RAMPART

RAMPART is methodology, decision model, and cognitive support system to aid with effective allocation of anti-terrorism (AT) resources at Marine Corps installations. The work has so far been focused on the military domain, but the model and the software tools developed to implement it are generalizable to a range of commercial and public-sector settings including industrial parks, corporate campuses, and civic facilities. The approach suggests that anti-terrorism decision makers determine mitigation project allocations using measures of facility priority and mitigation project utility as inputs to the allocation algorithm. The three-part hybrid resource allocation model presented here uses multi-criteria decision-making techniques to assess facility (e.g., building, hangar) priorities, a utility function to calculate antiterrorism project mitigation values (e.g., protective glazing, wall coatings, and stand-off barriers) and optimization techniques to determine resource allocations across multiple, competing AT mitigation projects. The model has been realized in a cognitive support system developed as a set of loosely coupled Web services. The approach, model, and cognitive support system have been evaluated using the cognitive walkthrough method with prospective system users in the field.

A field-tested model and web-based application for anti-terrorism planning and resource allocation.

Exists as a web-based application that provides a service-oriented, composable architecture of core decision support functionality for anti-terrorism planning and resource allocation.

• Haynes, S. R., Kannampallil, T. G., Larson, L. L., & Garg, N. (2005). Optimizing Anti-Terrorism Resource Allocation. Journal of the American Society for Information Science and Technology, 56(3): 299-309.
• Haynes, S. R. (2006). Design Knowledge as a Learning Resource. Conference on Design Science Research in Information Systems and Technology, Claremont, CA.
• Haynes, S. R. (2005). Three Studies of Design Rationale as Explanation. In Rationale Management in Software Engineering. A. H. Dutoit, R. McCall, I. Mistrik and B. Paech, Springer Verlag, Springer-Verlag/Computer Science Editorial: 53-71.
• Haynes, S. R. and T. G. Kannampallil (2004). Learning, Performance, and Analysis Support for Complex Software Applications. In Proceedings of the 3rd Annual Pre-ICIS Workshop on HCI Research in MIS, Washington, DC.

Steven R. Haynes, Information Sciences and Technology
Lawrence L. Larson, Office of Military and Security Programs