visible has been the Santa Fe Institute in New Mexico, involving prominent physicists, biologists, sociologists, economists and anthropologists, several of whom have been Nobel laureates (Waldrop, 1992).

	4. Concepts useful to socio-economists and natural scientists

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As researchers in different fields become more cognizant that they are working on similar types of problems, they enhance their potential for mutual learning. For illustrative purposes, we briefly discuss five major, but interrelated problem areas for which socio-economists and natural scientists use similar concepts. These five problem areas, all at the frontiers of research in both the natural and social sciences, are (1) self-organizing processes, (2) the structure and dynamics of complex networks, (3) power-law distributions, (4) the binding problem, or what might be called processes of integration and disintegration and (5) multi-level analysis. These are problem areas which socio-economics shares with a number of other fields (Moody, 2004). There are considerable differences in the ways that various disciplines address these processes/methods, and much additional research needs to be done before we have a thorough comprehension of any one of them. Even though scientists in different fields work on common problems and can derive insights from one another, scientific explanations are essentially specific to the phenomenon to be explained (Edelman, 2006). As Bunge (2003, p. 22) observes, ‘there are no all-encompassing explanations because there are no one-size-fits-all mechanisms’.

	4.1 Self-organizing processes

TOP 1. Introduction 2. The rivalry of... 3. Socio-economics in the... 4. Concepts useful to... 4.1 Self-organizing processes 5. Concluding observations Funding Notes Acknowledgements References

 
These processes generally occur in open, complex systems, not guided by some central agent. Research programmes designed to understand self-organizing processes are well underway in such diverse fields as sociology, political science, economics, anthropology, psychology, history, physics, biology, chemistry, meteorology and earth sciences. Invariably, the models employed are non-linear in nature. Hardly any scientist in these fields is able to make successful predictions about the future, as self-organizing processes are understood best by retrospective analysis (Prigogine and Stengers, 1997). Various sociologists and economists have elaborated on self-organizing processes by confronting the underlying mechanisms which permit spontaneous order and self-organizing processes. Social scientists who eloquently addressed this problem but in fundamentally different ways include Adam Smith, Thomas Malthus, Karl Marx, Nikokai Kondratiev, Joseph Schumpeter, Friedrich von Hayek, Ferdinand Braudel, Immanuel Wallerstein and Jon Elster. In self-organizing processes, the emergence of macro socio-economic structures results from collective and dynamic interactions among large assemblages of individuals operating at the micro level of societies. Thus macro and micro phenomena are constantly evolving together. Self-organization processes are evolutionary in that no central organizer or planner has much influence on emergent processes, and ‘control’ tends to be diffused among large numbers of interacting parts of an entire system. Causes and effects operate in a somewhat non-linear manner, and small causes can have huge effects or vice versa (Kauffman, 1993; Nicolis and Prigogine, 1977; Heylighen, 2003; Mayntz, 1992).

Scholars who work on self-organizing processes are generally those who cross disciplines (Barry, 1982), study problems over lengthy time periods, and include such contemporary social scientists as economic geographers, historians and sociologists (Pred, 1966; Saxenian, 1994; Krugman, 1996; Sabel and Zeitlin, 2003). Their writings have demonstrated that such varied subjects as the location and growth of cities, economic sectors in specific locations, and the rise and decline of empires occur in self-organizing and spontaneous ordering processes.

Many retrospective studies of radical innovations suggest that they are not planned but emerge in self-organizing, unpredictable processes in complex environments. These findings tend to be born out whether one is studying radical organizational innovations, technological innovations or innovations in basic and/or applied science (Freeman and Soete, 1997; Schumpeter, 1934; Saxenian, 1994; Hollingsworth et al., forthcoming [2008]; Hage and Hollingsworth, 2000; Dosi, 1982; Powell et al., 2005).

Those who study self-organizing processes are represented in many disciplines, frequently in subspecialties of an interdisciplinary nature. An interesting specialized area deserving brief mention is the subject of path dependency, which may be analysed under the broad category of self-organizing processes. While there are various approaches to path dependent processes, the one most frequently employed suggests that historical processes are self reinforcing (David, 2000). A number of scholars have nevertheless focused on the role of actors who influence paths at critical junctures. In the same vein, Thelen (2004) and others have demonstrated that historical processes often undergo abrupt change, some of which appear to have been revolutionary in nature (Mahoney, 2000; Pierson, 2000; Crouch, 2005).

Although many models and explanations of self-organizing processes are employed in numerous fields, socio-economists should recognize that much additional research is needed to address the fundamental problem of the relative importance of endogenous and exogenous explanations. There is a tendency in most literature on self-organizing processes to explain events as due to endogenous factors of a system and to minimize or even ignore the response of the system to an external shock (Reiter, 2003). As Sornette (2007) observes:

most natural and social systems are continuously subjected to external stimulations ... . It is not clear ... if a large event is due to a strong exogenous shock, to the internal dynamics of the system organizing in response to the continuous flow of small solicitations, or maybe a combination of both.

