By Hitchcock F. L.

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Use of monetary units usually implies performing a costbenefit analysis (Morgan and Henrion, 1990). If a comparative analysis between decision options is needed, and actual cost is relatively unimportant or is considered difficult to specify for some factors, then utility can potentially be specified instead using, for example, multi-attribute utility theory (MAUT) (Keeney and Raiffa, 1993), Analytical Hierarchy Process (AHP) (Saaty, 1980), and outranking methods (Rogers and Bruen, 1998). However, if, in any of these 1 Basic Steps for the Development of Decision Support Systems 17 approaches, a single attribute is specified in monetary terms, then, in a complete model, the value of all other attributes could be translated into monetary value.

Under the full Bayesian paradigm, decision analysis is performed by maximizing expected utility, and updating of information is performed using Bayes’ Theorem. Probability networks are extended with multi-criteria decision-making models to achieve a complete quantitative Bayesian decision support system. Other metrics for addressing uncertainty or decision analysis are possible, including fuzzy logic, random sets, and belief functions; however, as mentioned earlier, none of these metrics satisfy the coherence axioms of Bayesian decision analysis (Black, 1996a).

Annals of Probability 14(2):710–723 PostgreSQL Global Development Group (accessed 2008) PostgreSQL. org> Refractions Research (accessed 2008) PostGIS. net> Regents of the University of Minnesota (accessed 2008) MapServer. gis. edu> Ripley, BD (1996) Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK. 1 Basic Steps for the Development of Decision Support Systems 27 Rogers M, Bruen M (1998) A new system for weighting environmental criteria for use within ELECTRE III.