Papers
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# Papers

Recent publications related to complex systems
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## Mysteries of Development

Development is, literally, the journey of a life time, and it is a trip still as mysterious as it is remarkable. Despite new methods to probe how an animal or plant forms from a single cell, biologists have much to learn about the unimaginably complex process. To identify some of the field's persistent riddles, Senior Editors Beverly Purnell and Stella Hurtley and the news staff of Science have consulted with developmental biologists on our Board of Reviewing Editors and elsewhere. The mysteries offered here are a humbling reminder that our knowledge of development remains to a great extent embryonic.

How Do Organs Know When They Have Reached the Right Size?
Why Do So Many Neurons Commit Suicide During Brain Development?
How Do Microbes Shape Animal Development?
How Does Fetal Environment Influence Later Health?

Mysteries of Development
John Travis

Science 7 June 2013:
Vol. 340 no. 6137 p. 1156
http://dx.doi.org/10.1126/science.340.6137.1156-a

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## Social Media and Information Overload: Survey Results

Social Media and Information Overload: Survey Results

Kalina Bontcheva, Genevieve Gorrell, Bridgette Wessels

http://arxiv.org/abs/1306.0813

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## Game of Life on Phyllosilicates: Gliders, Oscillators and Still Life

A phyllosilicate is a sheet of silicate tetrahedra bound by basal oxygens. A phyllosilicate automaton is a regular network of finite state machines --- silicon nodes and oxygen nodes --- which mimics structure of the phyllosilicate. A node takes states 0 and 1. Each node updates its state in discrete time depending on a sum of states of its three (silicon) or six (oxygen) neighbours. Phyllosilicate automata exhibit localizations attributed to Conway's Game of Life: gliders, oscillators, still lifes, and a glider gun. Configurations and behaviour of typical localizations, and interactions between the localizations are illustrated.

Game of Life on Phyllosilicates: Gliders, Oscillators and Still Life

http://arxiv.org/abs/1306.0253

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## Adaptive long-range migration promotes cooperation under tempting conditions

Migration is a fundamental trait in humans and animals. Recent studies investigated the effect of migration on the evolution of cooperation, showing that contingent migration favors cooperation in spatial structures. In those studies, only local migration to immediate neighbor sites was considered, while long-range migration has not been considered yet, partly because the long-range migration has been generally regarded as harmful for cooperation as it would bring the population to a well-mixed state that favors defection. In this paper, we studied the effects of adaptive long-range migration on the evolution of cooperation through agent-based simulations of a spatial Prisoner's Dilemma game in which individuals can jump to a farther site if they are surrounded by more defectors. Our results show that adaptive long-range migration strongly promotes cooperation, especially under conditions where the temptation to defect is considerably high. Moreover, we found that cooperation emerges and remains robustly through mutation and migration even from a condition in which only defectors exist. These findings demonstrate the significance of adaptive long-range migration, a naturally observed migration style in human and animal behaviors, for the evolution of cooperation.

Adaptive long-range migration promotes cooperation under tempting conditions

Genki Ichinose, Masaya Saito, Hiroki Sayama, David Sloan Wilson

http://arxiv.org/abs/1306.0072

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 Rescooped by Complexity Digest from Collective intelligence

## Theories of Learning

Ivon Prefontaine's curator insight,

We treat social constructivism as if it is new. Dewey and Montessori wrote about it over a century ago although they did not call it constructivism. The idea of using digital technologies and social media add a new twist to old ideas and it is important to inquire into what that means.

Helen Teague's curator insight,

nicely succinct infographic on learning theories

Tom Short's curator insight,

Nice overview of various learning theories; positioned against some new thinking about Networked learning theory.

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## Collaborations: The fourth age of research

Research has progressed through three ages: the individual, the institutional and the national. Nations competed to be at the cutting edge because this contributed to the wider economy through knowledge, new processes and products.

Today, we are entering a fourth age of research, driven by international collaborations between elite research groups. This will challenge the ability of nations to conserve their scientific wealth either as intellectual property or as research talent. Tensions are growing: between the knowledge a country needs to remain competitive and the assets it can exclusively secure, and between the collaborative and domestic parts of the research base. Institutions that do not form international collaborations risk progressive disenfranchisement, and countries that do not nurture their talent will lose out entirely.

