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SOCIO-ECONOMICS AND A NEW SCIENTIFIC PARADIGM

Rogers HollingsworthKarl H. MüllerEllen Jane HollingsworthDavid M. Gear

INTRODUCTION

For several hundred years, the dominant framework shaping Western science has been the Descartes-Newtonian paradigm. This framework

was powerful in shaping the thinking of both natural scientists and social scientists. An alternative view of explaining reality has slowly been

emerging, and the influence of this new perspective has rapidly accelerated in recent decades. We focus on these two scientific paradigms,

especially the more recent one and suggest that it has considerable potential for enriching the field of socio-economics and assisting socio-

economists to understand the connections between their research and other fields of social and natural science.

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 its relentless criticism of the neoclassical paradigm, but has remained relatively weak in developing a comprehensive

theoretical alternative to the dominant neo-classical framework. A major exception was the effort by Amitai Etzioni to provide building blocks

for the ethical and moral bases of a socio-economics research program. In our view, socio-economics would benefit from theory and model

frameworks strictly independent from neo-classical rationality assumptions and situated far from neo-classical equilibrium. The emerging

paradigm heightens the potential for such developments.

Despite the theoretical weaknesses and the strong commitment to inter-disciplinarity in the socio-economic agenda, learning from other

disciplines has remained as difficult for socio-economics as for most other scientific fields. Learning from other disciplines, aside from being

intrinsically difficult even in a hybrid field like socio-economics, confronts two different types of errors. Using terminology from statistical test-

theory, learning from other disciplines can produce α-type errors by accepting analogies, methods or models from other disciplines which turn

out to be highly questionable and generate no cognitive value, let alone surplus value. Or learning from other disciplines may also generate β-

type errors by rejecting highly appropriate and very fruitful analogies, models or methods from outside one’s domains. Socio-economics very

seldom commits α-type errors, but like most other scientific fields, has a high propensity for β-type errors.

We suggest that a new emerging scientific landscape offers socio-economics the much needed theoretical alternative to neoclassical reasoning.

Using perspectives and concepts developed by the natural sciences during the past half-century, socio-economists can uncover new models and

theoretical components with which to engage in theory construction independent of the classical Descartian-Newtonian paradigm. As well,

socio-economists have potential to advance the theoretical insights of their natural science colleagues, so that there is the potential for more

serious interaction among social and natural scientists.

The Transition from Science I to Science II

This paper focuses on the fundamental re-organization and re-configuration of scientific knowledge which is rapidly occurring. Building on

insights by Nicholas Rescher (1978), we realize that by the 1950s a new phase had emerged in the structure and organization of scientific

knowledge, a phase with the potential for dramatically altering interdisciplinary learning across both the natural and social sciences.

For several hundred years, much of Western science has been influenced by a fundamental distinction, a leading metaphor and a dominant paradigm. The core paradigm was described in Thomas S. Kuhn’s Structure of Scientific Revolutions and was based on the Principia

Mathematica (1687) by Isaac Newton. The fundamental distinction and leading metaphor for the Newtonian paradigm had already been

proposed by René Descartes. In his Meditationes de Prima Philosophia , Descartes constituted two ontological kingdoms, res extensa for





the natural world and the natural sciences and the other one for mental substances ( res cogitans ). This kind of Descartian dualism paved the

way for separating science into two fundamentally different cultures, operating on two sets of principles, criteria and goals. With the rapid

differentiation of scientific disciplines during the nineteenth century, many philosophers and scientists addressed the cognitive distances and gaps

between the natural universe and the mental or social universe and their corresponding scientific matrices.

For Descartes, the dominant metaphor for the natural world became that of a machine as put forth in Part IV of his Discourse on Method .

While his metaphor was originally presented as a way of understanding living things, over time it has been generalized to be a view about theentire world. Descartes’ metaphor has been interpreted not only as a description of how the world operates but as a prescription for how to

study the world. Adopting his mechanistic view, we have all too often adopted an overly simplified view of the world, of the relation of parts to

wholes. Scholars have even extended the metaphor to that of an engineer, a watch-maker or a designer.

Turning to the cognitive organization of the Newton-Descartes paradigm—to the period we term Science I—its characteristic features can be

captured by a hierarchy of levels both in the scientific domains and in the socio-natural universe (see Table 1). The leading epistemological

vision within the Science I paradigm lies in its heavy emphasis on reductionism.