(For further discussion in socio-economics, see Thelen, 2003, p. 209; Streeck and Thelen, 2005.) Obviously, an understanding of this problem is fundamental for comprehending the nature of change in complex systems.

4.2 The structure of complex networks
Complementing interdisciplinary work on self-organizing processes is another body of research involving a number of academic fields: the study of complex networks. Those studying this subject find that the architectures of complex networks have a high degree of similarity, whether one is analysing the social world, biological cells, neural connections in the human brain, epidemics or the Internet. The study of complex networks has done more to bring a convergence of interests of social, physical and biological scientists than any other area of research in the history of modern science (Barabási, 2002, 2007; Barabási and Oltvai, 2004; Newman et al., 2006).

Of course, scholars have long been aware of networks in nature and society. However, a new science of networks has emerged. Until approximately 15 years ago, most network analysts assumed that in a network the connections among nodes had a normal (Gaussian) distribution. Most of their studies were essentially descriptive and static, focused on interactions among network members, underemphasizing the collective or macro structure of the entire network (Barabási, 2002).

The new science of networks concentrates on the structures and processes of entire networks, analyses the evolution and continuous change of networks, and the outcomes and effectiveness of networks. Research on complex networks engages in longitudinal studies of individual networks as well as comparisons of networks in the social world with those in the biological and physical worlds. A primary goal of the new science is to simplify our understanding of how things are connected to each other and how their connections evolve. A dominant theme in the recent work is that most complex networks were unplanned and very decentralized and emerged from a self-organizing collection of interacting parts (Powell et al., 2005; Uzzi and Spiro, 2005; Watts, 2003; Provan et al., 2007; Barabási, 2002).

In contrast to earlier students of networks, analysts of complex networks are holistic in their thinking. Complex networks have a characteristic signature: there is a high degree of local clustering, but at a more macro level there is only a short distance between any two nodes. Those studying complex networks clearly subscribe to the Science II framework, methodologically are anti-reductionist in their research strategies, and believe that the structure of networks is the key to understanding much of the world.

Recent work on complex networks demonstrates that some network nodes have a large number of links to other nodes, while most nodes have very few links to other nodes. This is called a scale-free distribution. In short, there is great inequality in the way that nodes are connected to each other. Inegalitarian or scale-free networks are characteristic of large transportation systems. For example, there are a few major hubs—Chicago O'Hare, London Heathrow, Frankfurt International—having links with hundreds of smaller airports. But each smaller airport is linked to relatively few other airports. Similarly, in the global network of banks, there are a few major banks in New York, London, Frankfurt and Tokyo linked to many other banks, while there are thousands of small banks, each with few links to other banks. It is the architecture of these kinds of networks which has been of greatest interest to social scientists engaged in the new science of networks (Watts, 2003, 2004; Sornette, 2003).

In the social sciences, the foundation for the study of complex networks was prepared years ago by several scholars working independently of one another. There was Robert Merton's famous paper (1968) ‘The Matthew Effect in Science’ and Derek de Solla Price's argument about cumulative advantage in science. Merton developed his paper from a quotation from the New Testament Gospel of Matthew, ‘For unto every one that hath shall be given, and he shall have abundance; but from him that hath not shall be taken away even that which he hath’. Merton's argument was that rewards in science are distributed in a self-organizing but very inegalitarian manner. Those who already have rewards continue to receive more, while those who are unrecognized—even if deserving—remain unrecognized. In Merton's thinking, each scientist was part of the architecture of a complex network. His analysis was a variant on Pareto's ‘law’ (1896): the rich tend to get richer, generally at the expense of the poor. Price (1976) laid the foundation for the study of networks in the citation of scientific papers and was one of the first to reveal that the pattern of citing scientific papers had a complex, inegalitarian network structure. He demonstrated that papers receive citations in proportion to the number they already have, a phenomenon he labelled as the process of ‘cumulative advantage’ (Price, 1976; Newman et al., 2006).

The science of complex networks is heavily based on three underlying concepts: growth, preferential attachment and re-wiring. Growth and preferential attachment are so common across all kinds of complex networks that analysts tend to view them as micro rules which generate highly ordered macro-behaviours across a variety of heterogeneous domains. The key idea of preferential attachment is that small differences in ability or even luck tend to get locked in and lead to large inequalities over time. Once an entity becomes large—regardless of how it occurred—it is likely to grow even larger (Arthur, 1994; David, 2000). If one node has many more links than any other, it is far more likely to continue growing new links with other nodes. The older nodes have distinct advantages over more recently established ones.

There was no intention in the work of Merton and Price to deny the importance of historical specificity in the phenomena they were addressing. Indeed, all of these studies were based on rich historical analysis. The idea was that regardless of the specific reasons for initial success in a particular space or time, the successful were more likely to continue reaping more rewards than the less successful. The rich have many ways of getting richer, some deserved and others not. The significant thing is that they continue to prosper relative to others in the network (Watts, 2003, pp. 109–11; Barabási, 2002). While processes of growth and preferential attachment tend to be common across complex networks, a literature is emerging—especially in sociology—which addresses why there is variation in the specific norms or rules which determine the type of preferential attachments which emerge in specific networks in particular circumstances (Kogut, 2000; Powell et al., 2005; Provan et al., 2007).²

Specific networks are subject to change or collapse due to internal and external conditions. Internal network mechanisms may become overloaded, as nodes cannot integrate and exchange new information. External conditions may change so much that a complex network becomes overwhelmed and previous patterns of growth and preferential attachment cease. Some changes may be so extreme that they destroy the nodes with the most attached links, especially if they are tightly linked to each other. This, of course, would mean that the shape of a network would be profoundly changed.

