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

Preferences, Utility, and Stochastic Approximation

Preferences, Utility, and Stochastic Approximation
View Sample PDF
Author(s): Yuri P. Pavlov (Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria) and Rumen D. Andreev (Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Bulgaria)
Copyright: 2019
Pages: 14
Source title: Advanced Methodologies and Technologies in Business Operations and Management
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-7362-3.ch055


View Preferences, Utility, and Stochastic Approximation on the publisher's website for pricing and purchasing information.


A complex system with human participation like “human-process” is characterized with active assistance of the human in the determination of its objective and in decision-taking during its development. The construction of a mathematically grounded model of such a system is faced with the problem of shortage of mathematically precise information that presents the human activity. A solution of this problem is to seek expression of different aspects of the complex system through description of the expert's preferences as an element of the system. The presentation of human preferences analytically with utility functions is an approach for their mathematical description. The objective of the chapter is to present an innovative approach to value-driven modeling of management based on preference-oriented decision making. A decision technology that realizes measurement of human's preferences as an analytic utility function is described. The utility theory and stochastic approximation are possible solutions for this problem that results in a value-based approach to modeling of complex systems.

Related Content

Veronica Baena, Marina Mattera. © 2021. 17 pages.
Raymond T. Stefani. © 2021. 19 pages.
Mauro Palmero, Kelly Price. © 2021. 38 pages.
Hyun Byun, Weisheng Chiu, Jung-sup Bae. © 2021. 17 pages.
Ho Keat Leng, Xinran Wu, Deping Zhong. © 2021. 14 pages.
Shi Ying Tan, Do Young Pyun. © 2021. 13 pages.
Ali Ahmed Abdelkader, Hussein Moselhy Syead Ahmed. © 2021. 23 pages.
Body Bottom