《科学》推出“复杂系统与网络”专题

 7月24日出版的《科学》杂志刊登专题——《复杂系统与网络》（Complex Systems and Networks），专题导言《关联》（Connections）开篇引用马丁•路德•金的名言——我们被困在无法逃避的相互关系网络中，任何事情，如果直接地影响了一个人，就会间接地影响所有人。 

 

导言说，混乱产生复杂性。从细胞内的分子运动到整个星球间的通讯，我们是网络的一部分。这一特殊的部分显示了科学家正如何在各学科领域间将网络分析推向极限。

 

专题包括1篇导言，4篇新闻，6篇观察（Perspectives）和1篇评论（Reviews）。 （科学网 梅进/编译）

 

以上资料转自：http://www.sciencenet.cn/htmlnews/2009/7/221811.shtm

 

 

Complex Systems and Networks

 

 Connections

 

Barbara R. Jasny, Laura M. Zahn, and Eliot Marshall

 

Science 24 July 2009: 405.

 

News:

 

Ourselves and Our Interactions: The Ultimate Physics Problem?

 

Adrian Cho

 

With 3.6 million of support from the European Union, a research team aims to develop a computer program that can analyze dialogue from Internet chat rooms and tell when people are growing excited, angry, and so on. The team is part of a small but growing number of physicists who are turning from atoms and electrons to study social phenomena such as terrorism, the growth of cities, and the popularity of Internet videos. Joining with social scientists, they treat groups of people as "complex socioeconomic systems" of many interacting individuals and analyze them using conceptual tools borrowed from physics, mathematics, and computer science. Last month, 130 researchers of various stripes gathered in Zurich, Switzerland, to discuss such work.

 

Econophysics: Still Controversial After All These Years

 

Adrian Cho

 

Traditional economic theory is fundamentally flawed, econophysicists say. It relies on "representative agent models" in which a hypothetical average Joe interacts with monolithic economic forces. Such models ignore correlations that lead to, say, booms and busts. To prove rigorous theorems, economists assume that market fluctuations follow a bell-shaped "Gaussian distribution," which underestimates the probability of big swings. Econophysicists claim to take a more data-driven approach. They have yet to score a major breakthrough, but they say their contributions are gaining wider acceptance. Still, econophysics does not impress some economists.

 

Counterterrorism's New Tool: ‘Metanetwork’ Analysis

 

John Bohannon

 

A decade ago, most research on social networks was abstract and academic. But in the wake of the 11 September 2001 attacks, interest in applying this research to warfare exploded. Many companies are now vying for a piece of the military funding. Academic network scientists are also diving in, competing for lucrative U.S. military contracts and grants. In spite of the boom, there is sharp disagreement about how effective social network analysis has been for counterterrorism. Some worry that in the rush to catch terrorists, the U.S. military has put too much faith in social network analysis. One former U.S. official even claims that applying these methods in war zones has led to unethical practices.

 

Investigating Networks: The Dark Side

 

John Bohannon

 

A few months ago, Lawrence Wilkerson, a former U.S. State Department official and Army colonel, painted a nightmare scenario of how social network science can be applied in a battle zone. Describing how U.S. forces gathered intelligence to identify networks of insurgents after the 2003 invasion of Iraq, Wilkerson outlined something he called "the mosaic philosophy." The strategy, he claims, was similar to sequencing a genome. But instead of assembling millions of strands of DNA, investigators worked with data from interrogations of thousands of civilian prisoners. The general strategy of casting a wide net for intelligence gathering was familiar to all network researchers contacted by Science (see main text), but many expressed disbelief that it was carried out on such a grand scale in Iraq and Afghanistan.

 

Perspectives

 

Scale-Free Networks: A Decade and Beyond

 

Albert-László Barabási

 

For decades, we tacitly assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm. The decade-old discovery of scale-free networks was one of those events that had helped catalyze the emergence of network science, a new research field with its distinct set of challenges and accomplishments.

 

Revisiting the Foundations of Network Analysis

 

Carter T. Butts

 

Network analysis has emerged as a powerful way of studying phenomena as diverse as interpersonal interaction, connections among neurons, and the structure of the Internet. Appropriate use of network analysis depends, however, on choosing the right network representation for the problem at hand.

 

Disentangling the Web of Life

 

Jordi Bascompte

 

Biodiversity research typically focuses on species richness and has often neglected interactions, either by assuming that such interactions are homogeneously distributed or by addressing only the interactions between a pair of species or a few species at a time. In contrast, a network approach provides a powerful representation of the ecological interactions among species and highlights their global interdependence. Understanding how the responses of pairwise interactions scale to entire assemblages remains one of the great challenges that must be met as society faces global ecosystem change.

 

A General Framework for Analyzing Sustainability of Social-Ecological Systems

 

Elinor Ostrom1,2,*

 

A major problem worldwide is the potential loss of fisheries, forests, and water resources. Understanding of the processes that lead to improvements in or deterioration of natural resources is limited, because scientific disciplines use different concepts and languages to describe and explain complex social-ecological systems (SESs). Without a common framework to organize findings, isolated knowledge does not cumulate. Until recently, accepted theory has assumed that resource users will never self-organize to maintain their resources and that governments must impose solutions. Research in multiple disciplines, however, has found that some government policies accelerate resource destruction, whereas some resource users have invested their time and energy to achieve sustainability. A general framework is used to identify 10 subsystem variables that affect the likelihood of self-organization in efforts to achieve a sustainable SES.

 

Economic Networks: The New Challenges

 

Frank Schweitzer,1,* Giorgio Fagiolo,2 Didier Sornette,1,3 Fernando Vega-Redondo,4,5 Alessandro Vespignani,6,7 Douglas R. White8

 

The current economic crisis illustrates a critical need for new and fundamental understanding of the structure and dynamics of economic networks. Economic systems are increasingly built on interdependencies, implemented through trans-national credit and investment networks, trade relations, or supply chains that have proven difficult to predict and control. We need, therefore, an approach that stresses the systemic complexity of economic networks and that can be used to revise and extend established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust.

 

Predicting the Behavior of Techno-Social Systems

 

Alessandro Vespignani

 

We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.

 

Review

 

Transcriptional Regulatory Circuits: Predicting Numbers from Alphabets

 

Transcriptional regulatory circuits govern how cis and trans factors transform signals into messenger RNA (mRNA) expression levels. With advances in quantitative and high-throughput technologies that allow measurement of gene expression state in different conditions, data that can be used to build and test models of transcriptional regulation is being generated at a rapid pace. Here, we review experimental and computational methods used to derive detailed quantitative circuit models on a small scale and cruder, genome-wide models on a large scale. We discuss the potential of combining small- and large-scale approaches to understand the working and wiring of transcriptional regulatory circuits.

 

以上资料摘自：http://www.sciencemag.org/content/vol325/issue5939/index.dtl