Some analysts have demonstrated that there is often a self-organizing process of re-wiring among complex networks, a process that allows for network flexibility by permitting the removal of links connected to certain nodes and replacement with new links, essentially a re-shuffling of node connections based on the principle of preferential attachment. This kind of activity facilitates the creation of new markets, technologies, behaviour and/or institutions—the continuing emergence of novelty (Schintler et al., 2005).

Despite their flexibility, complex networks are vulnerable to attack or collapse, so that at times they experience ‘tipping’ or cut-off points, which can have widespread ramifications. These are the points at which the collective organization of a network changes so much that the entire network structure may collapse. ‘Tipping points’ are critical states of change related to node relationships and their distribution in a network (Watts, 2003).

Examples of how change might or might not lead to a ‘tipping point’ can be found in the history of financial institutions. If a single bank is linked to only a few other banks, perhaps because of small capitalization or rural location, its failure will not resound throughout the network. However, the failure of several large financial institutions, each with links to many other banks, can induce so much turbulence in a financial system that an entire network could collapse. The failure of one or more large financial institutions could represent a ‘tipping point’, destroying the whole system. With these insights, complex network analysts are better able to understand ‘crashes’ in markets and on the Internet, the contagion effects of fads in fashion and book publishing, and the spread of disease. A major interest to analysts of networks is the fragility of specific networks in different scientific domains and the intense collaboration among social and natural scientists on this subject is impressive (Sornette, 2003; Pastor-Satorras and Vespignani, 2004; Vega-Redondo, 2007; Barabási, 2007).

4.3 Power-law distribution
The extensive research on complex networks reminds us that the world is extraordinarily complicated, very dynamic and extremely inegalitarian. Each individual, group and/or network is unique, and yet there is a great deal of commonality in the overall architecture of networks and their constituent parts, and one of our tasks as socio-economists is to understand that architecture.

Scientists long believed that most phenomena followed a bell-shaped or Gaussian curve. Observations, which were random, were believed to have a normal distribution, with a well-defined average. A bell-shaped curve has a distribution tapering on each side from the mean and is used so frequently that scholars have often referred to it as a ‘normal distribution’. Most observations were assumed to be independent of each other. A common example would be the distribution of the heights of males in a population. Most males would be between five and six feet tall, and there would be a peak value, with people scattered on either side of the peak. It is unimaginable that some might be 20 or 30 feet tall. Although many phenomena are best described with a bell-shaped curve, astute observers have long realized that distributions do not always follow a normal curve. Many distributions and patterns are non-linear, and there are many types of non-linear distributions.

Until almost 15 years ago, unless one were a physicist or mathematician, power laws were unfamiliar—despite the fact that they had been used to describe a variety of social phenomena for well over a half century (Zipf, 1949; Simon, 1955). But increasingly, scholars have observed that many kinds of social phenomena can best be described by a power-law distribution. With the emergence of Science II, analysts across different disciplines began to observe that the phenomena in self-organizing processes often have power-law distributions.

Power-law distributions do not have a peak at their average value. Rather, the distribution begins at its maximum value and then decreases towards zero. Power-law distributions are very different from normal distributions and have a small number of high values and a large number of low values. Thus, in the abstract example in Figure 1(a), there are very few individuals with extreme wealth, while most individuals have little wealth. In contrast, Figure 1(c) presents actual data and shows the inequality in the distribution of major scientific discoveries by research organizations across four countries and over time. A very few organizations made numerous major discoveries, while most had none.

View larger version (19K): [in this window] [in a new window] [Download PowerPoint slide]   Figure 1 Examples of power-law distributions. *Distribution of 291 discoveries in basic biomedical science across all (n = 755) major research organizations engaged in such research in Britain, France, Germany and the United States, 1901–1995. The equation of the curve in Figures 1(c) and 1(d) is y = 85.41(x+1)–1.9962 (R2 = 0.989), and the exponent, –1.9962, is the slope of the line in Figure 1(d). Data in the possession of Rogers Hollingsworth and Ellen Jane Hollingsworth, to be reported in a forthcoming study by Robert Hanneman, David Gear, and Rogers Hollingsworth. For methodology, see Newman, 2005; Hollingsworth et al., 2008.

 
Analysts generally portray a power-law distribution as shown in Figure 1. A distinguishing feature of a power-law distribution is that when plotted on a double logarithmic scale, it appears as a straight line with a negative slope (see Figures 1(b) and 1(d)). The key factor in a power-law distribution is a quantity generally referred to as an exponent, which essentially describes how the distribution changes as a function of the underlying variable. The power-law distribution has no cutoff value and is often described as being ‘scale free’—a term rooted in the statistical physics literature (Watts, 2003, pp. 104–07; 2004, p. 250).

