Complexity at large

Abstract

The following news item is taken in part from the June, 2010 issue of HFSP J titled “Robustness versus evolvability: A paradigm revisited,” by Erich Bornberg-Bauer and Linus Kramer. Evolvability is the property of a biological system to quickly adapt to new requirements. Robustness seems to be the opposite. Nonetheless many biological systems display both properties–a puzzling observation, which has caused many debates over the last decades. A recently published model by Draghi et al. [Nature 463, 353–355 (2010)] elegantly circumvents complications of earlier in silico studies of molecular systems and provides an analytical solution, which is surprisingly independent from parameter choice. Depending on the mutation rate and the number of accessible phenotypes at any given genotype, evolvability and robustness can be reconciled. A link to this article can be found at http://dx.doi.org/10.2976/1.3404403. The following news item is taken in part from the June 10, 2010 issue of PLoS Comput Biol titled “Rapid transition towards the division of labor via evolution of developmental plasticity,” by Sergey Gavrilets. Biological organisms are highly complex and are comprised of many different parts that function to ensure the survival and reproduction of the whole. How and why the complexity has increased in the course of evolution is a question of great scientific and philosophical significance. Biologists have identified a number of major transitions in the evolution of complexity including the origin of chromosomes, eukaryotes, sex, multicellular organisms, and social groups in insects. A crucial step in many of these transitions is the division of labor between components of the emerging higher level evolutionary unit. How the division of labor was achieved in the face of selfishness of lower level units is controversial. A link to this article can be found at http://dx.doi.org/10.1371/journal.pcbi.1000805. The following news item is taken in part from the July 2, 2010 issue of Science titled “The seductive allure of behavioral epigenetics,” by Greg Miller. Some researchers speculate that if these rodent findings extend to humans, epigenetics could turn out to be at the heart of some of the most vexing problems in society. These ills include the long-term health problems of people raised in lower socioeconomic environments, the vicious cycle in which abused children grow up to be abusive parents, and the struggles of drug addicts trying to kick the habit. Tempting as such speculation may be, others worry that the young but fast-growing field of behavioral epigenetics is getting ahead of itself. They point out that so far there's very little evidence in humans that epigenetics connects early life experience to behavioral or health problems later in life. A link to this article can be found at http://dx.doi.org/10.1126/science.329.5987.24. The following news item is taken in part from the June 30, 2010 issue of JASSS titled “Co-operative punishment cements social cohesion,” by Klaus Jaffe and Luis Zaballa. Most current attempts to explain the evolution-through individual selection-of pro-social behavior (i.e., behavior that favors the group) that allows for cohesive societies among non related individuals, focus on altruistic punishment as its evolutionary driving force. The main theoretical problem facing this line of research is that in the exercise of altruistic punishment, the benefits of punishment are enjoyed collectively while its costs are borne individually. We propose that social cohesion might be achieved by a form of punishment, widely practiced among humans and animals forming bands and engaging in mob beatings, which we call co-operative punishment. A link to this article can be found at http://jasss.soc.surrey.ac.uk/13/3/4.html. The following news item is taken in part from the July 13, 2010 issue of PNAS titled “Inferring individual rules from collective behavior,” by Ryan Lukeman, Yue-Xian Li, and Leah Edelstein-Keshet. Social organisms form striking aggregation patterns, displaying cohesion, polarization, and collective intelligence. Results point to strong short-range repulsion, intermediate-range alignment, and longer-range attraction (with circular zones), and a weak but significant frontal-sector interaction with one neighbor. A best-fit model with such interactions accounts well for observed group structure, whereas absence or alteration in any one of these rules fails to do so. We find that important features of observed flocking surf scoters can be accounted for by zonal models with specific, well-defined rules of interaction. A link to this article can be found at http://dx.doi.org/10.1073/pnas.1001763107. The following news item is taken in part from the July 14, 2010 issue of Nature titled “Social learning promotes institutions for governing the commons,” by Karl Sigmund, Hannelore De Silva, Arne Traulsen, and Christoph Hauert. Theoretical and empirical research highlights the role of punishment in promoting collaborative efforts. However, both the emergence and the stability of costly punishment are problematic issues. It is not clear how punishers can invade a society of defectors by social learning or natural selection, or how second-order free-riders (who contribute to the joint effort but not to the sanctions) can be prevented from drifting into a coercion-based regime and subverting cooperation. Here, we compare the prevailing model of peer-punishment with pool-punishment. A link to this article can be found at http://dx.doi.org/10.1038/nature09203. The following news item is taken in part from the July 3, 2010 issue of arXiv titled “Punish, but not too hard: How costly punishment spreads in the spatial public goods game,” by Dirk Helbing, Attila Szolnoki, Matjaz Perc, and Gyorgy Szabo. We study the evolution of cooperation in spatial public goods games where, besides the classical strategies of cooperation (C) and defection (D), we consider punishing cooperators or punishing defectors as an additional strategy. Using a minimalist modeling approach, our goal is to separately clarify and identify the consequences of the two punishing strategies. Since punishment is costly, punishing strategies loose the evolutionary competition in case of well-mixed interactions. When spatial interactions are taken into account, however, the outcome can be strikingly different, and cooperation may spread. A link to this article can be found at http://dx.doi.org/10.1038/nature09203. The following news item is taken in part from the July 16, 2010 issue of Science titled “Probing culture's