[comdig] Latest Complexity Digest Posts

Contents: Globally networked risks and how to respond Networks in Cognitive Science Formal Model of Living Organisms Network modularity promotes cooperation Quantifying Collective Attention from Tweet Stream Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation An Introduction to Social Media for Scientists Quantifying Trading Behavior in Financial Markets Using Google Trends Large-scale global optimization through consensus of opinions over complex networks Complex systems: counterintuitive behavior and disproportionate causal effects African Bus Routes Redrawn Using Cell-Phone Data Asymptotic Behaviour and Ratios of Complexity in Cellular Automata 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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    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. See it on Scoop.it, via Talks    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. See it on Scoop.it, via Papers    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 See it on Scoop.it, via Papers    More to read: Stephen Hawking’s advice for twenty-first century grads: Embrace complexity Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome Erik Brynjolfsson: The key to growth? Race with the machines Rethinking Economics Using Complexity Theory Between holism and reductionism: a philosophical primer on emergence Flow motifs reveal limitations of the static framework to represent human interactions Causal Entropic Forces Modeling Cultural Idea Systems: The Relationship between Theory Models and Data Models

follow on Twitter | like on Facebook | join on LinkedIn Founding Editor: Gottfried Mayer. Editor-in-Chief: Carlos Gershenson. To manage subscriptions, please go tohttp://comdig.unam.mx/subscriptions.php You can contribute to Complexity Digest selecting one of our topics and using the "Suggest" button. SOURCE:  Complexity Digest Administration

Kepler descubre los planetas más pequeños que hasta ahora se han encontrado dentro de la "zona habitable"

5 de mayo de 2013: 

La misión Kepler, de la NASA, ha descubierto dos nuevos sistemas planetarios que incluyen tres planetas de tamaño "super Tierra" dentro de la "zona habitable", el rango de distancia medido desde una estrella, y dentro del cual la temperatura de un planeta en órbita podría permitir la existencia de agua en estado líquido.

Los dos sistemas planetarios son Kepler-62 y Kepler-69. El sistema Kepler-62 posee cinco planetas; 62b, 62c, 62d, 62e y 62f. El sistema Kepler-69 posee dos planetas; 69b y 69c. Kepler-62e, 62f y 69c son las tres "super Tierras" que están ubicadas dentro de la "zona habitable".

"El descubrimiento de estos planetas rocosos dentro de la zona habitable representa un paso adelante en el propósito de encontrar un lugar como nuestro hogar", comenta John Grunsfeld, quien es el administrador asociado del Directorio de Misiones Científicas, en las oficinas centrales de la NASA. "Solo es cuestión de tiempo antes de que sepamos si nuestra galaxia es el hogar de una multitud de planetas como la Tierra, o si somos una rareza".

El diagrama compara los planetas internos de nuestro sistema solar con aquellos de Kepler-62, un sistema planetario de cinco planetas que se encuentra a una distancia de 1200 años luz de la Tierra.

Los planetas del sistema Kepler-62 orbitan alrededor de una estrella clasificada como enana tipo K2, con dimensiones de dos tercios el tamaño de nuestro Sol y tan solo un quinto de su brillo. Con una edad de siete mil millones de años, la estrella es algo más vieja que el Sol y se encuentra ubicada a una distancia aproximada de 1200 años luz de la Tierra, en la constelación de Lira (Lyra).

Por su parte, Kepler-62f solo es un 40 por ciento más grande que la Tierra, lo que lo convierte, respecto de sus dimensiones, en el exoplaneta más parecido a nuestro planeta que se ha encontrado (hasta el momento), dentro de la "zona habitable" de otra estrella. En cuanto a su composición, Kepler-62f muy probablemente es del tipo rocoso. Por otro lado, Kepler-62e orbita en el límite interno de la zona habitable y es aproximadamente un 60 por ciento más grande que la Tierra.

astro, con un 80 por ciento de su luminosidad, y se encuentra localizada a aproximadamente a 2700 años luz de la Tierra, en la constelación del Cisne (Cygnus).

Kepler-69c es un 70 por ciento más grande que la Tierra. Los astrónomos no están seguros de su composición, pero su órbita de 242 días alrededor de una estrella similar a la nuestra se parece a la de nuestro planeta vecino, Venus.