TABLE 1: DIFFERENCES IN THE PARADIGMS OF SCIENCE I AND SCIENCE II

Science I Science II

Dominant Paradigm Classical PhysicsEvolutionary Biology,

Science of Complexity

Theoretical Goal General, Universal LawsPattern Formation,

Pattern Recognition

Theory Structures Axiomatic, ReductionistPhenomena Nested in Multiple Levels

of Reality SimultaneouslyForecasting Capacities, Ability t oMake Predictions

High Low

Complexity Low High

OntologyDualism

(res extensa/res cogitans )

Emphasis on the Interconnectedness of Phenomena

Leading Metaphors Clocks Complex Networks, Living Cells, Clouds

Cognitive Distances between SocialScience and Natural Sciences

High Medium

Inspirational ScientistsRené Descartes, Isaac Newton,Adam Smith

Charles Darwin, Ilya Prigogine, Gerald Edelman

For example, societies are built up from individuals, individuals from cells and their neural organization, cells from molecules, molecules

from atoms and atoms from a small set of elementary particles. The key to understanding reality is to comprehend the parts of a system, and

how they operate together. Finally, the dominant theory formation in the Newton-Descartes paradigm lies in the identification of universal laws

and the clustering of universal laws. This mentality suggests that we can understand the interactions among variables and that only a relatively

few variables need be manipulated to change nature or a society’s history (Hollingsworth, 2006).

Even though the visions are often grand, the mentality is that of the engineer with an emphasis on efficiency and redundancy: design projects

as simply as possible and keep interactions among the necessary parts to a minimum. When the engineering mentality shapes policy or designs

projects, logic is expected to prevail, but for failsafe purposes, designers usually include in their plans some redundancy (i.e., the performance of

the same function by similar elements). Whether the project is built from the ground up or whether there is a great deal of bricolage or

incrementalism, the mentality of the design is to keep the components or plan as uncomplicated as possible. In very complex and sophisticated





projects, there is some feedback modeling or other elements from control theory, but at all stages of the design, irrelevancy is to be avoided.

Each part is designed to complement the next part.

Contrary to the Cartesian design, a massive re-configuration has been emerging for approximately 150 years, starting with the Darwinian

Revolution in the middle of the nineteenth century, accelerating from the 1950s onward toward a new science regime here labeled Science II. In

Science II, a broad range of trans-disciplinary foundations have led to a fundamentally new scientific paradigm with a complex configurative

structure. Science II is not grounded on engineering and clockwork metaphors but is primarily concerned with an effort to comprehend both thenatural and the social world in terms of evolution, complexity, self-organization, population dynamics, innovations, etc. Whereas a view of the

world shaped by the influence of Newton and Descartes is comparatively tidy and predictable, the new configuration emphasizes the complexity

and unpredictability of the world, open to many more possibilities (Kauffman, 1992).

The new perspective became increasingly widespread after physicists and computer scientists began to demonstrate in the 1960s and

1970s that even simple equations can produce results which are essentially unpredictable. Advances in genetics and neurosciences have also led

to conceptions of science which increasingly emphasize the important role of chance and unpredictability in explanation (Edelman, 1987). The

notions of general laws or axioms have been recast within the Science II world 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 the 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: 329; see Lewin, 1993). The new scientific paradigm so rapidly diffusing across academic disciplines suggests that the world does not

change in predictable ways (Mayr, 1991). Systems have an inherently nonlinear dynamic quality to them. The games in which actors are

engaged are ever changing, and even in the same game, the rules keep evolving. In short, there is an enormous amount of chance in shaping the

world. There is a great deal of co-evolution in the world, in which very small changes can have major 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 in explaining outputs. Too,

effort to engage in theory construction lie not only in the construction of patterns but also in much weaker requirements for scientific explanations

than in the Descartes-Newtonian paradigm.

Three scientists whose work very much inspired and embodied the Science II paradigm were Charles Darwin, Ilya Prigogine (physicist and

Nobel laureate), and Gerald Edelman (also recipient of a Nobel Prize). Unlike the static equilibrium and universal laws in the Descartes-

Newtonian paradigm, Darwin, Edelman, 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. All three understood the

importance of retrospective analysis in order to understand reality (Prigogine and Stengers, 1997).