Complex networks often follow a power-law distribution, with a few highly connected hubs or nodes holding together a large number of small nodes. Scale-free networks are generally dominated by hubs. It is in networks with power-law distributions that many ‘extreme’ events are observed, quite unlike the situation with ‘normal’ distributions (Barabási, 2002; Barabási and Oltvai, 2004; Taleb, 2007).

Analysts in the new science of networks have discovered that power-law distributions are quite pervasive in both the social and natural worlds. A few examples in the social world include the intensity of wars from 1916 to 1990, income and wealth within countries, the sizes of cities, the number of citations received by scientific papers, distribution of major scientific discoveries across organizations over time, the times words appear in a text, the size of firms within and across industries, power grids and transportation systems across countries and over time, and ‘crashes’ in financial markets (Mandelbrot and Hudson, 2004; Sornette, 2003; Newman, 2005; Zipf, 1949). Scholars have also found a distinctive architecture in the distribution of phenomena in the natural world—e.g., earthquakes, the networks in which neural cells are connected, the cellular metabolic networks of dozens of different organisms. In short, fundamentally different systems in the social and natural world have some of the same architectural design, especially if they are part of a complex (scale-free) network (Newman et al., 2006).

4.4 General binding problem
One of the most common issues confronting scientists in many fields is what is known as ‘the General Binding Problem’. This is concerned with why different types of phenomena are attracted to each other, how strongly and for what duration they are attracted, and what consequences ensue. Over the decades, both natural and social scientists have addressed this problem as they have studied complex networks, leading to increased communication across fundamentally different disciplines as scientists from such disparate fields as sociology, physics and chemistry increasingly interact with one another. Contemporary examples are Harrison White and Duncan Watts, two prominent sociologists at Columbia University, who have PhDs in the natural sciences but derived many of their key sociological insights from physics and mathematics. However, sociologists have a long and distinguished tradition of studying the general binding problems of social cohesion and solidarity (Durkheim, [1893] 1984) and more recently with the work based on network node connectivity (Moody and White, 2003).

In the field of socio-economics, the general binding problem has been raised in recent years not only for complex networks but also under the guise of complementarity. Colin Crouch, former President of the Society for the Advancement of Socio-Economics, observed that socio-economists and natural scientists were wrestling with the general binding problem when he pointed out that much of the literature on economic governance uses some of the reasoning of chemists and biologists when they have employed the concept ‘complementarity’ (Crouch, 2004; Crouch et al., 2005). In Crouch's discussion, complementarity exists when two or more dissimilar actors/agents (e.g., firms, institutions, macromolecules, etc.) are parts of a relationship due to underlying logic or rules, non-random in nature, a relationship sometimes threatened by instability.

The individual from whom some theorists on socio-economic governance (Hollingsworth and Boyer, 1997) derived considerable insight and inspiration on the general binding problem was Caltech chemist Linus Pauling—certainly the most creative scientist to emerge from the U.S. and probably the most important chemist of the twentieth century. Just as theorists of economic governance have been interested in the logic by which a particular type of market coheres to or is associated with a particular type of state, associative system, etc., Pauling throughout much of his career addressed a comparable problem—attempting to understand the logic with which atoms of particular molecules would bond to each other, why particular molecules were either loosely or tightly coupled, and with what consequences. Thus, some socio-economists and Pauling have been addressing the same theoretical problem: Why are different phenomena attracted to each other, how strong and for what duration is the attraction, and with what consequences? Addressing this problem, Granovetter (1973) wrote one of the most frequently cited papers in sociology during the latter half of the twentieth century; significantly, he had the key insights for his paper as a student in a chemistry course.

Pauling's most significant contribution to chemistry was his theory of chemical bonds and complementarity. His approach to how bonding is expressed in terms of complementarity was first introduced into the chemistry literature in 1940 in a very significant paper co-authored with a young German physicist, Max Delbrück (later also a Caltech professor). They wrote that ‘in order to achieve maximum stability, two molecules must have complementary surfaces, like die and coin’ (Pauling and Delbrück, 1940, p. 78). The idea that atoms from dissimilar molecules would be attracted to each other became a key component of bonding and complementarity in modern chemistry and biology. Later, Delbrück became a mentor to James D. Watson, and the logic of the Pauling–Delbrück 1940 paper about bonding and complementarity provided one of the key insights for Crick and Watson to solve the structure of DNA, one of the most important scientific discoveries of the last century.

The history of several academic fields could be written around the effort to understand why phenomena with differences as well as similarities are attracted to each other (Moody and White, 2003).³ One example of binding among mutually exclusive elements is found in the history of twentieth-century enzymology and immunology. Many scientists suggested that antigens and antibodies are attracted to each other like a lock and a key. Biologists long argued that an antigen and antibody were attracted to each other because there was a ‘complementarity’ in the way that their shapes fit or complemented one another—in much the same way that a lock fits or complements the key for which it has been designed.⁴ The antigen in the metaphor was the lock and the antibody the key (Serafini, 1989, pp. 99–100).

