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Dr. Samuelson, president and chief scientist

of InfoLogix, Inc., will speak at the Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences, Friday, February 22. Dr. Samuelson’s talk entitled “Garbage Cans, Lymph Nodes and Cybersecurity: Modeling Organizational Effectiveness” (abstract below) will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

This session will be live-streamed on the YouTube channel: https://www.youtube.com/channel/UC7YCR-pBTZ_9865orDNVHNA

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group: https://www.facebook.com/groups/257383120973297/

For a list of upcoming and previous seminars, please visit: https://cos.gmu.edu/cds/calendar/

Abstract: We re-examine and extend the well-known “Garbage Can Model” of Cohen, March and Olsen (1972). They postulated that organizational choice can be well represented by a garbage can into which problems and solutions are thrown randomly. When, by random mixing, a solution meets a problem, the problem is solved and removed from the venue. In 2006, Folcik and Orosz presented an agent-based model of a lymph node, into which blood cells bring foreign substances and objects that are then neutralized by specialized immune system cells. This model led several social scientists, notably Troitzsch (2008), to point out a strong resemblance to the garbage can model, but now adding the recognition that problems require skill sets which some, but not all, solvers possess. Matching skill sets is critical to effective performance, and providing the right mix of solver skill sets enables the organization to perform effectively and economically. We suggest ways to apply this approach to integrated man-machine systems intended to enhance information systems security. One implication is that some approaches currently popular with policy-makers are highly unlikely to work.