Abstract
The decision making process is a sequence of (mostly mental) actions. But individual decision making is a fuzzy process that lacks a clear workflow structure. This issue may decrease the quality of data-centric business decisions where information must be processed in the right order and used at the right time. We argue that, when faced with such a decision, step-by-step recommendation provides help in steering the process and valuable guidance in improving it. Our Data Decision Model (DDM) is an acyclic graph that suits the fuzzy nature of decision processes. In our approach, the recommendation is based on an aggregated DDM extracted from a large number of individuals. This paper introduces two algorithms that, given a certain state of the process, provide suggestions for the next action the decision maker should perform.
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Petrusel, R., Stanciu, P.L. (2012). Making Recommendations for Decision Processes Based on Aggregated Decision Data Models. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds) Business Information Systems. BIS 2012. Lecture Notes in Business Information Processing, vol 117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30359-3_24
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DOI: https://doi.org/10.1007/978-3-642-30359-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30358-6
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