Jerne's research—for which he was awarded a Nobel Prize in 1984—is especially suggestive for socio-economists who address why particular governance structures are attracted to one another. Just as there is not a precise, one-to-one relationship between any particular type of state, market or associative structure, so Jerne found that an antibody does not have to fit precisely to the antigen to have an ‘affinity’. In short, ‘a key doesn't have to fit 100 percent to open a lock’. The same key can fit multiple locks (Söderqvist, 2003, p. 177). Similarly, only particular kinds of state structures are attracted to particular kinds of markets. In nature, there are no perfect fits between antigen and antibodies; in the social sciences we also find that there is no perfect fit among different governance structures.

Is there some underlying logic as to how different governance structures are attracted to each other, or is the process by which governance structures fit together in particular societies simply a result of chance? The work of various scholars studying the socio-economics of capitalism includes analyses of why different institutional arrangements (e.g., forms of governance) bond to each other (Hollingsworth and Boyer, 1997; Hollingsworth et al., 1994; Whitley, 1998). This scholarship argues that there are a number of institutional arrangements/modes of governance (markets, types of hierarchies, networks, associations, state structures, communities and clans) for coordinating relationships among various economic actors. Though each of these seven governance modes has its own distinctive logic, none exists in a pure or ideal form. Each is found only in some kind of combination relationship with other modes of governance. Governance modes exist in relationship with each other in often-unstable configurations.

Moreover, there are many types of each of the above seven modes of governance. For example, Boyer (1997) demonstrated that there are many types of markets, and hereafter it should be obvious that it makes no sense for anyone to suggest that an economy is coordinated by a ‘pure market’, the ‘free market’, etc. It is hardly necessary to argue that there are many kinds of states. Because each type of governance may exist in combination with many other types, the possible combinations of governance structures is very large, although extraordinarily small in comparison to the combinations which computational biologists are facing (Kitano, 2002; Hood, 2003). Because the problems faced by scholars in these fields are quite similar, social scientists should be very attentive to the biologists' methods and strategies.

There are several critical theoretical issues about the binding of different governance arrangements which socio-economists should address: What is the logic by which one type of governance is attracted to another or repelled?; What is the logic by which governance configurations are tightly coupled or bonded in some societies, while elsewhere they are loosely coupled? This problem relates to why there is variation in the coherence of governance structures, both within and across societies—a theme prominently discussed in some of the varieties of capitalism literature as well as in the literature on governance of different sectors in the same country (Campbell et al., 1991; Herrigel, 1996, 2005; O'Sullivan, 2005).

Physicists and chemists have demonstrated that bonds are very strong when nuclear forces give rise to small systems, whereas bonds holding super-molecules in biology are much weaker. Similarly, in the socio-economic world, in small societies which are relatively homogeneous in ethnicity and religion, the bonds holding a society together tend to be relatively strong. On the other hand, in societies which are larger and have a great deal of ethnic, religious and linguistic diversity, the bonds holding the parts of the society together are much weaker (Hanneman and Hollingsworth, 1984; Granovetter, 1973).

Concerned with how the constituent parts of societies emerge and bond together, some scholars have long focused on the processes of nation and state building (Hollingsworth, 1971; Grew, 1978). Some have studied the processes of mergers and acquisitions of firms (Williamson, 1985). Others have written about the conditions under which capitalists and workers organize and how they relate to each other (Schmitter and Streeck, 1981; Streeck, 1992; Streeck and Yamamura, 2001; Offe and Wiesenthal, 1980). How nodes of networks are connected to each other has become a major research issue among those who study complex networks—for sociologists as well as for physicists (Powell et al., 2005; Uzzi and Spiro, 2005; Moody, 2004; Kogut, 2000; Padgett and Ansell, 1993; Newman et al., 2006).

Theorizing about how bonds hold phenomena together is only one side of the coin. The binding problem also concerns the process by which things come apart, a concern that cuts across many disciplines. Physicists and chemists have long been interested in the process by which solids break down. One of the leading areas of biological research is the mechanisms of aging and death—the process by which parts of cells become dysfunctional and lead to cell death. In the social sciences, scholars have long been fascinated with the breakdown of empires and states—e.g., the collapse of ancient empires, the downfall of the old regime in France, the collapse of the Austro-Hungarian, Ottoman and British Empires, the disintegration of the Soviet Union and Yugoslavia (Kennedy, 1987; Tainter, 1988).

4.5 Multi-level analysis
To understand why societies come together or break apart requires a multi-level form of analysis—micro, meso and macro research strategies to explain how individuals and institutions relate to one another (Hollingsworth et al., 2002). In many fields of science, a recent concern has been to overcome the tendency to engage in micro-reductionism, extreme forms of micro-level analysis. From the seventeenth century to recent decades, micro-reductionism was the dominant scientific strategy for understanding reality. If reality resembled a machine, it followed that to understand the machine, it made sense to deconstruct it into its component parts. For such reductionists, reality was to be understood only at the level of parts—e.g., protons, electrons, atoms, molecules in the natural sciences or individuals in the social sciences. In the social sciences, extreme micro-reductionism has often been characterized as methodological individualism (Hodgson, 2007).

