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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.

* [3] 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 [4] 
Segismundo [3] http://dx.doi.org/10.1371/journal.pone.0015505


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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.

* [5] 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

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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 (...)

* [6] 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
[6] http://dx.doi.org/10.1371/journal.pcbi.1000968

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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.

* [7] 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.
[7] http://dx.doi.org/10.1371/journal.pcbi.1000984

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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.

* [8] 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
[8] http://dx.doi.org/10.1371/journal.pcbi.1001005

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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.

* [9] 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 [10] Segismundo [9] http://dx.doi.org/10.1098/rspb.2010.1107


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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.

* [18] Hierarchy and information in feedforward networks, Bernat 
Corominas-Murtra, Joaquín Goñi, Carlos Rodríguez-Caso, Ricard Solé, 
2010/11/19, arXiv:1011.4394 [18] http://arXiv.org/abs/1011.4394

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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.

* [20] 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 [21] Segismundo [20] 
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
  _______________________________________________________________________

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