Commenced in January 2007
Paper Count: 30172
Integrating Security Indifference Curve to Formal Decision Evaluation
Abstract:Decisions are regularly made during a project or daily life. Some decisions are critical and have a direct impact on project or human success. Formal evaluation is thus required, especially for crucial decisions, to arrive at the optimal solution among alternatives to address issues. According to microeconomic theory, all people-s decisions can be modeled as indifference curves. The proposed approach supports formal analysis and decision by constructing indifference curve model from the previous experts- decision criteria. These knowledge embedded in the system can be reused or help naïve users select alternative solution of the similar problem. Moreover, the method is flexible to cope with unlimited number of factors influencing the decision-making. The preliminary experimental results of the alternative selection are accurately matched with the expert-s decisions.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334407Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1289
 M. B. Chrissis, M. Konrad, and S. Shrum, CMMI® Second Edition Guidelines for Process Integration and Product Improvement, Addison- Wesley, Boston, 2007.
 S. Thompson, T. Torabi, and P. Joshi, "A Framework to Detect Deviations During Process Enactment", Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference, IEEE Computer Society, July 11-13, 2007, pp 1066 - 1073.
 H. Markowitz, "Portfolio Selection", The Journal of Finance, Vol. 7, No. 1, Blackwell Publishing, UK, Mar, 1952, pp. 77-91.
 M. H. Partovi and M. Caputo, "Principal Portfolios: Recasting the Efficient Frontier", Economics Bulletin, Vol. 7 no. 3, 2004, pp. 1-10.
 K. Boness, A. Finkelstein, and R. Harrison, "A lightweight technique for assessing risks in requirements analysis", IET Periodicals, Volume: 2, Issue: 1, Institution of Engineering and Technology, UK, February 2008, pp 46-57.
 M. Tom, Machine Learning, McGraw Hill, New York, 1997.
 S. Donna, Quality, fourth edition, Pearson Prentice Hall, New Jersey, 2006.