However, wise investigators are reductionists only to obtain points of entry to complex systems. They are very much aware that parts or individuals are embedded in complex environments. As scientists in more recent years have become more sophisticated, they have engaged in a great deal of thought about interactions across different levels. Hence, in biology, a scientist may be a specialist in molecular biology but, at the same time, very concerned with phenomena from subatomic particles to atoms as well as phenomena above the molecular level such as cell biology, systems biology, whole-organism biology, population biology and even the global environment. Physical scientists also work at multiple levels. The logic of doing multi-level analysis and of moving beyond extreme reductionism is similar in both.

Similarly, the social sciences in recent years have been increasingly involved in multi-level analysis. Regardless of the field of science, analysts are increasingly viewing the world as a network of interacting components. Reality may be approached from the bottom up, or from top to bottom. Most scientists, whether social scientists or natural scientists, centre their research on only one level; very few systematically conduct research at multiple levels. E. O. Wilson of Harvard and Gerald Edelman are good examples of understanding how phenomena at one level are constrained by or interact with phenomena at other levels (Wilson, 1998). In most fields, all levels are constantly interacting with one another, and thus, there are clear benefits of relating one's specialized research to a larger system. In some respects, one of the major goals of a socio-economics research agenda should be to understand how levels interact with one another. Economies have many levels of interaction. Units at any level can serve as building blocks for structuring behaviours at any other level. In short, there are many kinds of interactions and channels of communication across levels.⁵

Whether in the social or the natural world, entities at each level have their own logic (i.e., rules which constrain their behaviour). No level exists out of relationship with other levels. Thus in the social sciences, analysts cannot understand human behaviour out of its context any more than the cell biologist can understand the behaviour of a cell totally abstracted from its environment. Natural as well as social scientists tend to become specialized at only one level. But in the future, we must all attempt to have a better understanding of how each level is linked with other levels, with positive and negative feedback among levels.

In some respects, the biological and social worlds operate similarly. Different levels do not fit together because there was some designer with specific purpose. As Wolfgang Streeck (2002) has observed with respect to the social world, when there is institutional coherence and complementarity, it is often because we as observers happen to recognize distinct patterns. Both rationalism and functionalism grossly exaggerate the capacity of actors to know what they are doing before they have done it. In short, the emergence of biological and social systems constitutes a long-term evolutionary process, with each level interacting with a larger environment and resulting whole. At any moment in history, the total complexity of a system contains the resources and legacies of its past. Pre-existing complexes are very rarely completely wiped out. All systems—biological and social—consist of multiple levels of history with their multiple logics. However, the cumulative effect of small changes in the alternation of phenomena at any particular level can have big effects at any other level (Bak, 1997; Kauffman, 1993).

Multi-level analysis has strong relevance for contemporary socio-economists who are interested in theories of governance. In recent years theorists have recognized that the nation-state is embedded in a world which extends far beyond the state—whether it be a transnational community (e.g., the European Union) or the global level (Hollingsworth, 1998; Boyer and Hollingsworth, 1997; Held, 1991). In many respects the governance of our world is more complex than at any time in the past, occurring at multiple levels simultaneously. Decisions about the quality of our lives are made in (1) the private sector, (2) the local governmental level, (3) sub-national regional governments within nation-states, (4) the nation-state, (5) transnational regions such as the European Union, NATO, etc. and (6) the global level. Most analysts and theorists have long been confident that one level was more dominant than the others, and that they could know where most important decisions were made. Indeed, for most of the past few centuries, theorists tended to assume that the dominant form of coordination took place at the level of the nation-state. But at present, the degree to which most important decisions about the quality of our lives are made at the level of the nation-state is a matter of increasing concern. The governance of the contemporary world and the interconnections among governance, democracy and knowledge are far more complex than most observers recognize. No single level is decisive in shaping the world in which we live. Moreover, the levels are nested and linked with each other. One of the great challenges of our time is to comprehend the nature of this nestedness, to understand how governance, democracy and knowledge are linked together not only at each of these levels but how these processes are linked together across different levels. As societal institutions are increasingly nested in a multi-level world, we are all faced with the perplexing problem of how to govern ourselves. Clearly one of the great challenges of our time is to create a new theory of governance/democracy for coordinating institutions nested in a world of unprecedented complexity, one in which sub-national regions, nation-states, continental and global regions are all intricately linked.

	5. Concluding observations

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There is a general lack of theoretical consensus among socio-economists about their subject matter. Because of great fragmentation within their field, socio-economists often find it difficult to become interested in the work of others in the same field. Socio-economics is a highly clustered network, with highly permeable boundaries, a trait that could well turn into a strength. High permeability of fields offers the potential for cross-disciplinary collaboration. Scholars specializing on distinct problems and different methods, but in separate disciplines, often have the potential to transfer their interests to highly permeable fields. Hence, one of the great strengths of socio-economics is its potential to promote collaboration among those in differentiated research areas, with wide-ranging and complimentary interests (Moody, 2004, pp. 215–17; Abbot, 2001).

