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A Multicriteria Model for the Evaluation of Intelligent Decision-making Support Systems (i-DMSS)

  • Gloria Phillips-Wren
  • Manuel Mora
  • Guisseppi A. Forgionne
  • Leonardo Garrido
  • Jatinder N. D. Gupta
Part of the Decision Engineering book series (DECENGIN)

Abstract

Although traditional decision-making support systems (DMSS) have been researched extensively, few, if any, studies have addressed a unifying architecture for the evaluation of intelligent DMSS (i-DMSS). Traditional systems have often been evaluated in the literature on the basis of single-outcome measures, such as decreased cost, increased profit, or improved forecasting, compared to decision making without a DMSS. In cases in which other metrics are used for evaluation, process measures are most often cited, such as increased efficiency, organizational learning, and increased speed. Previous research by the authors has shown that a multicriteria evaluation for DMSS can be provided, combining both outcome and process measures into a single metric using the analytic hierarchy process (AHP). However, the specific categories that should be utilized as evaluation measures have not been defined, and no studies have focused exclusively on categories for the evaluation of i-DMSS. This chapter explores the concept of intelligence in general, and artificial intelligence in particular, as it relates to aiding decision making. It then proposes an architecture for the evaluation of i-DMSS and applies the model to empirical systems. The results are: (1) recognition of the contribution of AI to i-DMSS; (2) identification of the criterion (or criteria) used to evaluate i-DMSS; (3) categorization of the evaluation measures; (4) an architecture for evaluation for i-DMSS; and (5) recommendation of a multicriteria model to assess i-DMSS.

Keywords

Analytic Hierarchy Process Decision Support System Operational Research Society Intelligent Behavior Analytic Hierarchy Process Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  • Gloria Phillips-Wren
    • 1
  • Manuel Mora
    • 2
  • Guisseppi A. Forgionne
    • 3
  • Leonardo Garrido
    • 4
  • Jatinder N. D. Gupta
    • 5
  1. 1.Department of Information SystemsLoyola College in MarylandBaltimoreUSA
  2. 2.Department of Information SystemsAutonomous University of AguascalientesAguascalientesMexico
  3. 3.Department of Information SystemsUniversity of Maryland Baltimore CountyCatonsvilleUSA
  4. 4.Center for Intelligent SystemsMonterrey TechMonterrey, N.L.Mexico
  5. 5.College of Administrative ScienceThe University of Alabama in HuntsvilleHuntsvilleUSA

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