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Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Preferences, Utility, Value-Driven Modeling, and Decision Support

Preferences, Utility, Value-Driven Modeling, and Decision Support
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Author(s): Yuri P. Pavlov (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria), Rumen D. Andreev (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria), Valentina T. Terzieva (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria), Katia A. Todorova (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria)and Petia I. Kademova-Katzarova (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria)
Copyright: 2021
Pages: 17
Source title: Encyclopedia of Organizational Knowledge, Administration, and Technology
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3473-1.ch021

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Abstract

The inclusion of organizational knowledge in the process of modeling of complex systems is an essential step in the decision-making. This needs normative description of the system structure in terms of objective and sub-objectives. In phenomena with human participation, the emphasis is on the cardinal significance as preferences. The approach to modeling such information is the utility theory. This chapter demonstrates a value-driven approach and presents two mathematical models of complex processes. The normative approach is based on stochastic-approximation methods for analytical representation of qualitative preferences. The approach is illustrated in two practical oriented applications. The first one represents modeling of exhaustible timber production by reflecting socio-economic and forest-related ecological factors. The second one concerns the determining of the optimal usage of active and passive technology-based resources in classroom teaching. The approach permits mathematical modeling and even control and prescriptive decision support in complex processes.

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