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Complex networks: Don't call in sick

Intuition informs a widespread policy of epidemic response, replacing infected workers in classrooms or hospitals with healthy substitutes. But modelling now suggests that this mechanism may be a key factor in the accelerated spread of an epidemic.

 

Complex networks: Don't call in sick

Thilo Gross
Nature Physics 12, 995–996 (2016) doi:10.1038/nphys3939

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The Strange Inevitability of Evolution

The Strange Inevitability of Evolution | Papers | Scoop.it
Ah, but isn’t all this wonder simply the product of the blind fumbling of Darwinian evolution, that mindless machine which takes random variation and sieves it by natural selection? Well, not quite. You don’t have to be a benighted creationist, nor even a believer in divine providence, to argue that Darwin’s astonishing theory doesn’t fully explain why nature is so marvelously, endlessly inventive. “Darwin’s theory surely is the most important intellectual achievement of his time, perhaps of all time,” says evolutionary biologist Andreas Wagner of the University of Zurich. “But the biggest mystery about evolution eluded his theory. And he couldn’t even get close to solving it.”
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Arjen ten Have's curator insight, October 31, 2016 2:51 PM
Mustread for every biologist
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Social norms as solutions

Climate change, biodiversity loss, antibiotic resistance, and other global challenges pose major collective action problems: A group benefits from a certain action, but no individual has sufficient incentive to act alone. Formal institutions, e.g., laws and treaties, have helped address issues like ozone depletion, lead pollution, and acid rain. However, formal institutions are not always able to enforce collectively desirable outcomes. In such cases, informal institutions, such as social norms, can be important. If conditions are right, policy can support social norm changes, helping address even global problems. To judge when this is realistic, and what role policy can play, we discuss three crucial questions: Is a tipping point likely to exist, such that vicious cycles of socially damaging behavior can potentially be turned into virtuous ones? Can policy create tipping points where none exist? Can policy push the system past the tipping point?

 

Social norms as solutions
Karine Nyborg, John M. Anderies, Astrid Dannenberg, Therese Lindahl, Caroline Schill, Maja Schlüter, W. Neil Adger, Kenneth J. Arrow, Scott Barrett, Stephen Carpenter, F. Stuart Chapin III, Anne-Sophie Crépin, Gretchen Daily, Paul Ehrlich, Carl Folke, Wander Jager, Nils Kautsky, Simon A. Levin, Ole Jacob Madsen, Stephen Polasky, Marten Scheffer, Brian Walker, Elke U. Weber, James Wilen, Anastasios Xepapadeas, Aart de Zeeuw

Science  07 Oct 2016:
Vol. 354, Issue 6308, pp. 42-43
DOI: 10.1126/science.aaf8317

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How complexity originates: Examples from history reveal additional roots to complexity

Most scientists will characterize complexity as the result of one or more factors out of three: (i) high dimensionality, (ii) interaction networks, and (iii) nonlinearity. High dimensionality alone need not give rise to complexity. The best known cases come from linear algebra: To determine the eigenvalues and eigenvectors of a large quadratic matrix, for example, is complicated but not complex. Every mathematician, physicist or economist, and most scholars from other disciplines can write down an algorithm that would work provided infinite resources in computer time and storage space are given. (...) 

 

How complexity originates: Examples from history reveal additional roots to complexity
Peter Schuster
Complexity
DOI: 10.1002/cplx.21841

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Marcelo Errera's curator insight, October 28, 2016 2:23 PM
It's reasonable to assume there is an underlying physics principle that drives systems to complexity.  Once the principle is identified, one will be able to discover when complexity emerges or not.
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Why I know but don't believe

Despite extensive efforts at public science education, polling over the past 30 years has consistently shown that about 40 to 45% of Americans believe that humans were supernaturally created in the past 10,000 years (1). A natural interpretation of this finding is that U.S. science education is failing to reach nearly half of the population, and that widespread belief in recent human origins reflects basic scientific illiteracy. However, the reality is more complex (2): Many of those who reject evolutionary theory are aware of the scientific consensus on the subject, and such rejection is not always associated with low scientific literacy. Similar results have been found for beliefs regarding anthropogenic climate change (3). On page 321 of this issue, Friedkin et al. (4) provide a key step toward understanding this phenomenon by introducing a simple family of models for social influence among individuals with multiple, interdependent beliefs.

 

Why I know but don't believe
Carter T. Butts

Science  21 Oct 2016:
Vol. 354, Issue 6310, pp. 286-287
DOI: 10.1126/science.aaj1817

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Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics

• A multimodel agent-based simulation environment (PULSE) is presented.
• Model integration techniques suggested: common space and commonly controlled agents.
• Crowd pressure metrics for simulating crushing and asphyxia in crowds are proposed.
• Simulations of evacuation from cinema building to the city streets are carried out.

