|
***** 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.
* [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
_________________________________________________________________
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
_________________________________________________________________
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
_________________________________________________________________
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
_________________________________________________________________
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
_________________________________________________________________
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
_________________________________________________________________
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
_________________________________________________________________
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
_______________________________________________________________________
_____________________________________________________________________
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.
|