In the Science II paradigm, scientists search for regularities within systems, but unlike neo-classical analysts they view systems as tending to

move far from equilibrium. Because a system is always changing, at some point it evolves into what appears to be 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 (Wallerstein, 2004). Some of the implications from John von

Neumann’s work have advanced the theory of complexity: the idea that systems with a large number of interacting parts are open to their

environment and have self-organizing internal structures (Waldrop, 1992; Macrae, 1992). Von Neumann’s work has had enormous impact on

the field of socio-economics as well as biology, geology, meteorology, and computer science. A central tenet of much of complex systems

analysis is that large-scale collective behaviors result from repeated nonlinear interactions among constituent parts, whereby wholes tend to be

much more than the sum of their parts (Bunge, 2003). Increasingly analysts maintain that most complex systems are not susceptible to







Economic Behavior by John von Neumann and Oskar Morgenstern (1944), originally conceived as a radical revolution for economic theory,

rapidly diffused into biological and other sciences (Lewontin, 1961). Mitman (1992) brilliantly demonstrated how entomologists borrowed

models from social scientists who studied social cooperation and associations in order to understand the social behavior of insects. In recent

decades, various complex models and/or methods have moved from their original domain to a wide spectrum of disciplinary fields in both the

natural and social sciences. For instance, Gerald Cory and others are working to integrate evolutionary neuroscience with social exchange and

economics, trying to understand human sociality from the standpoint of physiology (Cory, 2006; Levine, 2006; Wilson, 2006; Lynne, 2006).

Entire new institutes and research programs have emerged 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 some of the world’s most prominent physicists, biologists,

sociologists, economists, and anthropologists (Waldrop, 1992).

EXAMPLES OF SOCIO-ECONOMISTS AND NATURAL SCIENTISTS WORKING ON COMMON PROBLEMS

For illustrative purposes, we briefly discuss four general types of issues in which socio-economists are working with or parallel to their

colleagues in other fields. These are (1) multi-level analysis, (2) the general binding problem, (3) the structure and dynamics of complex

networks, and (4) power-law distributions.

1) Multi-level Analysis

In most every field of science, one of the major concerns in recent decades has been to overcome the tendency to engage in micro-

reductionism. 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 have become more sophisticated, they have engaged in a great deal of thought

about causes and effects across different levels. In biology, a scientist may be a specialist in molecular biology but also concerned with

phenomena ranging from subatomic particles to the global environment. Although most scientists become specialists at one level of analysis,

E.O. Wilson of Harvard and Nobel laureate Gerald Edelman are examples of how good scientists attempt to understand how phenomena at

one level are constrained by or interact with phenomena at other levels (Wilson, 1998; Edelman, 1987).

Similarly, the social sciences in recent years have been increasingly involved in multi-level analysis. In social sciences too, there are various

multi-level investigations, some of which are illustrated in Table 2. The table presents two different approaches by social scientists—one for

spatial arrangements in political systems and the other for individuals and structures in entire social systems.

TABLE 2: EXAMPLES OF MULTI-LEVEL ANALYSIS*

Social Sciences

Feedback Spatial Analysis Structural Analysis

Global Rules, Norms, Habits, Conventions, etc.(Institutions)





Transnational Regions (e.g., European Union) Institutional Arrangements (Markets, Hierarchies,States, etc.) and Institutional Sectors (Financial,Educational, Business, Research Sys tems, etc.)

Nation State Organizations, Firms

Subnational Region Small Groups, Families

Local Level Individuals

* It is assumed conceptually that each level influences all levels below it, and that there is feedback among all levels.

Most scientists, whether social scientists or natural scientists, center their research on only one level; very few systematically conduct

research at multiple levels (for an exception, see Burns and Flam, 1987). However, scholars working on one level have better opportunities for

rich scholarship if they are sensitive to and communicate with scientists who work at other levels. For examples in the field of socio-economics,

see Boyer and Hollingsworth (1997).

2) General Binding Problem

One of the most common issues confronting scientists in many fields is “The General Binding Problem,” 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. Both natural and

social scientists have addressed this problem.

In the field of socio-economics the general binding problem has been recently raised in 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 both wrestling

with the general binding problem when he pointed out that a good bit of the literature on economic governance encompasses some of the same

reasoning in which chemists and biologists have engaged when they have used the concept “complementarity” (Crouch, 2004; Crouch et al.,

2005). Implicit in Crouch’s analysis was that socio-economists and natural scientists had shared views of complementarity. To Crouch,

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. This type of relationship is threatened by instability because of both endogenous and

exogenous forces. There is varying strength of the constituent parts of the configuration, and among the constituent parts, there is often mutual

compensation for deficiencies: where one is weak, the other may be strong. Two key theoretical problems with this approach have been what

keeps the constituent parts of the configuration together, and how and why parts come into relationships in the first place.