secrets, from capuchins to children,” by Michael Balter. Scientists once designated culture as the exclusive province of humans. But that elitist attitude is long gone, as evidenced by a recent meeting on how culture, usually defined as the passing on of traditions by learning from others, arises and changes. The 700 attendees, a mixture of researchers and members of the public, heard talks on cultural transmission in fish, meerkats, birds, and monkeys, and in extinct and living humans. Researchers probed questions such as what sparks cultural trends and how complex traditions are transmitted, and most agreed that studies of both animals and children will provide important clues. A link to this article can be found at http://dx.doi.org/10.1126/science.329.5989.266. The following news item is taken in part from the December, 2009 issue of Swarm Intelligence titled “Editorial for the special issue on particle swarm optimization,” by Riccardo Poli, Andries Engelbrecht, and Jim Kennedy. The Particle Swarm Optimizer (PSO), one of the pillars of Swarm Intelligence, is a remarkable algorithm for at least two reasons: (a) it has a very simple formulation which makes it easy to implement, apply, extend, and hybridize, and (b) it is a constant source of complex and emergent phenomena, which are at the essence of swarm intelligence. Many people around the world are exploring PSOs and their applications. A link to this article can be found at http://dx.doi.org/10.1007/s11721-009-0033-9. The following news item is taken in part from the July 13, 2010 issue of PLoS Biol titled “Limbs made to measure,” by Anna Kicheva and James Briscoe. This year marks the 150th anniversary of the birth of D'Arcy Thompson, the British biologist, classicist, and all round polymath (For more information on D'Arcy Thompson see www.darcythompson.org). Like many, he was fascinated by the appearance and structure of living matter, and in his influential book, On Growth and Form, he set out to describe and explain the principles of morphogenesis–the way living things grow and acquire their forms. Using a vast range of examples, from the honeycomb in beehives to the spirals in a snail's shell, he emphasized that form should be studied in the context of growth and that to explain shape it was essential to understand the underlying mechanisms. This led to the central thesis of the book: biological forms are the result of mechanical and physical processes that should be described with mathematical precision. A link to this article can be found at http://dx.doi.org/10.1371/journal.pbio.1000421. The following news item is taken in part from the July 27, 2010 issue of arXiv titled “The triple helix perspective of innovation systems,” by Loet Leydesdorff and Girma Zawdie. Alongside the neo-institutional model of networked relations among universities, industries, and governments, the Triple Helix can be provided with a neo-evolutionary interpretation as three selection environments operating upon one another: markets, organizations, and technological opportunities. How are technological innovation systems different from national ones? The three selection environments fulfill social functions: wealth creation, organization control, and organized knowledge production. The main carriers of this system-industry, government, and academia-provide the variation both recursively and by interacting among them under the pressure of competition. Empirical case studies enable us to understand how these evolutionary mechanisms can be expected to operate in historical instance. The model is needed for distinguishing, for example, between trajectories and regimes. A link to this article can be found at http://arXiv.org/abs/1007.4756. The following news item is taken in part from the July 15, 2010 issue of PLoS ONE titled “Dynamics of person-to-person interactions from distributed RFID sensor networks,” by Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza, Jean-François Pinton, and Alessandro Vespignani. Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here, we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. A link to this article can be found at http://dx.doi.org/10.1371/journal.pone.0011596. The following news item is taken in part from the July 30, 2010 issue of Science titled “Making smarter, savvier robots,” by Sam Kean. What machines of the future really need to learn, say experts who plan to have them explore the far reaches of the solar system, is more independent behavior. A link to this article can be found at http://dx.doi.org/10.1126/science.329.5991.508. The following news item is taken in part from the June 4, 2010 issue of Physics of Life Reviews titled “A colorful origin for the genetic code: Information theory, statistical mechanics and the emergence of molecular codes,” by Tsvi Tlusty. The genetic code maps the sixty-four nucleotide triplets (codons) to twenty amino-acids. While the biochemical details of this code were unraveled long ago, its origin is still obscure. We review information-theoretic approaches to the problem of the code's origin and discuss the results of a recent work that treats the code in terms of an evolving, error-prone information channel. Our model “ which utilizes the rate-distortion theory of noisy communication channels ” suggests that the genetic code originated as a result of the interplay of the three conflicting evolutionary forces: the needs for diverse amino-acids, for error-tolerance and for minimal cost of resources. The description of the code as an information channel allows us to mathematically identify the fitness of the code and locate its emergence at a second-order phase transition when the mapping of codons to amino-acids becomes nonrandom. The noise in the channel brings about an error-graph, in which edges connect codons that are likely to be confused. The emergence of the code is governed by the topology of the error-graph, which determines the lowest modes of the graph-Laplacian and is related to the map coloring problem. A link to this article can be found at http://dx.doi.org/10.1016/j.plrev.2010.06.002. The 5th Int'l Conference on Bio-Inspired Models of Network, Information and Computing Systems, Boston, MA, USA, 2010/12/1-3 http://www.bionetics.org/ IWSOS 2011, Fifth International Workshop on Self-Organizing Systems, Karlsruhe, Germany, 2011/02/23-25 http://iwsos2011.tm.kit.edu/ GECCO 2011, Genetic and Evolutionary Computation Conference, Dublin, Ireland, 2011/07/12-16 http://www.sigevo.org/gecco-2011/ IJCAI 2011, the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011/07/19-22 http://ijcai-11.iiia.csic.es/