Numerically Imprecise Decision Making -

Despite the fact that unguided decision making might lead to inefficient and non-optimal decisions, decisions made at organizational levels seldom utilise decision analytical tools. A problem could be that all decision analytic software only is able to handle precise input, and no known software is [...]
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Despite the fact that unguided decision making might lead to inefficient and non-optimal decisions, decisions made at organizational levels seldom utilise decision analytical tools. A problem could be that all decision analytic software only is able to handle precise input, and no known software is capable of handling full scale imprecision, i.e. imprecise probabilities, values and weights, in the form of interval and comparative statements. Therefore, a natural question is how a reasonable decision analytical framework can be built based on prevailing interval methods, dealing with the problems of uncertain and imprecise input? Further, will the interval approach actually prove useful? The framework presented herein handles imprecise multi-level trees, multi-criteria, risk analysis, together with several different evaluation options. The framework supports interval probabilities, values, and criteria weights, as well as comparative statements, also allowing for mixing probabilistic and multi-criteria decisions. The framework has also been field tested in a number of studies, proving the usefulness of the interval approach.