"Conocemos una sola estrella que posee un planeta con vida, el Sol. Hallar un planeta dentro de la zona habitable de una estrella similar a nuestro Sol representa un avance significativo para encontrar planetas verdaderamente similares a la Tierra", comenta Thomas Barclay, un científico de la misión Kepler, en el Instituto para Investigaciones Ambientales del Área de la Bahía, y autor principal del artículo sobre el descubrimiento del sistema Kepler-69, publicado en la revista de astrofísica denominada Astrophysical Journal, en idioma inglés.

El diagrama compara los planetas internos de nuestro sistema solar con aquellos de Kepler-69, un sistema planetario de dos planetas que se encuentra a una distancia aproximada de 2700 años luz de la Tierra.

"Kepler ha traído consigo un resurgimiento de los descubrimientos astronómicos, de modo que nos encontramos realizando avances importantes que llevarán a determinar si planetas como el nuestro representan una excepción a la regla", comenta William Borucki, quien es un investigador principal del área científica de la misión Kepler, en el Centro de Investigaciones Ames, de la NASA, y autor principal del artículo sobre el sistema Kepler-62, publicado en la revista Science.

Los científicos desconocen si puede existir vida en los recién descubiertos planetas; sin embargo, su descubrimiento indica que estamos un paso más cerca de encontrar un mundo similar a la Tierra, alrededor de una estrella como nuestro Sol.

Grunsfeld comenta: "La sonda Kepler ciertamente se ha convertido en una super estrella de la ciencia".

Para obtener más información acerca de la misión Kepler, así como imágenes digitales, visite:http://www.nasa.gov/kepler.

Créditos y Contactos
Funcionaria Responsable de NASA: Ruth Netting Editor de Producción: Dr. Tony Phillips	Traducción al Español: Rodrigo Gamboa Goñi Editora en Español: Angela Atadía de Borghetti Formato: Rodrigo Gamboa Goñi

Más información

El telescopio espacial Kepler, que continua y simultáneamente mide el brillo de más de 150.000 estrellas, es la primera misión de la NASA capaz de detectar planetas del tamaño de la Tierra que giran alrededor de otras estrellas como el Sol. Cuando un candidato a planeta transita, o pasa por delante de su estrella (desde la línea de visión de la sonda), bloquea un porcentaje de la luminosidad de la estrella. La disminución del brillo que se produce como resultado revela el tamaño del planeta en tránsito respecto de su estrella. Usando el método del tránsito, Kepler ha detectado 2740 candidatos. Por medio de varias técnicas de análisis, telescopios terrestres y otras consideraciones vinculadas con el espacio, se han confirmado 122 planetas.

En el comienzo de la misión, el telescopio Kepler encontró gigantes gaseosos en órbitas muy cercanas a sus estrellas. Se los conoce como “Júpiters calientes” y son los más sencillos de detectar, debido a sus tamaños y a sus muy cortos períodos orbitales. A la Tierra le tomaría tres años completar los tres tránsitos que se requieren para ser considerada como candidata a planeta. Es de esperar que, mientras Kepler continúe operando, señales de tránsito de planetas del tamaño de la Tierra, que orbiten dentro de la zona habitable de estrellas como el Sol, comiencen a emerger.

El Centro de Investigaciones Ames, de la NASA, está encargado del desarrollo de los equipos terrestres, así como de las operaciones de la misión y el análisis científico de los datos. El Laboratorio de Propulsión a Chorro (Jet Propulsion Laboratory o JPL, por su sigla en idioma inglés), de la NASA, ubicado en Pasadena, California, se encargó del desarrollo de la misión Kepler.

La firma Ball Aerospace and Technologies, en Boulder, Colorado, desarrolló los sistemas de vuelo de Kepler y brinda apoyo para las operaciones de la misión en conjunto con el Laboratorio de Física Espacial y Atmosférica de la Universidad de Colorado, en Boulder.

El Instituto de Ciencia del Telescopio Espacial, en Baltimore, archiva, aloja y distribuye los datos científicos proporcionados por Kepler. La misión Kepler es la décima de la NASA dentro del Programa Discovery (Descubrimiento, en idioma español), y es patrocinada por el Directorio de Misiones Científicas de la NASA, en la base de operaciones de la agencia.

Fuente: SCIENCE@NASA