The individual from whom some theorists on socio-economic governance (Hollingsworth and Boyer, 1997) have derived considerable

insight and inspiration was the Caltech chemist and Nobel laureate Linus Pauling, certainly the most creative scientist in the U.S. and probably

the most important chemist of the twentieth century. Just as some theorists of economic governance have long been interested in the logic by

which a particular type of market coheres to a particular type of state, associative system, etc., Pauling addressed a comparable problem, trying

to understand the logic with which atoms of particular molecules would bond to each other, why the bonding of particular molecules was loosely

or tightly coupled, and with what consequences.

Perhaps Pauling’s most significant contribution to chemistry was his theory of chemical bonds and complementarity. His approach to





bonding in terms of complementarity was first introduced into the chemistry literature in 1940 in a very significant paper co-authored with the

German physicist, Max Delbrück (later recipient of a Nobel Prize). Pauling and Delbrück wrote that “in order to achieve maximum stability, two

molecules must have complementary surfaces, like die and coin” (Pauling and Delbrück, 1940). 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, the logic

embedded in the Pauling-Delbrück paper about bonding and complementarity provided the key insight for Crick and Watson to solve the

structure of DNA.

One example of binding among mutually exclusive elements is found in twentieth century immunology. Scientists long suggested that

antigens and antibodies were attracted to each other like a lock and a key. Indeed, it was in antigen-antibody reactions that complementarity

often received the most attention. Biologists had 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. In the metaphor, the antigen was the lock and the antibody the key (Serafini, 1989: 99–100).

Niels Jerne’s Nobel Prize-winning work on bonding and complementarity between antigens and antibodies 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-onerelationship 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: 177; Jerne, 1993). In nature, there are no perfect fits between antigen and antibodies; in the social sciences we find that

there is no perfect fit among different governance structures.

Socio-economists’ work on capitalism includes analyses of why different institutional arrangements (e.g., forms of governance) bond to

each other (Hollingsworth and Boyer, 1997; Hollingsworth, Müller and Hollingsworth, 2002; Hollingsworth, Schmitter and Streeck, 1994;

Nelson, 1993; Whitley, 1998). This work argues that there are a number of 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 to be found only in some kind of combination

relationship with other modes of governance. In other words, discrete governance modes exist in relationship with each other, often in unstable

configurations. Because each type of governance may exist in combination with many other types, the number of possible combinations is very

large. Even so, the number of possible combinations is much smaller than those which computational biologists are facing (Kitano, 2002; Hood,

2003).

There are several critical theoretical issues about the binding of different governance arrangements for which we have poor answers in the

social science community.

(1) What is the logic by which one type of governance is attracted to another or repelled? Is there a logic by which one kind of

market ends up in a configuration with a particular type of state, a particular kind of association, etc.? [1]

[1] It is not as though this is a neglected research problem in the social sciences. Wolfgang Streeck’s work on the associations of workers and business interests is certainly noteworthy in this respect.

(2) What is the logic by which governance configurations are tightly coupled or bonded in some societies, while elsewhere they

are loosely coupled ? Why is there 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, Hollingsworth, Lindberg, 1991; Herrigel, 1996).





(3) What is the logic for understanding governance structures at different levels of the social world? Boyer and Hollingsworth

(1997) have emphasized that one of the most complex issues is to understand the complementarities of governance structures at

each level of the social world, how each level interacts with others, and with what consequences.

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. As Bunge observes “there are no all-encompassing explanations because there are no

one-size-fits-all mechanisms” (Bunge, 2003: p.22).

Focusing on how the constituent parts of societies emerge together, some scholars have long been concerned with the processes of nation

and state building (Hollingsworth, 1971; Grew, 1978). Economists have been concerned with the processes of mergers and acquisitions of

firms (Williamson, 1985). Moreover, some socio-economists have written stimulating studies about the conditions under which capitalists and

workers organize (Streeck and Schmitter, 1985). 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, and this concern cuts across many disciplines. Thus, physicists and

chemists have long focused on the process by which solids break down, for decades using chromatography to separate compounds into

constituent parts. In the social sciences, scholars have been fascinated with the breakdown of empires and states—e.g., the downfall of theRoman Empire, the downfall of the British Empire, the disintegration of the Soviet Union (Kennedy, 1987; Beissinger, 2002).

