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The New Sciences of Networks & Complexity: A Short Introduction

2.2.2 Strengths and Weaknesses
A reliance on hubs can be advantageous or not depending on the system.

First, one has to note that resistance to randombreakdown is good news for both the Internet and the cell. In addition, the cell’s reliance on hubs provides pharmaceutical re­­searchers with new strategies for selecting drug targets, potentially leading to cures that would kill only harmful cells or bacteria by selectively targeting their hubs, while leaving healthy tissues unaffected.

Second, the ability of a small group of well-informed hackers to crash the entire communications infrastructure by targeting its hubs is a major reason for concern.

2.3 Some Examples of Applications
Over the past several years, researchers have uncovered scale-free structures in a stunning range of systems which include

  • the World Wide Web;
  • some social networks. A network of sexual relationships among people (from a research in Sweden) followed a power law: although most individuals had only a few sexual partners during their lifetime, a few (the hubs) had hundreds;
  • the network of people connected by e-mail;
  • the network of scientific papers, connected by citations, follows a power law: collaborations among scientists in several disciplines, including physicians and computer scientists;
  • business networks; a study on the formation of alliance networks in the U.S. bio-technology industry discovered definite hubs;
  • the network of actors in Hollywood: popularized by the game Six Degrees of Kevin Bacon, in which players try to connect actors via the movies in which they have appeared together. A quantitative analysis of that network showed that it, too, is dominated by hubs;
  • biological realm: in the cellular metabolic networks of 43 different organisms from all three domains of life, including Archaeoglobus fulgidus (an archae-bacterium), Escherichia coli (a eubacterium) and Caenorhabditis elegans (a eukaryote), it was found that most molecules participate in just one or two reactions, but a few (the hubs), such as water and adenosine triphosphate, play a role in most of them;
  • protein-interaction network of cells. In such a network, two proteins are “connected” if they are known to interact with each other. Investigating Baker’s yeast, one of the simplest eukaryotic (nucleus-containing) cells, with thousands of proteins, a scale-free topology was discovered: although most proteins interact with only one or two others, a few are able to attach themselves physically to a huge number; a similar result was found in the protein-interaction network of an organism that is very different from yeast, a simple bacterium called Helicobacter pylori.

Indeed, the more scientists studied networks, the more scale-free structures were discovered. These findings raised an important question: How can systems as fundamentally different as the cell and the Internet have the same architecture and obey the same laws? Not only are these various networks scale-free, they also share an intriguing property: for reasons not yet known, the value of nin the knterm of the power law tends to fall between 2 and 3.

A compelling question arises: How many hubs are essential? Recent research suggests that, generally speaking, the simultaneous elimination of as few as 5 to 15% of all hubs can crash a system.

3. The Science of Complexity

3.1 General Remarks
The focus lies on the innovative character of this new science, in terms of scientific development: mathematical, biological, as well as in terms of societal behavior, in particular in sociology but also in economics. Will industrial societies evolve to a new pattern of evolution/development under the influence of these new network facilities created by entirely new technologies? Relationship between individuals, or inter-subjectivity, will depend on the availability and accessibility of network and complexity methodologies. Therefore, uncovering new types of relationships enables more sustainable prospective scenarios on how our industrial societies will or could look like by the mid-21st century.

Important issues to be examined are democratic processes through the existence or ‘spontaneous’ emergence of networks. This new phenomenon becomes an important parameter in electoral campaigns, in major political processes as overthrowal of leaders, local and community issues. This very interesting domain is open for debate and reflection.

The state of knowledge about networking and complexity will play an increasing role in understanding the organization and functions of societies. Some most recent events and tendencies, in a large variety of domains, indicate the richness of applicability of these sciences: analysis and search for remediation of the worldwide financial crises; underlying political channels and possible solutions regarding the events in the Middle East; nature and size of social developments in nations with emerging economies; health research and disease dis­semination; impact of diminishing bio-diversity on human society and on a planetary scale etc.

The issues which have not yet found appropriate and durable (sustainable) answers will most likely find substantial progress with the application of these new sciences. The understanding of such phenomena requires other type of approaches – more holistic than reductionist – necessary for improved diagnosis and resulting in a better understanding and increased acceptance of proposed solutions.

In the case of world problems, the search for appropriate solutions by international or­ganizations within the present political frame shows quite clearly that progress can only be made by other approaches than the one used until now, based on scientific analysis and understanding, in which these new sciences will play a substantial role.

3.2 The Science of Complexity: Definitions, Properties & Tools

3.2.1 Definitions
Defining complexity remains not an easy task. Some definitions below are taken from publications and depend strongly on the viewpoint of the authors.

From Melanie Mitchell (2009):16

“Complexity is a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.”

From Roger Lewin(1993):17

“Complexity science offers a way of going beyond the limits of reductionism, because it understands that much of the world is not machine-like and comprehensible through a cataloging of its parts; but consists instead mostly organic and holistic systems that are difficult to comprehend by traditional scientific analysis.”

From the OECD Global Science Forum Applications of Complexity Science for Public Policy: New Tools for Finding Unanticipated Consequences and Unrealized Opportunities (2009):18

“Government officials and other decision makers increasingly encounter a daunting class of problems that involve systems composed of very large numbers of diverse interacting parts. These systems are prone to surprising, large-scale, seemingly uncontrollable, behaviors. These traits are the hallmarks of what scientists call complex systems.

An exciting, interdisciplinary field called complexity science has emerged and evolved over the past several decades, devoted to understanding, predicting, and influencing the behaviors of complex systems. The field deals with issues that science has previously had difficulty addressing (and that are particularly common in human systems) such as: non-linearities and discontinuities; aggregate macroscopic patterns rather than causal microscopic events; probabilistic rather than deterministic outcomes and predictions; change rather than stasis.”


16. Melanie Mitchell, Complexity
17. Roger Lewin, Complexity
18. “Report on: Applications of Complexity Science for Public Policy”


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