 

Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics
Vladislav Karbovskii, Daniil Voloshin, Andrey Karsakov, Alexey Bezgodov, Carlos Gershenson

Future Generation Computer Systems

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The phylogenetic roots of human lethal violence

The phylogenetic roots of human lethal violence | Papers | Scoop.it

The percentage of human deaths caused by interpersonal violence reflects our membership of a particularly violent clade of mammals, although changes in socio-political organization have led to marked variations in this proportion.

 

The phylogenetic roots of human lethal violence
José María Gómez, Miguel Verdú, Adela González-Megías & Marcos Méndez
Nature 538, 233–237 (13 October 2016) doi:10.1038/nature19758

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Compression and the origins of Zipf's law for word frequencies

Here we sketch a new derivation of Zipf's law for word frequencies based on optimal coding. The structure of the derivation is reminiscent of Mandelbrot's random typing model but it has multiple advantages over random typing: (1) it starts from realistic cognitive pressures, (2) it does not require fine tuning of parameters, and (3) it sheds light on the origins of other statistical laws of language and thus can lead to a compact theory of linguistic laws. Our findings suggest that the recurrence of Zipf's law in human languages could originate from pressure for easy and fast communication.

 

Compression and the origins of Zipf's law for word frequencies
Ramon Ferrer-i-Cancho

Complexity

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Introduction to focus issue: Patterns of network synchronization

The study of synchronization of coupled systems is currently undergoing a major surge fueled by recent discoveries of new forms of collective dynamics and the development of techniques to characterize a myriad of new patterns of network synchronization. This includes chimera states, phenomena determined by symmetry, remote synchronization, and asymmetry-induced synchronization. This Focus Issue presents a selection of contributions at the forefront of these developments, to which this introduction is intended to offer an up-to-date foundation.

 

Introduction to focus issue: Patterns of network synchronization Daniel M. Abrams, Louis M. Pecora and Adilson E. Motter

Chaos 26, 094601 (2016); http://dx.doi.org/10.1063/1.4962970

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A general framework for measuring system complexity

In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity—although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is “simple” and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems.
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The Self-Organizing Society: The Role of Institutions

Is it possible to constrain a human society in such a way that self-organization will thereafter tend to produce outcomes that advance the goals of the society? Such a society would be self-organizing in the sense that individuals who pursue only their own interests would none-the-less act in the interests of the society as a whole, irrespective of any intention to do so. I sketch an agent-based model that identifies the conditions that must be met if such a self-organizing society is to emerge. The model draws heavily on an understanding of how self-organizing societies have emerged repeatedly during the evolution of life on Earth (e.g. evolution has produced societies of molecular processes, of simple cells, of eukaryote cells and of multicellular organisms). The model demonstrates that the key enabling requirement for a self-organizing society is ‘consequence-capture’. Broadly this means that all agents in the society must capture sufficient of the benefits (and harms) that are produced by the impact of their actions on the goals of the society. If this condition is not met, agents that invest resources in actions that produce societal benefits will tend to be out-competed by those that do not. This ‘consequence-capture’ condition can be met where a society is managed by appropriate systems of evolvable constraints that suppress free riders and support pro-social actions. In human societies these constraints include institutions such as systems of governance and social norms. If a self-organizing society is to emerge, consequence-capture must occur for all agents in the society, including those involved in the establishment and adaptation of institutions. By implementing consequence-capture, appropriate institutions can produce a self-organizing society in which the interests of all agents (including individuals, associations, firms, multi-national corporations, political organizations, institutions and governments) are aligned with those of the society as a whole.

 

The Self-Organizing Society: The Role of Institutions
John E. Stewart

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Can we open the black box of AI?

Can we open the black box of AI? | Papers | Scoop.it
Artificial intelligence is everywhere. But before scientists trust it, they first need to understand how machines learn.
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Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee

Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee | Papers | Scoop.it

Science-based board games can help people grasp the ecological complexity of autonomous pest control (APC) in the shade-coffee agroecosystem. Azteca Chess is a board-game that captures in a stylized way the fascinating natural history and the dynamics of a complex network of direct, indirect and cascading trait-mediated interactions among five species of arthropods dwelling in shade coffee bushes (a coffee-scale, an ant, an adult and larval lady beetle, a parasitoid wasp and a parasitoid fly). In exchange for honey-dew, the Azteca ant protects scale-insects that help control the devastating coffee-rust disease. The ant repels the adult ladybeetle but inadvertently protects its larvae, which devour scales to local extinction. The head-hunting fly paralyzes Azteca and opens a window of opportunity for the adult beetle to oviposit under scales, but also for a parasitoid wasp to kill the beetle larvae. Interactions can cascade or not towards APC. Experimental test-driving shows Azteca Chess meets good modeling and game-design standards and is proved statistically to enhance understanding and application of relevant complex ecological processes.

