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Decision Model

Decision models provide a framework, usually in the form of an algorithm or set of algorithms, for organizing and presenting information about a decision problem that is intended to help decision makers with activities such as choosing among alternatives or allocating resources among a set of alternatives. Practitioners in the field of decision science routinely make the point that decision models are not intended to replace decision makers, but rather to facilitate their decision processes. There is a wide array of decision modeling methods, ranging from the very simple to the very sophisticated. The simplest decision model simply organizes the elements of a decision problem into a table of alternatives and their attributes. In this model, the attributes of each alternative are scored (usually on some standardized scale) with respect to how well each satisfies some criterion, and one then adds up the scores of each alternative to identify the best choice. Multi-attribute utility theory takes the latter process one step further by formalizing scores with utility functions, and optionally weighting the contributions of attributes. Still more sophisticated methods include the analytic hierarchy process and Bayesian decision networks, each of which provide additional tools for helping to structure a problem. Most software tools that implement a decision-support process also include various tools for problem structuring, dealing with uncertainties, evaluating model sensitivities, and documenting the process.

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