Collaborations: The fourth age of research

Nature 497, 557–560 (30 May 2013) http://dx.doi.org/10.1038/497557a

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## Early warning signals: The charted and uncharted territories

The realization that complex systems such as ecological communities can collapse or shift regimes suddenly and without rapid external forcing poses a serious challenge to our understanding and management of the natural world. The potential to identify early warning signals that would allow researchers and managers to predict such events before they happen has therefore been an invaluable discovery that offers a way forward in spite of such seemingly unpredictable behavior. Research into early warning signals has demonstrated that it is possible to define and detect such early warning signals in advance of a transition in certain contexts. Here we describe the pattern emerging as research continues to explore just how far we can generalize these results. A core of examples emerges that shares three properties: the phenomenon of rapid regime shifts, a pattern of 'critical slowing down' that can be used to detect the approaching shift, and a mechanism of bifurcation driving the sudden change. As research has expanded beyond these core examples, it is becoming clear that not all systems that show regime shifts exhibit critical slowing down, or vice versa. Even when systems exhibit critical slowing down, statistical detection is a challenge. We review the literature that explores these edge cases and highlight the need for (a) new early warning behaviors that can be used in cases where rapid shifts do not exhibit critical slowing down, (b) the development of methods to identify which behavior might be an appropriate signal when encountering a novel system; bearing in mind that a positive indication for some systems is a negative indication in others, and (c) statistical methods that can distinguish between signatures of early warning behaviors and noise.

Early warning signals: The charted and uncharted territories

Carl Boettiger, Noam Ross, Alan Hastings

http://arxiv.org/abs/1305.6700

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 Rescooped by Complexity Digest from Global Brain

## Distinguishing Brain From Mind

In coming years, neuroscience will answer questions we don't even yet know to ask. Sometimes, though, focus on the brain is misleading.

Via Spaceweaver
luiy's curator insight,

Understanding the brain is of course essential to developing treatments for devastating illnesses like schizophrenia and Parkinson's. More abstract but no less compelling, the functioning of the brain is intimately tied to our sense of self, our identity, our memories and aspirations. But the excitement to explore the brain has spawned a new fixation that my colleague Scott Lilienfeld and I call neurocentrism -- the view that human behavior can be best explained by looking solely or primarily at the brain.

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## Big Data Needs a Big Theory to Go with It

As the world becomes increasingly complex and interconnected, some of our biggest challenges have begun to seem intractable. What should we do about uncertainty in the financial markets? How can we predict energy supply and demand? How will climate change play out? How do we cope with rapid urbanization? Our traditional approaches to these problems are often qualitative and disjointed and lead to unintended consequences. To bring scientific rigor to the challenges of our time, we need to develop a deeper understanding of complexity itself.

Luciano Lampi's curator insight,

A concise and objective tour over CAS!

Víctor Farré's curator insight,

Si integramos la complejdad en una nueva teoría más holística, paradojicamente llegamos a la conclusión de que se podfrán hacer algunas predicciones probabilisticas sobre algunos parámetros escogidos de sistemas complejos como el mercado financiero de base digital. En resumen se podrá establecer la probabilidad de un crash financiero en los próximos siete años. Big Deal!

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## How much does it help to know what she knows you know? An agent-based simulation study

In everyday life, people make use of theory of mind by explicitly attributing unobservable mental content such as beliefs, desires, and intentions to others. Humans are known to be able to use this ability recursively. That is, they engage in higher-order theory of mind, and consider what others believe about their own beliefs. In this paper, we use agent-based computational models to investigate the evolution of higher-order theory of mind. We consider higher-order theory of mind across four different competitive games, including repeated single-shot and repeated extensive form games, and determine the advantage of higher-order theory of mind agents over their lower-order theory of mind opponents. Across these four games, we find a common pattern in which first-order and second-order theory of mind agents clearly outperform opponents that are more limited in their ability to make use of theory of mind, while the advantage for deeper recursion to third-order theory of mind is limited in comparison.

