Abstract
Covert networks are often difficult to reason about, manage and destabilize. In part, this is because they are a complex adaptive system. In addition, this is due to the nature of the data available on these systems. Making these covert networks less adaptive, more predictable, more consistent will make it easier to contain or constrain their activity. But, how can we inhibit adaptation? Herein, covert networks are characterized as dynamic multi-mode multi-plex networks. Dynamic network analysis tools are used to assess their structure and identify effective destabilization strategies that inhibit the adaptivity of these groups.
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This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037. Additional support was provided by the NSF IGERT for research and training in CASOS and by the center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon University (http://www.casos.cs.cmu.edu). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation or the U.S. government.
Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area.
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Carley, K.M. Destabilization of covert networks. Comput Math Organiz Theor 12, 51–66 (2006). https://doi.org/10.1007/s10588-006-7083-y
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DOI: https://doi.org/10.1007/s10588-006-7083-y