3) The Structure of Complex Networks

In recent years, one of the most common subjects drawing the attention of physicists, biologists, computer scientists, and social scientists

has been the study of complex networks. Of course study of networks is not new. Whereas most of the older work on networks was essentially

descriptive and static, the new science of complex networks is highly theoretical, with a great deal of empirical analysis. The new scholarship

engages in dynamic analysis, tends to observe phenomena in a continuous state of change, and analyzes complex networks as evolving

structures. The scholarship reports that most complex networks are not developed by designers or engineers but have been unplanned, very

decentralized, self-organizing, and emerged over long periods of time.

In contrast to traditional reductionists and some early students of networks, the current group of complex network analysts tends to think

that nothing happens in isolation. We live in a small world in which everything is linked to everything else (Barabási, 2002; Watts, 2003). In the

new literature, the structure of complex networks is the key to understanding much of the world around us (Barabási, 2002). The scholarship

suggests that the difference between things not in networks and parts of networks is very subtle. Networks are analyzed as part of a system,

and the analysis methodologically is quite anti-reductionist. As analysts have studied how complex networks evolve, they conclude that they are

self-organizing, not steered or directed by some external system. They emerge from a disordered collection of interacting parts. .

Those who study complex networks describe two types: the aristocratic type and the egalitarian type. Both networks have many different

nodes, but the aristocratic type network has a few nodes connected to many other nodes. This type of network is characteristic of the world’s

aviation system. Globally, there are a few major hubs—airports such as Chicago O’Hare, London Heathrow—with each having links with

hundreds of smaller airports, but each smaller airport is linked to relatively few other airports. Alternatively, there are egalitarian type networks

in which each node has only a few links to other nodes, but none has numerous links to others. It is the aristocratic type network which has

been of greatest interest to social scientists engaged in the new science of networks (Watts, 2003, 2004; Sornette, 2003).

Some of the foundation of the new science of networks in the social sciences was prepared by Robert Merton’s famous paper on “The

Matthew Effect in Science” (Merton, 1968), Herbert Simon’s celebrated work “The Gibrat Principle,” and Derek John de Solla Price’s

argument about cumulative advantage in science. Merton quoted 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,” and argued that rewards in

science go to those who already have rewards, and they continue to receive more and more, while those who are unrecognized—even if

deserving—remain unrecognized. Merton’s analysis was a variant on that of Pareto (1896) some years earlier: the rich tend to get richer,

generally at the expense of the poor. Herbert Simon (1955) demonstrated that business firms grow in a more or less random fashion but their

probability of growing is proportional to their current size. At about the same time, Simon Kuznets received a Nobel Prize in economics for

demonstrating a similar principle about the distribution of income within countries. Price, in a famous paper which laid the foundation for the

study of networks in citation of scientific papers, was one of the first to demonstrate that the pattern of citing scientific papers has a network

type structure, that papers receive citations in proportion to the number they already have. He labeled this process “cumulative advantage”

(Price, 1963; Newman, Barabási, Watts, 2006: 17–18).

As the new science of networks has emerged, there has been considerable emphasis on two underlying principles: growth and preferential

attachment. A node with more links than any other is far more likely to continue growing new links with other nodes. The older nodes have

distinct advantage over more recently established nodes. For example, urban geographers have observed that there is a systematic rank

ordering of cities whereby large cities are more likely to attract new arrivals than small cities.

One of the most interesting findings of the new science of complex networks is that two micro-rules (growth and preferential attachment)

appear to be both necessary and sufficient to generate highly ordered macro-behaviors across a variety of heterogeneous domains. The work of

Merton, Simon, and Price did not deny the importance of historical specificity in explaining the original success of a scientist’s discovery or

paper or the merits of a particular firm. Rather the idea is that regardless of the specific reasons for initial success, the successful are more likely

to continue reaping more rewards than the less successful. The rich have many ways of getting richer, some deserved and others not, but as far

as the resulting statistical distribution is concerned, the significant thing is that they continue to prosper relative to others (Watts, 2003: 110–

111).

Networks, whether aristocratic or egalitarian, are subject to change due to internal and external conditions. Internal network mechanisms

may become overloaded, as nodes cannot integrate and exchange new information. External conditions may overwhelm the network and

change previous patterns of growth and preferential attachment. Some of these changes, especially in aristocratic, small-world, networks may

be so extreme that they destroy the nodes with the most attached links, especially if they are tightly linked to each other, and the shape of the

network changes profoundly. Egalitarian networks are less subject to such extreme fluctuation.