How much does it help to know what she knows you know? An agent-based simulation study
Harmen de Weerd, Rineke Verbrugge, Bart Verheij

Artificial Intelligence
Available online 14 May 2013

In Press, Corrected Proof

http://dx.doi.org/10.1016/j.artint.2013.05.004

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 Suggested by Brian Josephson

## What If Everything Ran Like the Internet?

When the Internet was first starting to catch on in the 1980s, I was invited, as a representative of a large business consulting organization, to a day-long seminar explaining what this new phenomenon was and how businesses should be responding to it. It was led by a man who now makes millions as a social media guru (I won’t embarrass him by identifying him), but at the time he warned that the Internet had no future. The reason, he said, was that it was “anarchic” — there was no management, no control, no way of fixing things quickly if they got “out of hand”. The solution, he said, was for business and government leaders to get together and create an orderly alternative — “Internet 2″ he called it — that would replace the existing Internet when it inevitably imploded. Of course, he couldn’t have been more wrong.

Olivier Auber's comment, May 29, 2013 5:19 AM
In fact, the Internet as we know it, is also hierarchical, due to its silos and protocols.
luiy's curator insight,

Organization models --- > Internet --> “wirearchy” --> nature’s model of self-organizing, self-adapting, evolving complex systems

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## Culture, Genes, and the Human Revolution

State-of-the-art DNA sequencing is providing ever more detailed insights into the genomes of humans, extant apes, and even extinct hominins, offering unprecedented opportunities to uncover the molecular variants that make us human. A common assumption is that the emergence of behaviorally modern humans after 200,000 years ago required—and followed—a specific biological change triggered by one or more genetic mutations. For example, Klein has argued that the dawn of human culture stemmed from a single genetic change that “fostered the uniquely modern ability to adapt to a remarkable range of natural and social circumstance”. But are evolutionary changes in our genome a cause or a consequence of cultural innovation?

Culture, Genes, and the Human Revolution
Simon E. Fisher, Matt Ridley

Science 24 May 2013:
Vol. 340 no. 6135 pp. 929-930
http://dx.doi.org/10.1126/science.1236171

Arjen ten Have's curator insight,

The main question the authors hopefully address is whether evolutionary changes in our genome are cause or consequence of cultural innovation. Rather a philosophical question, most likely a question without an answer. Evolutionary biologists are indeed at a crosspoint, where they have to get philosophical and they are at the point where they discuss how plasticity is related to the adaptability. In the end it all comes down to the fact that evolution has no goal, is blind but it has to follow the challenges its environment puts and it has to do that with the mutations provided. WTF? Cultural innovation will obviously direct evolution and evolution will direct cultural innovation.

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## Advances in Neuroprosthetic Learning and Control

Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.

Carmena JM (2013) Advances in Neuroprosthetic Learning and Control. PLoS Biol 11(5): e1001561. http://dx.doi.org/10.1371/journal.pbio.1001561

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## Getting into Shape

As development progresses from a single fertilized egg to 2, 4, 6, 8, 16 cells, and so on, the early apparent homogeneity soon transitions to cells displaying varied sizes and shapes. Cell adhesion and cortical tension, with their associated forces, contribute to such changes. Crowded cells are pushed and pulled, but some make their own way via cell-autonomous migration or chemotaxis. These events proceed in an amazingly precise, choreographed manner, both temporally and spatially. Distinct germ layers and ultimately the stereotypic body form result, with amazing robustness. This special issue presents exciting advances in understanding morphogenesis, or the development of body shape.

Getting into Shape
Beverly A. Purnell

Science 7 June 2013:
Vol. 340 no. 6137 p. 1183
http://dx.doi.org/10.1126/science.340.6137.1183

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## Competition-induced criticality in a model of meme popularity

Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent $\alpha<2$, unlike preferential-attachment models), similar to those seen in empirical data.

