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*****  To join INSNA, visit http://www.insna.org  *****

 Barry Wellman
 _______________________________________________________________________

  S.D. Clark Professor of Sociology, FRSC              NetLab Director
  Centre for Urban & Community Studies           University of Toronto
  455 Spadina Avenue          Room 418          Toronto Canada M5S 2G8
  http://www.chass.utoronto.ca/~wellman            fax:+1-416-978-7162
  Updating history:     http://chass.utoronto.ca/oldnew/cybertimes.php
         Elvis wouldn't be singing "Return to Sender" these days
 _______________________________________________________________________

Predicting Human Interactive Learning by Regret-Driven Neural Networks ,
Science

Excerpts: An unexpectedly simple neural network model that includes
feedback driven by regret predicts human behavior in strategic games and
outperforms existing models of learning. (...) We found that even very
simple learning networks, driven by regret-based feedback, accurately
predict observed human behavior in different experiments on 21 games with
unique equilibria in mixed strategies. Introducing regret in the feedback
dramatically improved the performance of the neural network. We show that
regret-based models provide better predictions of learning than
established economic models.

* [6] Predicting Human Interactive Learning by Regret-Driven Neural
Networks,
Davide Marchiori ,  Massimo Warglien, 08/02/22, Science : 1111-1113.

[6] http://www.sciencemag.org/cgi/content/full/319/5866/1111

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SCImago Journal Rankings , SCImago Journal & Country Rank

Excerpts: The Science Citation Index and associated Journal Citation
Report (JCR) has long dominated the measurement of scholarly citation, but
SCImago Journal and Country Rank, is a new alternative database of journal
citation metrics developed by researchers in Spain. Unlike the JCR (which
is available to subscribers only), SCImago is freely available online, and
SCImago offers important improvements, compared to the JCR such as
weighting citations from journals according to how highly cited the
journal itself is. For further information please read the announcement on
the BioMed Central blog.

* [13] SCImago Journal Rankings, SCImago Journal & Country Rank

[13]
http://news.biomedcentral.com/t/2020138/10128546/871228/0/?u=aHR0cDovL3d3dy5zY2ltYWd
vanIuY29tLw%3d%3d&x=508f7a58

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Excerpts: A crop of websites is making networking among scientists easier
than ever. (...) The success of social-networking websites such as
MySpace, Facebook and LinkedIn shows the power of the Internet not only to
cultivate, but to capitalize on, friendships. Although online networks may
seem impersonal, they can do something for scientists that a handshake
cannot: highlight common research interests without leaving the comfort of
your desk. Say goodbye to name tags and awkward introductions - say hello
to profiles and blogs. In the search for jobs, mentors, collaborators or
data, these cyber-social mixers are revealing new ways to gain career
advice, create collaborations and share resources.

* [14] The New Networking Nexus, Virginia Gewin, 08/02/21, DOI:
10.1038/nj7181-1024a, Nature 451

[14] http://www.nature.com/naturejobs/2008/080221/full/nj7181-1024a.html



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 A Mathematical Formalism for Agent-based Modeling , arXiv

Excerpt: Many complex systems can be modeled as multiagent systems in
which the constituent entities (agents) interact with each other. The
global dynamics of such a system is determined by the nature of the local
interactions among the agents. Since it is difficult to formally analyze
complex multiagent systems, they are often studied through computer
simulations. While computer simulations can be very useful, results
obtained through simulations do not formally validate the observed
behavior. (...) The paper contains a sampling of the mathematical results
from the literature to show how finite dynamical systems can be used to
carry out a rigorous study of the properties of multiagent systems (...).

* [22] A Mathematical Formalism for Agent-based Modeling, Reinhard
Laubenbacher, Abdul S. Jarrah, Henning Mortveit, and S.S. Ravi,
2007/12/31,
DOI: 0801.0249, arXiv
* Contributed by [23] Carlos Gershenson

[22] http://uk.arXiv.org/abs/0801.0249

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 Set-based Complexity and Biological Information , arXiv

Abstract: It is not obvious what fraction of all the potential information
residing in the molecules and structures of living systems is significant
or meaningful to the system. Sets of random sequences or identically
repeated sequences, for example, would be expected to contribute little or
no useful information to a cell. This issue of quantitation of information
is important since the ebb and flow of biologically significant
information is essential to our quantitative understanding of biological
function and evolution.  Motivated specifically by these problems of
biological information, we propose here a class of measures to quantify
the contextual nature of the information in sets of objects, based on
Kolmogorov's intrinsic complexity. Such measures discount both random and
redundant information and are inherent in that they do not require a
defined state space to quantify the information. The maximization of this
new measure, which can be formulated in terms of the universal information
distance, appears to have several useful and interesting properties, some
of which we illustrate with examples.

* [24] Set-based Complexity and Biological Information, David J. Galas,
Matti
Nykter, Gregory W. Carter, Nathan D. Price, Ilya Shmulevich, 2008/01/25,
DOI:
0801.4024, arXiv
* Contributed by [25] Carlos Gershenson

[24] http://uk.arXiv.org/abs/0801.4024

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 STOP Terrorism Software: Technology For Analysis And Forecasting Of
Terrorism , Science Daily

Excerpts: Researchers at the University of Maryland's Institute for
Advanced Computer Studies (UMIACS) have developed the SOMA Terror
Organization Portal (STOP) allowing analysts to query automatically
learned rules on terrorist organization behavior, forecast potential
behavior based on these rules, and, most importantly, to network with
other analysts examining the same subjects. SOMA (Stochastic Opponent
Modeling Agents) is a formal, logical-statistical reasoning framework that
uses data about past behavior of terror groups in order to learn rules
about the probability of an organization, community, or person taking
certain actions in different situations.

* [54] STOP Terrorism Software: Technology For Analysis And Forecasting Of
Terrorism, 08/02/27, ScienceDaily

[54] http://www.sciencedaily.com/releases/2008/02/080226092812.htm

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