In recent years, two significant and interrelated changes have occurred which offer socio-economists new prospects to expand their theoretical perspectives on the world. First, declining dominance of Science I and increasing importance of Science II have provided new opportunities for understanding, especially because our new theoretical frameworks play an important role in influencing the problems we pose about the world and also in how we go about addressing these problems. Science II suggests that much of the world is dynamic and complex with large numbers of micro-level interactions which lead to the emergence of macro-level patterns of behaviour.

Second, we have discussed five common problems and processes which socio-economists share with many colleagues in the social and natural sciences. But among these problem areas, the one which intersects most often with the others is the analysis of complex networks. The study of such networks is one of the most rapidly expanding fields of research in the social, biological and physical sciences. Because of the importance of complex networks in multiple areas of science, we are at a moment in the history of modern science when there is high potential for serious interaction between socio-economists and the other sciences.

In 1992, Renate Mayntz published a very insightful, impressive and widely read essay on the influence of natural science on contemporary social science, in which she differentiated various types of borrowing which might occur across scientific fields—methods, concepts, theoretical models and fully specified theories. Mayntz set forth a strong version of the conditions under which there can be successful theory transfer from one field to another:

Theory transfer in a strict sense presupposes—and assumes—isomorphism between the empirical phenomena to be described and explained (i.e., a 1:1 relationship between the elements, properties, and relationships of interdependence in two phenomenal fields (p. 30; also see p. 69)

Without rejecting Mayntz's views about the potential for theory transfer across fields, we believe 15 years later that there is high potential for transferring theoretical models about complex networks across fields even when there is no strict isomorphism among the empirical phenomena to be explained.

Is there anything about complex networks which permits us to be a bit more optimistic about the potential for the transfer of theoretical models between the natural and social sciences than was the case when Mayntz's paper was published? Since 1992, the study of complex networks has emerged as a significant framework for facilitating the collaboration of social, biological and physical scientists in studying problems of a complementary nature. In complex networks—whether in the natural or the social world—one finds a growing assembly of elements (nodes) linked to one another whereby the formation of new links is not subject to a random process, but to the rule of preferential attachment.

But why should complex networks allow for a transfer of theoretical models across widely disparate fields? First, the study of complex networks did not emerge in specific fields like chemistry or biology, but was put forward by a heterogeneous group of social scientists, physicists and biologists. Moreover, the understanding of complex networks was built on long and well-established traditions of social network analysis. Thus, complex networks are opposite to previous reductionist coups de discipline as in socio-biology or in socio-physics. Second, paradigmatic examples for complex networks were not restricted to a special reference domain in the natural sciences, but had applications in both the natural and the social world. There was no need for borrowing and for transfers from the natural to the social sciences, because the initial work on complex networks was advanced by scholars already collaborating with their colleagues in the social, biological and physical sciences.

Third, complex networks, due to their focus on the micro-constitution of macro-phenomena, provide the underlying or generative mechanism for the area of investigation. Unlike the case of curve fitting in a macro-domain which in principle can be accomplished regardless of different underlying generative mechanisms, complex networks are dynamic micro-models of the evolution of an unfolding macro-configuration. ‘Starting from a small nucleus of nodes, the number of nodes increases throughout the history of the network by the subsequent addition of new nodes’ (Albert and Barabási, 2002, p. 71). Fourth, due to the relative simplicity and few constitutive rules for complex networks, it becomes a rather straightforward task to establish common models among complex networks in both the natural and social worlds.

Fifth, the framework of complex networks can be used by socio-economists as a much needed theoretical alternative to the neoclassical paradigm. More specifically, complex networks offer a rich laboratory to study highly relevant core problems in the socio-economic agenda. The following remarks offer several heuristic suggestions for utilizing complex network analysis for socio-economic problems.

High degree of applicability: In the social science literature, growth and preferential attachment have been identified as key factors in such different domains as migration processes, the growth of cities, industrial districts, scientific communities, business firms, citation indices, the World Wide Web, the clustering of innovations, the spread of ideas, the emergence of social movements, railway and airport networks, as well as research and development collaborations. Thus, complex networks are applicable across astonishingly wide areas of the socio-economic world.

Power-law distributions: In multiple sciences, scholars have discovered that growing networks with a preferential attachment rule have a natural tendency to self-organize themselves in power-law distributions: a few nodes become strongly connected, while others have few connections (Barabási, 2002, 2007). It remains an urgent task for socio-economists to explore the ramifications of power-law distributions for inequalities at the national, supra-national and global level.