Competition-induced criticality in a model of meme popularity

James P. Gleeson, Jonathan A. Ward, Kevin P. O'Sullivan, William T. Lee

http://arxiv.org/abs/1305.4328

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## Islands and the CounterIntuitive Effect They Have on Tsunamis

Computer simulations show that, far from protecting coastal communities, islands can dramatically amplify the damaging impact of tsunamis.
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## A Simple Generative Model of Collective Online Behaviour

Human activities---from voter mobilization to political protests---increasingly take place in online environments, providing novel opportunities for relating individual behaviours to population-level outcomes. The recent availability of data sets that capture the behaviour of individuals participating in online social systems has driven the emerging field of computational social science, as large-scale empirical data sets enable the development of detailed computational models of individual and collective behaviour. Given the inherent limitations of observational data, it is crucial to investigate the extent to which models of collective dynamics can distinguish between different individual-level mechanisms. Here we introduce a simple generative model for the collective behaviour of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct components: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behaviour that is consistent with data, only models that strongly emphasize recent popularity of applications over their cumulative popularity reproduce the observed temporal dynamics. Our approach demonstrates the value of even very simple generative models in understanding collective social behaviour, and it highlights the need to address temporal dynamics---not just long-time behaviour---when modelling complex social systems.

A Simple Generative Model of Collective Online Behaviour

James P. Gleeson, Davide Cellai, Jukka-Pekka Onnela, Mason A. Porter, Felix Reed-Tsochas

http://arxiv.org/abs/1305.7440

Spaceweaver's curator insight,

to investigate

 Suggested by Joseph Lizier

## Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series

In financial markets, participants locally optimize their profit which can result in a globally unstable state leading to a catastrophic change. The largest crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks due to high-risk trading in complex products. We introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information, and apply it to the time series of USD and EUR interest rate swaps (IRS). We find in both markets that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy, and decreases afterwards. Previously introduced indicators such as ‘critical slowing down’ do not provide a clear leading indicator. Our results suggest that the IDL may be used as an early-warning signal for critical transitions even in the absence of a predictive model.

Information dissipation as an early-warning signal for the Lehman Brothers collapse in financial time series
Rick Quax, Drona Kandhai & Peter M. A. Sloot
Scientific Reports 3, Article number: 1898
http://dx.doi.org/10.1038/srep01898

Michael Power's comment, June 7, 2013 3:45 PM
Investment bankers will build this into their models and it will become a late-warning sign.
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## Students as Collaborators in Systems Biology Research

In his 1854 speech at the University of Lille, Louis Pasteur stated "In the fields of observation, chance favors only the prepared mind." Preparation for scientific inquiry is critical for the future of research, yet opportunities are rare in the early stages of education. This discrepancy can be resolved by immersing students in genuine research activities as early as possible. We have created an experience for high school students to engage in and contribute to ongoing research. We prepare students to formulate and test hypotheses using computational tools and data collected in our laboratory, or available from public repositories.

Students as Collaborators in Systems Biology Research
Susan McClatchy, Deborah McGann, Robert Gotwals, Amanda Baskett, Gary Churchill

Science 31 May 2013:
Vol. 340 no. 6136 pp. 1061-1062
http://dx.doi.org/10.1126/science.1229906

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## From exaptation to radical niche construction in biological and technological complex systems

Biological adaptation assumes the evolution of structures toward better functions. Yet, the roots of adaptive trajectories usually entail subverted—perverted—structures, derived from a different function: what Gould and Vrba called “exaptation.” Generally, this derivation is regarded as contingent or serendipitous, but it also may have regularities, if not rules, in both biological evolution and technological innovation. On the basis of biological examples and examples from the history of technology, the authors demonstrate the centrality of exaptation for a modern understanding of niche, selection, and environment. In some cases, biological understanding illuminates technical exaptation. Thus, the driver of exaptation is not simply chance matching of function and form; it depends on particular, permissive contexts.

From exaptation to radical niche construction in biological and technological complex systems

Pierpaolo Andriani, Jack Cohen

http://dx.doi.org/10.1002/cplx.21450

Complexity
Volume 18, Issue 5, pages 7–14, May/June 2013

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 Rescooped by Complexity Digest from FuturICT Journal Publications

## Global Multi-Level Analysis of the ‘Scientific Food Web'

We introduce a network-based index analyzing excess scientific production and consumption to perform a comprehensive global analysis of scholarly knowledge production and diffusion on the level of continents, countries, and cities. Compared to measures of scientific production and consumption such as number of publications or citation rates, our network-based citation analysis offers a more differentiated picture of the ‘ecosystem of science’. Quantifying knowledge flows between 2000 and 2009, we identify global sources and sinks of knowledge production. Our knowledge flow index reveals, where ideas are born and consumed, thereby defining a global ‘scientific food web’. While Asia is quickly catching up in terms of publications and citation rates, we find that its dependence on knowledge consumption has further increased.

