Preferences, Utility and Prescriptive Decision Control in Complex Systems
Keywords:
Preferences, Utility, Stochastic Approximation, Complex systems, Edgeworth boxAbstract
The evaluation of the preferences based utility function is a goal of the human cantered control (management) design.The achievement of this goal depends on the determination and on the presentation of the requirements, characteristics and preferences of the human behaviour in the appropriate environment (management, control or administration of complex processes). The decision making theory, the utility and the probability theory are a possible approach under consideration. This paper presents an approach to evaluation of human’s preferences and their utilization in complex problems.The stochastic approximation is a possible resolution to the problem under consideration. The stochastic evaluation bases on mathematically formulated axiomatic principles and stochastic procedures. The uncertainty of the human preferences is eliminated as typically for the stochastic programming. The evaluation is preferences-oriented machine learning with restriction of the “certainty effect and probability distortion” of the utility assessment. The mathematical formulations presented here serve as basis of tools development. The utility and value evaluation leads to the development of preferences-based decision support in machine learning environments and iterative control design in complex problems.Downloads
References
Y. Pavlov, Subjective Preferences, Values and Decisions: Stochastic Approximation Approach, Proceedings of Bulgarian Academy of Sciences (Comptes rendus de L’Academie bulgare des Sciences), Tome 58, N4, 367-372, 2005.
Available: http://cat.inist.fr/?aModele=afficheN&cpsidt=16768115
Y. Pavlov, Equivalent forms of Wang-Yerusalimsky kinetic model and optimal growth rate control of fed-batch cultivation processes, Online Journal Bioautomation, 11, 1-13, 2008. Available: http://core.kmi.open.ac.uk/display/791332
Y. Pavlov, Specific growth rate and sliding mode stabilization of fed-batch processes, Global Journal of Computer Science and Technology, 11(20), (1.0), 19-29, 2011. Available: http://computerresearch.org/stpr/index.php/gjcst/article/viewArticle/935
Y. Pavlov and R. Andreev, Decision control, management, and support in adaptive and complex systems: Quantitative models, Hershey, PA: IGI Global, 2013. Available: http://www.igi-global.com/book/decision-control-management-support-adaptive/70775
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