CFP last date
22 April 2024
Reseach Article

Application of Fuzzy Expert Systems in Assessing Risk Management in the US Army

by Charles Karels, Heath Mccormick, Rania Hodhod
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 6
Year of Publication: 2015
Authors: Charles Karels, Heath Mccormick, Rania Hodhod
10.5120/19828-1676

Charles Karels, Heath Mccormick, Rania Hodhod . Application of Fuzzy Expert Systems in Assessing Risk Management in the US Army. International Journal of Computer Applications. 113, 6 ( March 2015), 10-16. DOI=10.5120/19828-1676

@article{ 10.5120/19828-1676,
author = { Charles Karels, Heath Mccormick, Rania Hodhod },
title = { Application of Fuzzy Expert Systems in Assessing Risk Management in the US Army },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 6 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number6/19828-1676/ },
doi = { 10.5120/19828-1676 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:13.971309+05:30
%A Charles Karels
%A Heath Mccormick
%A Rania Hodhod
%T Application of Fuzzy Expert Systems in Assessing Risk Management in the US Army
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 6
%P 10-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A risk management process is most effective when the users are properly educated on the process and the process itself promotes a uniform perception of risk. Every soldier in the US Army is expected to be capable of managing risk—eliminating it when possible or mitigating it to an acceptable level through the principles and application a formal, multi-step, iterative process known as the US Army Risk Management program. This paper describes a study in which the researchers developed and used a fuzzy rule based expert system to evaluate a respondent population's ability to assess risk using the US Army's Risk Management program, and to determine if there were any common characteristics amongst those respondents with similar output. The results showed that while some factors such as active duty versus reserve status yielded negligible differences, there existed a significant difference between the way the commissioned and non-commissioned officer participants perceived risk. This information is one key to understanding that the differences in the way commissioned and non-commissioned officers are taught the Risk Management process and how it can affect their perceptions of risk and suggests that a better, more uniform method of risk training could be developed for the training audiences.

References
  1. Field Manual 5-19 Composite Risk Management (2006). Headquarters, Department of the Army. Washington, DC.
  2. Department of the Army Form 7566, US Army Composite Risk Management Worksheet (2005). Headquarters, Department of the Army. Washington, DC.
  3. VanVactor, J. D. (2007). Risk Mitigation Through A Composite Risk Management Process: The US Army Risk Assessment. Organization Development Journal, 25(2).
  4. Prokop, J. , Pfeifer, D. (2013). How do you deal with operational risk? A survey of risk management practices in the German insurance sector. Journal of Risk Management in Financial Institutions, 6(4), 444-454.
  5. Rotar, L. J. , Kozar, M. (2012). Exploring the mechanisms for implementing a risk management process: overall approach and practical example. Performance Management, 223.
  6. Samvedi, A. , Jain, V. , Chan, F. T. (2013). Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research, 51(8), 2433-2442.
  7. Gates, S. , Nicolas, J. L. , Walker, P. L. (2012). Enterprise risk management: A process for enhanced management and improved performance. Management accounting quarterly, 13(3), 28-38.
  8. Army Techniques Publication 5-19 Risk Management (2014). Headquarters, Department of the Army. Washington, DC.
  9. Liwang, H. , Ericson, M. , Bang, M. (2014). An examination of the implementations of risk based approaches in military operations. Journal of military studies, 5(2), 1-26.
  10. Karmperis, A. , Sotirchos, A. , Tatsiopoulos, I. , Konstantinos, A. (2014). Risk assessment techniques as decision support tools for military operations. Journal of computations & modeling (online), 4(1), 67-81.
  11. Karmperis, A. , Sotirchos, A. , Tatsiopoulos, I. , Konstantinos, A. (2013). Decision support models for solid waste management: review and game-theoretic approaches. Elsevier, 33(5), 1290-1301.
  12. Costa-Font, J. , Mossialos, E. , Rudisill, C. (2009). Optimism and the perceptions of new risks. Journal of risk research, 12(1), 27-41.
  13. Gang, H. (2014). Individual differences in risk-taking tendency and framing effect. Social Behavior & Personality: An International Journal, 42(2), 279-284.
  14. Nicholson, N. , Soane, E. , Fenton-O'Creevy, M. , Willman, P. (2009). Personality and domain-specific risk taking. Journal of Risk Research, 8(2), 157-176.
  15. Weber, Elke U. , Ann-Rene Blais, and Nancy E. Betz (2002). A domain-specific risk-attitude scale: ´ measuring risk perceptions and risk behaviors. Journal of behavioral decision making, 15, 263–290.
  16. Dohmen, T. , Falk, A. , Huffman, D. , Sunde, U. , Schupp, J. , & Wagner, G. G. (2011). Individual risk attitudes: measurement, determinants, and behavioral consequences. Journal of the european economic association, 9(3), 522-550. doi:10. 1111/j. 1542-4774. 2011. 01015. x
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Expert Systems Hazard Identification Risk Management