Global Multi-Level Analysis of the ‘Scientific Food Web'

Amin Mazloumian, Dirk Helbing, Sergi Lozano, Robert P. Light & Katy Börner

Scientific Reports 3, Article number: 1167 http://dx.doi.org/10.1038/srep01167

Via FuturICT
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## Statistics shows that economic prosperity needs both high scientific productivity and complex technological knowledge, but in different ways

Statistical analyses ... showed that ...scientific productivity correlates stronger with Gross National Income than technological sophistication; that science is important for economic growth among developed economies, whereas technical complexity is more important for the economic development of poorer countries; and that per capita scientific productivity seems to reach an upper limit in the most developed countries...

Statistics shows that economic prosperity needs both high scientific productivity and complex technological knowledge, but in different ways. Jaffe K., Rios, A. & Florez, A. Interciencia 38: 150-156, 2013.

http://atta.labb.usb.ve/Klaus/art194.pdf

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 Rescooped by Complexity Digest from Social Foraging

## Biomimicry: Ant movements inspire tunnel-digging robots

Biomimicry is a great tool to solve problems. Rather than reinvent the wheel, we can often look at the solutions that nature has come up with over millions of years of trials & errors. For example, the study of how ants can so quickly move underground and dig relatively stable tunnels in all kinds of soil can teach scientists and engineers a lot, some of which might be quite useful to make robots that could do search & rescue missions or explore hard to access corners of the Earth (equipped with the proper sensors, they could be used for all kinds of environmental monitoring jobs).

Via Ashish Umre
Andrew Glynn's curator insight,

More uses for insect inspired programming techniques.

 Rescooped by Complexity Digest from Dual impact of research; towards the impactelligent university

## Academic Impact and Wikipedia Ranking

ABSTRACT

In addition to its broad popularity Wikipedia is also widely used for scholarly purposes. Many Wikipedia pages pertain to academic papers, scholars and topics providing a rich ecology for scholarly uses. Although many recognize the scholarly potential of Wikipedia, as a crowdsourced encyclopedia its authority and quality is questioned due to the lack of rig-

orous peer-review and supervision. Scholarly references and mentions on Wikipedia may thus shape the\societal impact" of a certain scholarly communication item, but it is not clear whether they shape actual \academic impact". In this paper we compare the impact of papers, scholars, and topics according to two dierent measures, namely scholarly citations and Wikipedia mentions. Our results show that academic and Wikipedia impact are positively correlated. Papers, authors, and topics that are mentioned on Wikipedia have higher academic impact than those are not mentioned. Our ndings validate the hypothesis that Wikipedia can help assess the impact of scholarly publications and underpin relevance indicators for scholarly retrieval or recommendation systems.

The authors: "This implies that Wikipedia does serve as a collaborative social ltering system which is able to favor \classical" papers, authors, and topics, and recommend them to the general public."

Source:

A Comparative Study of Academic Impact and Wikipedia Ranking
Xin Shuai, Zhuoren Jiang, Xiaozhong Liu and Johan Bollen. JCDL 2013, Indianopolis, Indiana

Fulltext: http://www.cs.indiana.edu/~xshuai/papers/acm_wiki.pdf

Via wmijnhardt
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## Community detection and graph partitioning

Many methods have been proposed for community detection in networks. Some of the most promising are methods based on statistical inference, which rest on solid mathematical foundations and return excellent results in practice. In this paper we show that two of the most widely used inference methods can be mapped directly onto versions of the standard minimum-cut graph partitioning problem, which allows us to apply any of the many well-understood partitioning algorithms to the solution of community detection problems. We illustrate the approach by adapting the Laplacian spectral partitioning method to perform community inference, testing the resulting algorithm on a range of examples, including computer-generated and real-world networks. Both the quality of the results and the running time rival the best previous methods.

Community detection and graph partitioning

M. E. J. Newman

http://arxiv.org/abs/1305.4974

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