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kritis:smart_and_fcm_in_gdm_environment_fivam

Simple Multi-Attribute Rating Technique (SMART)

SMART is a compensatory method of multiple criteria/attribute decision making (MCDM), developed by Edwards in 1971. This method was designed to provide a simple way to implement the beginnings of MAUT. SMART uses the Simple Additive Weight (SAW) method as a basis for obtaining the total values of individual alternatives to rank them according to the order of preference.

Edwards, W. (1971). ‘Social utilities’, Engineering economist, Summer symposium series (Vol. 6).

Edwards, W. (1977). How to use multiattribute utility measurement for social decision making. IEEE Transactions on Systems, Man and Cybernetics, 7, 326–340.

Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multi attribute utility measurement. Organizational Behavior and Human Decision Processes, 60, 306–325.

Fuzzy Cognitive Maps (FCM) methodology

FCM methodology is a natural extension to cognitive maps, which can be found in the fields of economics, sociology and political science. It is originated from the combination of Fuzzy Logic and Neural Networks for modeling complex systems. A FCM describes the behavior of a system in terms of concepts; each concept represents an entity, a state, a variable or a characteristic of the system.

Axelrod, R. (1976). Structure of decision: the cognitive maps of political elites. Princeton University Press.

Kosko, B. (1986). Fuzzy cognitive maps. International Journal on Man–Machine Studies, 24(1), 65–75.

Kosko, B. (1991). Neural networks and fuzzy systems. Englewood Cliffs: Prentice Hall.

Dickerson, J. A., & Kosko, B. (1997). Virtual worlds as fuzzy cognitive maps. In B. Kosko (Ed.), Fuzzy engineering (pp. 125–141). Upper Saddle River, NJ: Prentice Hall.

Group decision-making (GDM)

Fuzzy integrated vulnerability assessment model (FIVAM)

The FIVAM framework in this study is based on fuzzy set theory, SMART and FCM methodology in the GDM environment. In this integrated utilization, fuzzy SMART is used as a simple and effective MCDM technique to weight the vulnerability criteria and to calculate the initial vulnerability value of the components with respect to these weighted criteria. After calculating the initial vulnerability values of all components, the physical dependencies of functions on components and the logical dependencies of system on functions are determined. Then, the initial vulnerability values of both the functions and the system are computed using these dependencies and component vulnerability values ignoring the possible interdependencies among the system functions. In the next phase, FCM methodology is applied to simulate the system vulnerability behavior depending on the vulnerabilities and the interdependencies among the system functions. After calculating the vulnerability values of the functions in the long run by using FCM, the system function and component vulnerabilities are recalculated by considering the effects of these possible interdependencies among the system functions. According to these results, the most critical functions and components in the system are determined and ranked. Finally, the vulnerabilities before and after the FCM simulation are compared and evaluated.

kritis/smart_and_fcm_in_gdm_environment_fivam.txt · Last modified: 2015/03/24 15:32 (external edit)