Aristocratic, small world networks are in a sense more vulnerable, in that they occasionally experience what are known as “tipping points,”

the points at which the collective organization of phenomena change. “Tipping points” are critical states of change in part related to the node

relationships and their distribution. Among financial institutions, the failure of a large organization with many network links can induce so much

turbulence that a whole network can fail. The failure of a large financial institution could thus represent a “tipping point,” destroying the whole

system. It is with these insights that analysts in the new science of networks are able to explain “crashes” in stock markets going all the back to

the “tulip mania” in the seventeenth century, even the crashes that happen on the Internet, the contagion effects with fads in fashion, book

publishing, the media, and the spread of disease (Sornette, 2003; Pastor-Satorras and Vespignani, 2001, 2004).

4) Power-law Distributions

Scientists long believed that most observations in the social and natural world were distributed in a bell-shaped curve, alternatively known

as a Gaussian curve. Most social scientists have long assumed that most things fall within a normal distribution in which things have a well

defined average. A bell shaped curve has a sharp peak, which rapidly tapers off on each side (Figure 1.1). This kind of curve is so widespread




http://faculty.history.wisc.edu/hollingsworth/documents/Hollingsworth,M%C3%BCller,Holli ngsworth,Gear.Socio-Economics_and_A_New_Scientific_Par… 10/14

that scientists refer to it as a “normal distribution.” Observations are assumed to be independent of each other. A common example of a bell

shaped curve is the distribution of the height 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. However, many analysts 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.

With the emergence of Science II, analysts across different disciplines began to observe that the characteristic distributions in self-

organizing processes often have power-law distributions. Indeed, analysts in the new science of networks have discovered that power-lawdistributions are far more pervasive in the social and natural world than previously thought. As we observe in Figure 1.3, power-law

distributions do not have a peak at their average value. Rather, the distribution begins at its maximum value and then decreases steadily.

Moreover, power-law distributions have a clear asymmetry between a small number of high values and a large number of low values. Thus, as

examples of Figure 1.3, there are very few individuals with extreme wealth, while most individuals have very little wealth.





Analysts generally portray a power-law distribution as shown in Figure 1.4. The key factor in a power-law distribution is a quantity, the

exponent, which describes how the distribution changes as a function of the underlying variable. 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 (Figure 1.4). This contrasts with

a normal distribution plotted on logarithmic scales (Figure 1.2), which curves sharply downward to a “cutoff” value, quickly reaching zero. The

power-law distribution has no such abrupt cutoff (Watts, 2003: 104–107; 2004: 250). As an illustration, complex networks with a power-law

distribution have numerous nodes with few or no links and a small number of nodes with large numbers of connections.

Many disciplines in the natural and social sciences have discovered that there are vast amounts of reality in which things are distributed in a

power-law distribution, and this has had a startling impact on the way that analysts in the new science of networks have begun to study

phenomena. Network analysts have demonstrated that power-law distributions occur in many different kinds of phenomena other than

distributions of city size, wealth, scientific citations, scientific rewards, but also in the severity of wars, the frequency of use of words in human

languages, the number of papers scientists write, the number of hits on web pages, the sales of books and music recordings, cell metabolism,

and a variety of other phenomena with phase transitions in the natural world (Newman, Barabási, Watts, 2006).

CONCLUDING OBSERVATION

Our discussion of the search process in the new scientific knowledge base has brought to light several areas of research

going on across many scientific domains, in particular common problems which have high relevance to socio-economics. We

hope, returning to a terminology introduced at the beginning of this paper, that in the course of our explorations no serious α-

type error has been committed. Rather, we think that by introducing complex networks and their potential extensions as well

as a specific socio-economic network agenda, we have helped to eliminate a potentially large β-type error. As we undertake

active transfers of the ever-expanding stock of common problems, metaphors or methods and models used by natural and

social scientists as part of our socio-economic research strategies, we should substantially advance our understanding of social reality. In this way we could contribute to diminishing the cultural divide between the natural sciences and the rest of the

world which C.P. Snow and others have found so frustrating. [1]

[1] C.P. Snow was a British physicist, novelist, and administrator who became a scientific advisor to the British government inWorld War II. He is well known for his book about science and literature, The Two Cultures and the Scientific Revolution(1959). In it he argues that practitioners in each of the two fields know very little about the other and there is littlecommunication among them.

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