如何运用大量的数据去解决人类面临的挑战？

如何刻画共生演化的自适应网络上由个体策略自组织所产生的全种群范围的集体动力学？

什么是多层网络结构及其动力学而涌现出来的物理现象？

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从大数据到重要信息

（From Big Data To Important Information）

April 15, 10:34 PM

BY Yaneer Bar-Yam

(Translated by – 猪哥)

不断发展着的科学正在寻找新的契机去收集大量关于复杂系统的数据。人们虽然在系统的细节结构上取得了重要进展，但还不清楚如何运用这些数据去解决人类所面临的挑战。我们希望解决的问题通常需要确定人为干预如何对系统产生影响，而这些影响在现有的详细数据中并不明显。在这里，我们对一些重要概念进行回顾，并试图建立一个一般的框架来构建复杂系统的大型视图，并刻画出信息在物理、生物和社会系统中的重要性。我们提供了将其应用于与生态、生物多样性、流行病学以及人类寿命相关的进化生物学之中；以及与种族暴力、全球粮食价格和股市恐慌相关的社会系统之中的案例。通过将科学探究转化为一种确定什么重要什么不重要的问题，我们对于许多实际问题如经济发展或疾病治疗会有更深的理解。

不断发展着的科学正在寻找新的契机去收集大量关于复杂系统的数据。人们虽然在系统的详细结构上取得了重要进展，但还不清楚如何运用这些数据去解决人类所面临的挑战。我们希望解决的问题需要确定人为干预对系统的影响，而这些影响在现有的详细数据中并不明显。在这里，我们审视重要概念，促使建立一个一般的框架来构建复杂系统的大型视图，并表征物理、生物和社会系统中信息的重要性。我们提供了其应用于与生态学、生物多样性、流行病和人类寿命相关的进化生物学，以及与种族暴力、全球粮食价格和股市恐慌相关的社会系统背景下的例子。努力让科学探究成为确定信息重要还是不重要的依据，有利于促进我们对许多实际关心的问题的理解和解决，比如经济的发展或疾病的治疗。

Advances in science are being sought in newly available opportunities to collect massive quantities of data about complex systems. While key advances are being made in detailed mapping of systems, how to relate this data to solving many of the challenges facing humanity is unclear. The questions we often wish to address require identifying the impact of interventions on the system and that impact is not apparent in the detailed data that is available. Here we review key concepts and motivate a general framework for building larger scale views of complex systems and for characterizing the importance of information in physical, biological and social systems. We provide examples of its application to evolutionary biology with relevance to ecology, biodiversity, pandemics, and human lifespan, and in the context of social systems with relevance to ethnic violence, global food prices, and stock market panic. Framing scientific inquiry as an effort to determine what is important and unimportant is a means for advancing our understanding and addressing many practical concerns, such as economic development or treating disease.

原文链接：http://arxiv.org/abs/1604.00976

自适应社会网络中从个体到集体行为的连接

（Linking Individual and Collective Behavior in Adaptive Social Networksv）

Phys. Rev. Lett. 116, 128702 April 15, 11:28 PM

BY Flávio L. Pinheiro, Francisco C. Santos, and Jorge M. Pacheco

(Translated by -高德华)

人们已经知道，适应性社会结构能够促进合作的演化。然而直至目前，要对共生演化的自适应网络上由个体策略自组织所产生的全种群范围的集体动力学进行刻画仍然是比较困难的。在本文中，我们在个体（微观）行为和集体（宏观）行为之间建立起一种（可逆的）连接，并将其视为是一个共生演化的过程。我们的研究证明：自适应网络将个体在局部面临的双人社会困境转变成了类似于N人协调博弈的集体动力学，其表征敏感地依赖于所涉及的行为和网络演化的相对时间尺度。特别地，我们的研究表明：网络适应的相对速率越快，合作的扩散所需的合作者的临界比例就越小，由此我们在网络适应性和合作演化两者之间建立了一种直接的联系。我们所提出的这个框架具有一般性，可以较容易地应用于适应性网络上的其他动力学过程，如疾病的传播或者创新的扩散。

人们已经知道，适应性社会结构能够促进合作的演化。然而直至目前，要对共生演化的自适应网络上由个体策略自组织所产生的全种群范围的集体动力学进行刻画仍然是比较困难的。本文中，我们在个体（微观）行为和集体（宏观）行为之间建立起连接，并将其视为是一个共生演化的过程。我们的研究证明：自适应网络将个体在局部面临的双人社会困境转变成了类似于N人协调博弈相关联的集体动力学，其表征敏感依赖于所涉及为和网络演化之间的相对时间尺度。进而，我们的研究表明：网络适应的相对速率越快，合作所需合作者的临界比例就越小，由此在网络适应性和合作演化两者之间建立了直接的联系。我们所提出的这个框架具有一般性，可以较容易地应用于适应性网络上所发生的其他动力学过程，如传染性疾病的传播或者创新的扩散。

Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N-person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

原文链接： http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002433

多层网络上的物理

（The physics of multilayers networks）

April 16, 3:33 PM

BY Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas

(Translated by 蔡嘉文)

网络研究是调查无数学科中多种类的复杂系统的结构，动力学，功能的关键。尽管传统网络分析取得了成功，但是由于复杂系统中的组成结构和大量互相作用的子系统（多元复杂性 multiplexity），普通网络难以对其进行完善的表示。这种结构上的复杂性对动力学和功能都有着重要的作用。如果简单地丢掉或者聚合可用的结构信息可能产生错误的结果并对系统的内在机制的理解造成阻碍。最近的“多层网络”模型能够使我们能够清晰地建模出这种多元复杂性以及其他真实网络系统的特征。一方面，这种方法使得我们可以将不同的结构关系编码到一个易用的数学对象。另一方面，我们可以在这种相互连接的结构上尝试不同种类的动力学过程。这个框架是对复杂系统建立完善而准确的理解的关键。对多层网络的研究也揭示出了一些在传统网络结构的图表示下被隐藏的物理过程。在这里我们试图对多层网络上的动力学过程建立更深刻的理解，并且着重突出了一些随多层网络结构及其动力学而涌现出来的物理现象。

The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., “multiplexity”) among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent “multilayer’ approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.