***** To join INSNA, visit http://www.insna.org ***** Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert? , PLoS ONE Excerpt: Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions - where individuals are able to consider the success of previous decision outcomes - the collective's aggregated information is almost always superior. *  Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?, Katsikopoulos KV , King AJ, October 2010, DOI: 10.1371/journal.pone.0015505, PLoS ONE 5(11): e15505 * Contributed by  Segismundo  http://dx.doi.org/10.1371/journal.pone.0015505 _________________________________________________________________ 04. Networks and the Epidemiology of Infectious Disease , arXiv Excerpt: The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. *  Networks and the Epidemiology of Infectious Disease, Leon Danon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, Matthew C. Vernon, 2010/11/27, arXiv:1011.5950 _________________________________________________________________ 04.01. Infectious Disease Modeling of Social Contagion in Networks , PLoS Comput Biol Excerpt: Information, trends, behaviors and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this paper we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. (...) As an example, we study the spread of obesity (...) *  Infectious Disease Modeling of Social Contagion in Networks, Alison L. Hill, David G. Rand, Martin A. Nowak, Nicholas A. Christakis, 2010/11/04, DOI: 10.1371/journal.pcbi.1000968, PLoS Comput Biol 6(11): e1000968  http://dx.doi.org/10.1371/journal.pcbi.1000968 _________________________________________________________________ 04.02. Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics , PLoS Comput Biol Excerpt: The way potentially infectious contacts are made strongly influences how fast and how widely epidemics spread in their host population. Therefore, it is important to assess changes in contact behavior throughout an epidemic; these may occur due to external factors, such as demographic change, or as a side effect of the epidemic itself, leading to an accumulation of individuals with risky behavior in the infected population. We have developed a mathematical framework that allows for the study of the mutual interdependencies between epidemic spread and changes in contact behavior. The method is used to study HIV epidemics in model populations. *  Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics, Christel Kamp, 2010/11/18, DOI: 10.1371/journal.pcbi.1000984, PLoS Comput Biol 6(11): e1000984.  http://dx.doi.org/10.1371/journal.pcbi.1000984 _________________________________________________________________ 04.03. Network Analysis of Global Influenza Spread , PLoS Comput Biol Excerpt: As evidenced by several historic vaccine failures, the design and implementation of the influenza vaccine remains an imperfect science. (...) On a local scale, our technique can output the most likely origins of a virus circulating in a given location. On a global scale, we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation. We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses, including swine-origin H1N1, once more seasonal data is collected. *  Network Analysis of Global Influenza Spread, Joseph Chan, Antony Holmes, Raul Rabadan, 2010/11/18, DOI: 10.1371/journal.pcbi.1001005, PLoS Comput Biol 6(11): e1001005  http://dx.doi.org/10.1371/journal.pcbi.1001005 _________________________________________________________________ 04.04. Imitation dynamics of vaccination behaviour on social networks , Proc. R. Soc. B Excerpt: The problem of achieving widespread immunity to infectious diseases by voluntary vaccination is often presented as a public-goods dilemma, as an individual's vaccination contributes to herd immunity, protecting those who forgo vaccination. The temptation to free-ride brings the equilibrium vaccination level below the social optimum. Here, we present an evolutionary game-theoretic approach to this problem, exploring the roles of individual imitation behaviour and population structure in vaccination. *  Imitation dynamics of vaccination behaviour on social networks, Fu F , Rosenbloom DI , Wang L , Nowak MA, December 2010, DOI: 10.1098/rspb.2010.1107, Proc. R. Soc. B 7 vol. 278 no. 1702 * Contributed by  Segismundo  http://dx.doi.org/10.1098/rspb.2010.1107 _________________________________________________________________ 10. Hierarchy and information in feedforward networks , arXiv Abstract: In this paper we define a hierarchical index for feedforward structures taking, as the starting point, three fundamental concepts underlying hierarchy: order, predictability and pyramidal structure. Our definition applies to the so called causal graphs, i.e., connected, directed acyclic graphs in which the arrows depict a direct causal relation between two elements defining the nodes. The estimator of hierarchy is obtained by evaluating the complexity of causal paths against the uncertainty in recovering them from a given end point. This naturally leads us to a definition of mutual information which, properly normalized and weighted through the layered structure of the graph, results in suitable index of hierarchy with strong theoretical grounds. *  Hierarchy and information in feedforward networks, Bernat Corominas-Murtra, Joaquín Goñi, Carlos Rodríguez-Caso, Ricard Solé, 2010/11/19, arXiv:1011.4394  http://arXiv.org/abs/1011.4394 _________________________________________________________________ 12. Sixteen common misconceptions about the evolution of cooperation in humans , Evolution and Human Behavior Abstract: The occurrence of cooperation poses a problem for the biological and social sciences. However, many aspects of the biological and social science literatures on this subject have developed relatively independently, with a lack of interaction. This has led to a number of misunderstandings with regard to how natural selection operates and the conditions under which cooperation can be favoured. Our aim here is to provide an accessible overview of social evolution theory and the evolutionary work on cooperation, emphasising common misconceptions. *  Sixteen common misconceptions about the evolution of cooperation in humans, West SA , Mouden CE , Gardner A, November 2010, DOI: 10.1016/j.evolhumbehav.2010.08.001, Evolution and Human Behavior, in Press * Contributed by  Segismundo  http://dx.doi.org/10.1016/j.evolhumbehav.2010.08.001 Barry Wellman _______________________________________________________________________ S.D. Clark Professor of Sociology, FRSC NetLab Director Department of Sociology 725 Spadina Avenue, Room 388 University of Toronto Toronto Canada M5S 2J4 twitter:barrywellman http://www.chass.utoronto.ca/~wellman fax:+1-416-978-3963 Updating history: http://chass.utoronto.ca/oldnew/cybertimes.php _______________________________________________________________________ _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.