Simulation of Cultural Identities for Prediction of Reactions-(SCIPR)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-06-C-6634
Agency Tracking Number: F051-069-1223
Amount: $1,890,000.00
Phase: Phase II
Program: SBIR
Awards Year: 2006
Solicitation Year: 2005
Solicitation Topic Code: AF05-069
Solicitation Number: 2005.1
Small Business Information
APTIMA, INC.
12 Gill Street, Suite 1400, Woburn, MA, 01801
DUNS: 967259946
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Justin Boesel
 Simulation Analyst
 (202) 842-1548
 jboesel@aptima.com
Business Contact
 Margaret Clancy
Title: Chief Financial Officer
Phone: (781) 496-2415
Email: clancy@aptima.com
Research Institution
N/A
Abstract
A central challenge for the U.S. armed services lies not in fighting nation-state armed forces, but in dealing with more irregular forces. The glue that holds many of these forces together and motivates them to fight is a common belief system, or shared cultural identity. Hence, it is essential to understand cultural identities and cultural issues. This is difficult because cultural identities are neither monolithic nor static. An individual person can have multiple cultural identities because he or she belongs to a number of different groups. Cultural identities are also dynamic. To address these difficulties, we propose to create the Simulation of Cultural Identities for Prediction of Reactions (SCIPR) system. SCIPR is a set of analysis methods and a simulation modeling tool that combines a method for determining relevant cultural identities with an agent-based simulation tool to predict responses to events, including U.S. and adversary courses of action (COAs), as well as non-military events, such as civil reconstruction and political events. SCIPR draws on cultural anthropology to understand how cultural identities are defined and how they may change over time in response to events and actions, and employs agent-based modeling to simulate these dynamics and predict responses to COAs.

* information listed above is at the time of submission.

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