Unambiguous Goal Seeking Through Mathematical Modeling

Unambiguous Goal Seeking Through Mathematical Modeling

Giusseppi Forgionne (University of Maryland, Baltimore County, USA) and Stephen Russell (University of Maryland, Baltimore County, USA)
DOI: 10.4018/978-1-59904-843-7.ch100
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Abstract

All decision-making support systems (DMSS) provide both input and output feedback for the user. This feedback helps the user find a problem solution and captures any created knowledge for future reference. Goal seeking is an important form of DMSS feedback that guides the user toward the problem solution. Often in the decision support literature there is a concentration on providing forward-oriented decision assistance. From this perspective, the decision problem is viewed from the problem forward, towards a solution and advice is given with that orientation. In the literature, this is most often seen in “what-if” analysis. Goal seeking takes an inverted view where a preferred or optimal solution is known and the advice provided identifies values for the decision problem variables so that the optimal or preferred solution is obtained. Goal seeking approaches can be useful not only in identifying a solution, but also examining and explaining the relationships between decision variables. Unlike what-if analysis, which is forward oriented, goal seeking starts with a preferred outcome and decision makers do not have to manipulate decision variables in a recursive manner to examine different decision scenarios.

Key Terms in this Chapter

Decision-Making Process: Decision-making process is the process of developing a general problem understanding, formulating the problem explicitly, evaluating alternatives systematically, and implementing the choice.

Decision-Making Support System: Decision-making support system is a system (typically computerized) that provides partial or complete support for all phases of decision making: intelligence, design, choice, and implementation.

Goal Seeking: Goal seeking is the prescribing of an outcome and examination of what values variables relevant to that outcome must have, in order to attain the prescribed output.

Mathematical Model: Mathematical model is an abstract model that describes a problem, environment, or system using a mathematical language.

Quantitative Analysis: Quantitative analysis is the expression of a problem using a mathematical formulation and then measuring or estimating variables in the created mathematical construct.

Model-Based Decision Support: Model-based decision support is a system that uses one or more quantitative models (e.g., statistical, financial, optimization) to support decision-making problems.

What-If Analysis: What-if analysis is the manipulation of input variables in order to “ask” what the effect will be on the output, if these variables have a given value or values.

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