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TopBackground
The description of the value based decision requires basic analytical representation of the DM’s preferences. The mathematical description on such a fundamental level requires basic mathematical terms like sets, relations and operations over them, and their gradual elaboration to more complex and specific terms like functions, operators on mathematically structured sets as well, and equivalency of these descriptions with respect to a given real object. In the last aspect of equivalency of the mathematical descriptions we enter the theory of measurements and scaling (Luce, Krantz, Suppes, & Tversky, 1990; Pfanzagl, 1971).
Key Terms in this Chapter
Value Based Decision: Enables the assessment of a value for every design option so that options can be rationally compared and a choice taken. At the whole system level, the objective function which performs this assessment of value is called a “value model.”
Expected Utility Theory: Utility theory is a normative approach to the matter of how people should rationally make choice under uncertainty.
Machine Learning: A computational methodology that provides automatic means of improving programmed tasks from experience.
Personalized E-Learning: An approach for adaptation of e-learning resources and environment to learner’s characteristics.
Value-Driven Design: Systems engineering strategy which enables multidisciplinary design optimization. Value-driven design creates an environment that enables optimization by providing designers with an objective function. The objective function inputs all the important attributes of the system being designed, and outputs a score.
Stochastic Programming: Subfield of mathematical programming that considers optimization in the presence of uncertainty.
Value Based Management: An approach to management that aligns an organization’s overall aspirations, analytical techniques and management processes to focus management decision making on the key drivers of value.