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A Simulation of Partial Information Use in Decision Making: Implications for DSS Design
Author(s) -
Karim Ahmer S.,
Hershauer James C.,
Perkins William C.
Publication year - 1998
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1998.tb01344.x
Subject(s) - computer science , decision support system , decision analysis , operations research , variety (cybernetics) , optimal decision , decision quality , context (archaeology) , information system , risk analysis (engineering) , quality (philosophy) , management information systems , management science , offset (computer science) , data mining , decision tree , knowledge management , artificial intelligence , engineering , medicine , paleontology , team effectiveness , statistics , philosophy , mathematics , electrical engineering , epistemology , biology , programming language
Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision‐making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost‐benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer‐based decision aid. Simulation experiments are conducted with a completely specified model based on the cost‐benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.