***** 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. *  Predicting Human Interactive Learning by Regret-Driven Neural Networks, Davide Marchiori , Massimo Warglien, 08/02/22, Science : 1111-1113.  http://www.sciencemag.org/cgi/content/full/319/5866/1111 ------------------------------- 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. *  SCImago Journal Rankings, SCImago Journal & Country Rank  http://news.biomedcentral.com/t/2020138/10128546/871228/0/?u=aHR0cDovL3d3dy5zY2ltYWd vanIuY29tLw%3d%3d&x=508f7a58 ---------------------------------------------- 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. *  The New Networking Nexus, Virginia Gewin, 08/02/21, DOI: 10.1038/nj7181-1024a, Nature 451  http://www.nature.com/naturejobs/2008/080221/full/nj7181-1024a.html -------------------------------------------------------- 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 (...). *  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  Carlos Gershenson  http://uk.arXiv.org/abs/0801.0249 -------------------------------------------- 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. *  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  Carlos Gershenson  http://uk.arXiv.org/abs/0801.4024 ----------------------------------------------- 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. *  STOP Terrorism Software: Technology For Analysis And Forecasting Of Terrorism, 08/02/27, ScienceDaily  http://www.sciencedaily.com/releases/2008/02/080226092812.htm _____________________________________________________________________ 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.