 

Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee
Luis García-Barriosa, Ivette Perfecto, John Vandermeer

Agriculture, Ecosystems & Environment
Volume 232, 16 September 2016, Pages 190–198

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Self-organized UAV Traffic in Realistic Environment

We investigated different dense multirotor UAV traffic simulation scenarios in open 2D and 3D space, under realistic environments with the presence of sensor noise, communication delay, limited communication range, limited sensor update rate and finite inertia. We implemented two fundamental self-organized algorithms: one with constant direction and one with constant velocity preference to reach a desired target. We performed evolutionary optimization on both algorithms in five basic traffic scenarios and tested the optimized algorithms under different vehicle densities. We provide optimal algorithm and parameter selection criteria and compare the maximal flux and collision risk of each solution and situation. We found that i) different scenarios and densities require different algorithmic approaches, i.e., UAVs have to behave differently in sparse and dense environments or when they have common or different targets; ii) a slower-is-faster effect is implicitly present in our models, i.e., the maximal flux is achieved at densities where the average speed is far from maximal; iii) communication delay is the most severe destabilizing environmental condition that has a fundamental effect on performance and needs to be taken into account when designing algorithms to be used in real life.

 

Self-organized UAV Traffic in Realistic Environment

Csaba Virágh, Máté Nagy, Carlos Gershenson, Gábor Vásárhelyi

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Social and economic impacts of climate

For centuries, thinkers have considered whether and how climatic conditions—such as temperature, rainfall, and violent storms—influence the nature of societies and the performance of economies. A multidisciplinary renaissance of quantitative empirical research is illuminating important linkages in the coupled climate-human system. We highlight key methodological innovations and results describing effects of climate on health, economics, conflict, migration, and demographics. Because of persistent “adaptation gaps,” current climate conditions continue to play a substantial role in shaping modern society, and future climate changes will likely have additional impact. For example, we compute that temperature depresses current U.S. maize yields by ~48%, warming since 1980 elevated conflict risk in Africa by ~11%, and future warming may slow global economic growth rates by ~0.28 percentage points per year. In general, we estimate that the economic and social burden of current climates tends to be comparable in magnitude to the additional projected impact caused by future anthropogenic climate changes. Overall, findings from this literature point to climate as an important influence on the historical evolution of the global economy, they should inform how we respond to modern climatic conditions, and they can guide how we predict the consequences of future climate changes.

 

Social and economic impacts of climate
Tamma A. Carleton, Solomon M. Hsiang

Science  09 Sep 2016:
Vol. 353, Issue 6304,
DOI: 10.1126/science.aad9837

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Sensitivity of Complex Networks

The sensitivity (i.e. dynamic response) of complex networked systems has not been well understood, making difficult to predict whether new macroscopic dynamic behavior will emerge even if we know exactly how individual nodes behave and how they are coupled. Here we build a framework to quantify the sensitivity of complex networked system of coupled dynamic units. We characterize necessary and sufficient conditions for the emergence of new macroscopic dynamic behavior in the thermodynamic limit. We prove that these conditions are satisfied only for architectures with power-law degree distributions. Surprisingly, we find that highly connected nodes (i.e. hubs) only dominate the sensitivity of the network up to certain critical frequency.

 

Sensitivity of Complex Networks
Marco Tulio Angulo, Gabor Lippner, Yang-Yu Liu, Albert-László Barabási

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What Counts as Science?

What Counts as Science? | Papers | Scoop.it

The arXiv preprint service is trying to answer an age-old question. 

 

Before arXiv, preprint papers were available only within small scientific circles, distributed by hand and by mail, and the journals in which they were ultimately published months later (if they were published at all) were holed up in university libraries. But arXiv has democratized the playing field, giving scientists instant access to ideas from all kinds of colleagues, all over the world, from prestigious chairs at elite universities to post-docs drudging away at off-brand institutions and scientists in developing countries with meager research support.

