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  1.  17
    What You Believe Travels Differently: Information and Infection Dynamics Across Sub-Networks.Patrick Grim, Christopher Reade, Daniel J. Singer, Stephen Fisher & Stephen Majewicz - 2010 - Connections 30:50-63.
    In order to understand the transmission of a disease across a population we will have to understand not only the dynamics of contact infection but the transfer of health-care beliefs and resulting health-care behaviors across that population. This paper is a first step in that direction, focusing on the contrasting role of linkage or isolation between sub-networks in (a) contact infection and (b) belief transfer. Using both analytical tools and agent-based simulations we show that it is the structure of a (...)
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  2.  25
    Germs, Genes, and Memes: Functional and Fitness Dynamics on Information Networks.Patrick Grim, Daniel J. Singer, Christopher Reade & Stephen Fisher - 2015 - Philosophy of Science 82:219-243.
    It is widely accepted that the way information transfers across networks depends importantly on the structure of the network. Here, we show that the mechanism of information transfer is crucial: in many respects the effect of the specific transfer mechanism swamps network effects. Results are demonstrated in terms of three different types of transfer mechanism: germs, genes, and memes. With an emphasis on the specific case of transfer between sub-networks, we explore both the dynamics of each of these across networks (...)
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  3.  22
    Information Dynamics Across Linked Sub-Networks: Germs, Genes, and Memes.Patrick Grim, Daniel J. Singer, Christopher Reade & Stephen Fisher - 2011 - In Proceedings, AAAI Fall Symposium on Complex Adaptive Systems: Energy, Information and Intelligence. AAAI Press.
    Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than degree of (...)
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  4.  61
    Polarization and Belief Dynamics in the Black and White Communities: An Agent-Based Network Model From the Data.Patrick Grim, Stephen B. Thomas, Stephen Fisher, Christopher Reade, Daniel J. Singer, Mary A. Garza, Craig S. Fryer & Jamie Chatman - 2012 - In Christoph Adami, David M. Bryson, Charles Offria & Robert T. Pennock (eds.), Artificial Life 13. MIT Press.
    Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with (...)
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  5.  30
    Philosophical Analysis in Modeling Polarization: Notes From a Work in Progress.Patrick Grim, Aaron Bramson, Daniel J. Singer, Stephen Fisher, Carissa Flocken & William Berger - 2013 - In Paul Youngman & Mirsad Hadzikadik (eds.), Complexity and the Human Experience: Modeling Complexity in the Humanities and Social Sciences. Pan Sanford.
    A first take, matured in later work, in modeling belief polarization.
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  6.  25
    Robustness Across the Structure of Sub-Networks: The Contrast Between Infection and Information Dynamics.Patrick Grim, Christopher Reade, Daniel J. Singer, Stephen Fisher & Stephen Majewicz - 2010 - In Proceedings, AAAI FAll Symposium on Complex Adaptive Systems: Resilience, Robustness, and Evolvability.
    In this paper we make a simple theoretical point using a practical issue as an example. The simple theoretical point is that robustness is not 'all or nothing': in asking whether a system is robust one has to ask 'robust with respect to what property?' and 'robust over what set of changes in the system?' The practical issue used to illustrate the point is an examination of degrees of linkage between sub-networks and a pointed contrast in robustness and fragility between (...)
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