lunes, 6 de mayo de 2013



[comdig] Latest Complexity Digest Posts




Globally networked risks and how to respond

Today’s strongly connected, global networks have produced highly interdependent systems that we do not understand and cannot control well. These systems are vulnerable to failure at all scales, posing serious threats to society, even when external shocks are absent. As the complexity and interaction strengths in our networked world increase, man-made systems can become unstable, creating uncontrollable situations even when decision-makers are well-skilled, have all data and technology at their disposal, and do their best. To make these systems manageable, a fundamental redesign is needed. A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking.
 
Globally networked risks and how to respond
Dirk Helbing
Nature 497, 51–59 (02 May 2013) http://dx.doi.org/10.1038/nature12047


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Networks in Cognitive Science

Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences.
 
Networks in Cognitive Science
Andrea Baronchelli, Ramon Ferrer-i-Cancho, Romualdo Pastor-Satorras, Nick Chater, Morten H. Christiansen
http://arxiv.org/abs/1304.6736


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Formal Model of Living Organisms

A modeling formalism is proposed for the description and study of living and life-like systems. It provides an abstract conceptual model framework for real life and evolution of biological organisms. It is proposed, that this model formalism provides a novel system view and immediately applicable conceptual tools for understanding real life and evolution of biological organisms. The modeling principle is very generic, suggesting that it can be directly applied also to the study of engineered and artificial systems.
 
Formal Model of Living Organisms
Margareta Segerståhl
http://arxiv.org/abs/1304.5090


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Network modularity promotes cooperation

Cooperation in animals and humans is widely observed even if evolutionary biology theories predict the evolution of selfish individuals. Previous game theory models have shown that cooperation can evolve when the game takes place in a structured population such as a social network because it limits interactions between individuals. Modularity, the natural division of a network into groups, is a key characteristic of all social networks but the influence of this crucial social feature on the evolution of cooperation has never been investigated. Here, we provide novel pieces of evidence that network modularity promotes the evolution of cooperation in 2-person prisoner's dilemma games. By simulating games on social networks of different structures, we show that modularity shapes interactions between individuals favouring the evolution of cooperation. Modularity provides a simple mechanism for the evolution of cooperation without having to invoke complicated mechanisms such as reputation or punishment, or requiring genetic similarity among individuals. Thus, cooperation can evolve over wider social contexts than previously reported.
 
Network modularity promotes cooperation
Marianne Marcoux, David Lusseau
Journal of Theoretical Biology
Volume 324, 7 May 2013, Pages 103–108
http://dx.doi.org/10.1016/j.jtbi.2012.12.012

Complexity Digest's insight:
Modularity is prevalent in natural and artificial systems. A modular structure reduces the probability of "damage" or "perturbations" to spread through a network. More at:
Modular Random Boolean NetworksRodrigo Poblanno-Balp and Carlos GershensonArtificial Life 2011 17:4, 331-351
http://dx.doi.org/10.1162/artl_a_00042

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Quantifying Collective Attention from Tweet Stream


We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms.(...)we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake.
 
 
Sasahara K, Hirata Y, Toyoda M, Kitsuregawa M, Aihara K (2013) Quantifying Collective Attention from Tweet Stream. PLoS ONE 8(4): e61823.http://dx.doi.org/10.1371/journal.pone.0061823


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Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In each scenario, individuals are faced with a forced "go" versus "no go" evacuation decision, based on information available on competing broadcast and peer-to-peer sources. In this controlled setting, all actions and observations are recorded prior to the decision, enabling development of a quantitative decision making model that accounts for the disaster likelihood, severity, and temporal urgency, as well as competition between networked individuals for limited emergency resources. Individual differences in behavior within this social setting are correlated with individual differences in inherent risk attitudes, as measured by standard psychological assessments. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios.
 
Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation
Jean M. Carlson, David L. Alderson, Sean P. Stromberg, Danielle S. Bassett, Emily M. Craparo, Francisco Gutierrez-Villarreal, Thomas Otani
http://arxiv.org/abs/1304.4704


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An Introduction to Social Media for Scientists

Online social media tools can be some of the most rewarding and informative resources for scientists—IF you know how to use them.
 
Bik HM, Goldstein MC (2013) An Introduction to Social Media for Scientists. PLoS Biol 11(4): e1001535. http://dx.doi.org/10.1371/journal.pbio.1001535


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Quantifying Trading Behavior in Financial Markets Using Google Trends


Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.
 
Quantifying Trading Behavior in Financial Markets Using Google Trends
Tobias Preis, Helen Susannah Moat & H. Eugene Stanley
Scientific Reports 3, Article number: 1684 http://dx.doi.org/10.1038/srep01684


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Large-scale global optimization through consensus of opinions over complex networks

Consensus in multi-agents systems can be efficiently used for large-scale optimization problems. Connectivity structure of the consensus network is effective in the convergence to the optimum solution where random structures show better performance as compared to heterogeneous networks.
 
Large-scale global optimization through consensus of opinions over complex networks
Omid Askari Sichani and Mahdi Jalili
Complex Adaptive Systems Modeling 2013, 1:11 http://dx.doi.org/10.1186/2194-3206-1-11


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Complex systems: counterintuitive behavior and disproportionate causal effects


Presentation on the counterintuitive behavior and disproportionate causal effects of complex system. Illustration
using the the example of restaurant dynamics determined by the quality. The simulation is applied to Discrete Duty and Analogue Action.


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African Bus Routes Redrawn Using Cell-Phone Data

Researchers at IBM, using movement data collected from millions of cell-phone users in Ivory Coast in West Africa, have developed a new model for optimizing an urban transportation system.
The IBM model prescribed changes in bus routes around the around Abidjan, the nation’s largest city. These changes—based on people’s movements as discerned from cell-phone records—could, in theory, slash travel times 10 percent.


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Asymptotic Behaviour and Ratios of Complexity in Cellular Automata

We study the asymptotic behaviour of symbolic computing systems, notably one-dimensional cellular automata (CA), in order to ascertain whether and at what rate the number of complex versus simple rules dominate the rule space for increasing neighbourhood range and number of symbols (or colours), and how different behaviour is distributed in the spaces of different cellular automata formalisms. Using two different measures, Shannon's block entropy and Kolmogorov complexity, the latter approximated by two different methods (lossless compressibility and block decomposition), we arrive at the same trend of larger complex behavioural fractions. We also advance a notion of asymptotic and limit behaviour for individual rules, both over initial conditions and runtimes, and we provide a formalisation of Wolfram's classification as a limit function in terms of Kolmogorov complexity.
 
Asymptotic Behaviour and Ratios of Complexity in Cellular Automata
Hector Zenil, Elena Villarreal-Zapata
http://arxiv.org/abs/1304.2816


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