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Evidence of Shared Aspects of Complexity Science and Quantum Phenomena

Complexity science concepts of emergence, self-organization, and feedback suggest that descriptions of systems and events are subjective, incomplete, and impermanent-similar to what we observe in quantum phenomena. Complexity science evinces an increasingly compelling alternative to reductionism for describing physical phenomena, now that shared aspects of complexity science and quantum phenomena are being scientifically substantiated. Establishment of a clear connection between chaotic complexity and quantum entanglement in small quantum systems indicates the presence of common processes involved in thermalization in large and small-scale systems. Recent findings in the fields of quantum physics, quantum biology, and quantum cognition demonstrate evidence of the complexity science characteristics of sensitivity to initial conditions and emergence of self-organizing systems. Efficiencies in quantum superposition suggest a new paradigm in which our very notion of complexity depends on which information theory we choose to employ.

 

Evidence of Shared Aspects of Complexity Science and Quantum Phenomena
Cynthia Larson

Cosmos and History: The Journal of Natural and Social Philosophy, Vol 12, No 2 (2016)

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A new urban agenda

A new urban agenda | Papers | Scoop.it
Ahead of the UN’s bi-decadal Habitat III meeting, a suite of pieces in Nature sets out priorities for urban policy and planning, from where new cities could be built most sustainably and which are the most vulnerable, to how they should join forces.
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Symmetric States Requiring System Asymmetry

Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the complete synchronization of the entire network is the state inheriting the system symmetry. As in other systems subject to symmetry breaking, such symmetric states are not always stable. Here we report on the discovery of the converse of symmetry breaking--the scenario in which complete synchronization is not stable for identically-coupled identical oscillators but becomes stable when, and only when, the oscillator parameters are judiciously tuned to nonidentical values, thereby breaking the system symmetry to preserve the state symmetry. Aside from demonstrating that diversity can facilitate and even be required for uniformity and consensus, this suggests a mechanism for convergent forms of pattern formation in which initially asymmetric patterns evolve into symmetric ones.

 

Symmetric States Requiring System Asymmetry
Takashi Nishikawa, Adilson E. Motter

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An efficient system to fund science: from proposal review to peer-to-peer distributions

This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications.

 

An efficient system to fund science: from proposal review to peer-to-peer distributions

Johan Bollen, David Crandall, Damion Junk, Ying Ding, Katy Börner

Scientometrics (2016). doi:10.1007/s11192-016-2110-3

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Statistical mechanics of ecological systems: Neutral theory and beyond

Statistical mechanics of ecological systems: Neutral theory and beyond | Papers | Scoop.it
It is of societal importance to advance the understanding of emerging patterns of biodiversity from biological and ecological systems. The neutral theory offers a statistical-mechanical framework that relates key biological properties at the individual scale with macroecological properties at the community scale. This article surveys the quantitative aspects of neutral theory and its extensions for physicists who are interested in what important problems remain unresolved for studying ecological systems.

Via Samir
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Defining emergence: Learning from flock behavior

The idea of emergence originates from the fact that global effects emerge from local interactions producing a collective coherent behavior. A particular instance of emergence is illustrated by a flocking model of interacting “boids” encompassing two antagonistic conducts—consensus and frustration—giving rise to highly complex, unpredictable, coherent behavior. The cohesive motion arising from consensus can be described in terms of three ordered dynamic phases. Once frustration is included in the model, local phases for specific groups of flockmates, and transitions among them, replace the global ordered phases. Following the evolution of boids in a single group, we discovered that the boids in this group will alternate among the three phases. When we compare two uncorrelated groups, the second group shows a similar behavior to the first one, but with a different sequence of phases. Besides the visual observation of our animations with marked boids, the result is evident plotting the local order parameters. Rather than adopting one of the consensus ordered phases, the flock motion resembles more an entangled dynamic sequence of phase transitions involving each group of flockmates.
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Control principles of complex systems

Control principles of complex systems | Papers | Scoop.it

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

 

Control principles of complex systems
Yang-Yu Liu and Albert-László Barabási
Rev. Mod. Phys. 88, 035006

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Stochastic dynamics and the predictability of big hits in online videos

The competition for the attention of users is a central element of the Internet. Crucial issues are the origin and predictability of big hits, the few items that capture a big portion of the total attention. We address these issues analyzing 10 million time series of videos' views from YouTube. We find that the average gain of views is linearly proportional to the number of views a video already has, in agreement with usual rich-get-richer mechanisms and Gibrat's law, but this fails to explain the prevalence of big hits. The reason is that the fluctuations around the average views are themselves heavy tailed. Based on these empirical observations, we propose a stochastic differential equation with Le\'evy noise as a model of the dynamics of videos. We show how this model is substantially better in estimating the probability of an ordinary item becoming a big hit, which is considerably underestimated in the traditional proportional-growth models.

 

Stochastic dynamics and the predictability of big hits in online videos
Jose M. Miotto, Hogler Kantz, Eduardo G. Altmann

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