Micro-variability: One of the most intriguing aspects of the new science of complex networks lies in the fact that several micro rules (i.e., growth and preferential attachment) tend to generate highly ordered macro-behaviours across a wide variety of domains. It must be added, though, that preferential attachment can come in a wide variety of different rules which all fulfil the requirement of preferential attachment. In principle, a link between a node A and B can be symmetric (A is linked to B and B is linked to A), asymmetric (A links with B or B links with A, but not vice versa), temporary (A links with B to time t*), a link can be rewired (A has been linked to B, but links now to C) and the like. Thus, the multiplicity of rules for preferential attachment brings about a degree of randomness and variation which can lead to different evolutionary histories, despite identical initial configurations and almost identical micro-rules. In this way, complex networks combine a certain degree of micro-variability with macro-features such as a power-law distribution.

Multi-level frameworks: Complex networks offer the possibility to study processes in a multi-level arrangement where different types of complex networks become linked in a vertical manner. An intriguing example of a multi-level network configuration is the study of obesity networks at the level of social relationships by American social scientists Christakis and Fowler (2007). Their description of multi-level configurations permitted dense collaborations among social scientists, medical researchers and cell biologists, demonstrating how complex networks operate across levels—linking the human cellular level to those of disease and the social world.

New perspective on robustness: Most complex networks tend to be quite robust. Nodes loosely linked to the overall network structure can fail, but the overall structure of a complex network has a high degree of error-tolerance. Nevertheless, an attack on central components of a complex network can lead to a complete breakdown of an entire network. Significantly, social scientists, biologists, neuroscientists, epidemiologists, security analysts and others are presently engaged in interdisciplinary research on how to repair attacks on node connections in a network (Sornette, 2003; Pastor-Satorras and Vespignani, 2004; Vega-Redondo, 2007; Barabási, 2007).⁶

While the study of complex networks provides a rich example of the transfer of theoretical models across different domains, we agree with Mayntz's reservations about the transferability of a complete theory across fields unless there is isomorphism among the specific empirical phenomena being explained. We need to be mindful of the tension between range and specificity. The study of complex networks is an example of how theoretical models may be applied across a wide range of different phenomena. But complex networks are theoretical models and not complete theories. Theoretical models permit us to rule out many options when we attempt to explain specific phenomena, though by themselves their explanatory power is limited, absent full elucidation of the specific mechanisms of how things work (Edelman, 2006).⁷

Returning to the terminology introduced at the beginning of this paper, we hope that in the course of our searches for common models and methods across different disciplines, we will commit no serious -type errors. In the process of emphasizing the importance of complex networks to a socio-economics agenda, we believe that we have minimized the potential for large β-type errors. As we undertake the active transfer of the ever-expanding stock of common problems, metaphors, methods and/or models from other fields to socio-economics, we should be substantially advancing our understanding of socio-economic realities. In this way we will contribute to diminishing the cultural divide between the natural and human sciences which C. P. Snow and others have found so frustrating.

	Funding

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Support was provided by the National Science Foundation, the Andrew W. Mellon Foundation, the Alfred P. Sloan Foundation, the University of Wisconsin (Madison), and the University of California San Diego. In addition, we thank Stefan Potmesil and Richard Fuchsbichler of the Austrian Ministry of Economics and Labor as well as Wolfgang Neurath and Martina Hartl of the Austrian Ministry of Science and Research.

	Acknowledgements

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Ellen Jane Hollingsworth and David Gear offered vigorous criticism and research assistance on multiple drafts of this manuscript. Their efforts were indispensable. Robert Hanneman, Jerald Hage, and Richard Whitley provided helpful comments on earlier drafts; Gerald Edelman, Ralph Greenspan, Robert Boyer, and Robert Hanneman helped to educate us on many of the issues discussed in the paper. Variations of this paper were presented before the following organizations: CEPREMAP in Paris, the Austrian Ministry of Science, Institute Para Limes (the Netherlands), and the University of Ljubljana.

	Notes

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¹ Science I and Science II should not be confused with concepts Mode I and II discussed in Nowotny et al. (2001).

² One of the most sophisticated studies on the theoretical process of growth and preferential attachment in complex networks is by Powell et al. (2005). This study, along with Padgett and Ansell (1993) on the rise of the Medici during the Renaissance in Florence, is among the few analysing rules and norms which shape the emergence and stability of specific types of preferences in complex networks over extended periods of time.

³ Historically, complementarity as used by scholars in most fields does not address phenomena having similarities. Bonding and complementarity theory generally involves interconnectivity of fundamentally different components. Throughout the twentieth century, a critical puzzle for many scholars has been how mutually exclusive and dissimilar phenomena are attracted to each other.

⁴ Emil Fischer, the great organic chemist and biochemist, is credited for postulating the "lock and key" metaphor in 1894. He was the second recipient of the Nobel Prize in Chemistry in 1902 (Cramer, 1994).

⁵ For elaboration of these ideas, see Boyer and Hollingsworth, 1997; Arthur, Durlauf and Lane, 1997, especially pp. 114.

⁶ Interestingly, the literature on counterterrorism and maintenance of robustness of the world's banking system borrows from research about the robustness and vulnerability of complex networks in both the natural and social sciences (Vega-Redondo, 2007; Taleb, 2007).

⁷ Rogers Hollingsworth expresses his gratitude to Gerald Edelman for many conversations about scientific explanations, though the views expressed herein are not necessarily those of Edelman.

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