Using Real-Time Decision Tools to Improve Distributed Decision-Making Capabilities in High-Magnitude Crisis Situations

Decision Sciences

Volumn 42, Issue 2, 2011, Pages 477-493

  Moskowitz, Herbert ^a   Drnevich, Paul ^b   Ersoy, Okan ^c   Altinkemer, Kemal ^a   Chaturvedi, Alok ^a  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF ALABAMA   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Distributed Decision Making; Dynamic Modeling; Information Technology (IT) based Decision Support Systems; Machine Learning Algorithms; Multi Organizational Collaborative Decision Making; Real Time Decision Tools; Support Vector Machines (SVM)

Indexed keywords

EID: 79955368147     PISSN: 00117315     EISSN: 15405915     Source Type: Journal    
DOI: 10.1111/j.1540-5915.2011.00319.x     Document Type: Article

References (28)

1. Carley, K., Altman, N., Kaminsky, B., Nave, D., & Yahja, A. (2004). Biowar: A city-scale multi-agent network model of weaponized biological attacks. Technical Report (CMU-SRI-04-101). Carnegie Mellon University, Pittsburgh, PA: CASOS.
2. Chaturvedi, A., Dolk, D., & Drnevich, P. (in press). Design principles for virtual worlds. MIS Quarterly.
3. Chi, H., Ersoy, O., Moskowitz, H., & Altinkemer, K. (2007a). Toward automated intelligent manufacturing systems (AIMS). INFORMS Journal of Computing, 19(2), 302-312.
4. Chu, P., & Spires, E. (2000). The joint effects of effort and quality on decision strategy choice with computerized decision aids. Decision Sciences, 31(2), 259-292.
5. Drnevich, P., Mehta, S., & Dietz, E. (2006). Coordinating effective government response to bio-terrorism. In S. Amass, A. Chaturvedi, & S. Peeta (Eds.), Advances in Homeland Security, Volume II: Guiding Future Homeland Security Policy-Directions for Scientific Inquiry. West Lafayette, IN: Purdue University Press, 55-78.
6. Drnevich, P., Ramanujam, R., Mehta, S., & Chaturvedi, A. (2009). Affiliation or situation: What drives strategic decision making in crisis response Journal of Managerial Issues, 21(2), 216-231.
7. Drnevich, P., Brush, T., & Chaturvedi, A. (2010). Examining the implications of process and choice for strategic decision making effectiveness. International Journal of Decision Support System Technology, 2(3), 1-15.
8. Goes, P., Pino, J., & Vakharia, A. (2008). Call for papers: Special topic forum new frontiers in collaborative decision making. Decision Sciences, 39(3), 595-597.
9. Green, L., & Kolesar, P. (2004). Improving Emergency Responsiveness with Management Science. Management Science, 50(8), 1001-1014.
10. Guide, V., & Van Wassenhove, L. (2007). Dancing with the devil: Partnering with industry but publishing in academia, Decision Sciences, 38(4), 531-546.
11. Hardy, C., & Phillips, N. (1998). Strategies of engagement: Lessons from the critical examination of collaboration and conflict in an interorganizational domain. Organization Science, 9(2), 217-230.
12. Harrison, J., Lin, Z., Carroll, G. & Carley, K. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32(4), 1229-1245.
13. Hevner, A., March, S., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105.
14. Holmstrom, J., Ketokivi, M., & Hameri, A. (2009). Bridging practice and theory: A design science approach. Decision Sciences, 40(1), 65-87.
15. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning, 137-142.
16. Joachims, T. (2002). Learning to classify text using support vector machines. Boston: Kluwer Academic.
17. Kumar, K., & Niti, K. (1998). Differential learning and interaction in alliance dynamics: A process and outcome discrepancy model. Organization Science, 9(3), 356-367.
18. Kumar, K., & Van Dissel, H. (1996). Sustainable collaboration: Managing conflict and cooperation in interorganizational systems. MIS Quarterly, 20(3), 279-300.
19. Lin, Z., & Carley, K. (1993). Proactive or reactive: An analysis of the effect of agent style on organizational decision making performance. International Journal of Intelligent Systems in Accounting, Finance, and Management, 2(4), 271-287.
20. Lin, Z., & Carley, K. (1997). Organizational response: The cost performance tradeoff. Management Science, 43(2), 217-234.
21. McKay, S. C., Chaturvedi, A., & Adams, D. E. (2011). A process for anticipating and shaping adversarial behavior. Naval Research Logistics, doi
22. Osborn, R., & Hagedoorn, J. (1997). The institutionalization and evolutionary dynamics of interorganizational alliances and networks. Academy of Management Journal, 40(2), 261-278.
23. Ross, A., Buffa, F., Droge, C., & Carrington, D. (2009). Using buyer-supplier performance frontiers to manage relationship performance. Decision Sciences, 40(1), 37-64.
24. Schwaighofer, A. (2005). MATLAB interface to SVM light. Institute for Theoretical Computer Science at Graz University of Technology, accessed October 23, 2007 available at
25. Sexton, R., Sriram, R., & Etheridge, H. (2003). Improving decision effectiveness of artificial neural networks: A modified genetic algorithm approach. Decision Sciences 34(3), 421-442.
26. Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on individual-level technology adoption with a focus on decision making. Decision Sciences 37(4), 497-518.
27. Westley, F., & Vrendenburg, H. (1997). Interorganizational collaboration and the preservation of global biodiversity. Organization Science, 8(4), 381-403.
28. Yi, M., & Davis, F. (2001). Improving computer training effectiveness for decision technologies: Behavior modeling and retention enhancement. Decision Sciences, 32(3), 521-544.