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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Learning to Anticipate Flexible Choices in Multiple Criteria Decision-Making Under Uncertainty

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Author(s):
Azevedo, Carlos R. B. [1] ; Von Zuben, Fernando J. [1]
Total Authors: 2
Affiliation:
[1] Univ Estadual Campinas, Sch Elect & Comp Engn, BR-13083852 Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE TRANSACTIONS ON CYBERNETICS; v. 46, n. 3, p. 778-791, MAR 2016.
Web of Science Citations: 2
Abstract

In several applications, a solution must be selected from a set of tradeoff alternatives for operating in dynamic and noisy environments. In this paper, such multicriteria decision process is handled by anticipating flexible options predicted to improve the decision maker future freedom of action. A methodology is then proposed for predicting tradeoff sets of maximal hypervolume, where a multiobjective metaheuristic was augmented with a Kalman filter and a dynamical Dirichlet model for tracking and predicting flexible solutions. The method identified decisions that were shown to improve the future hypervolume of tradeoff investment portfolio sets for out-of-sample stock data, when compared to a myopic strategy. Anticipating flexible portfolios was a superior strategy for smoother changing artificial and real-world scenarios, when compared to always implementing the decision of median risk and to randomly selecting a portfolio from the evolved anticipatory stochastic Pareto frontier, whereas the median choice strategy performed better for abruptly changing markets. Correlations between the portfolio compositions and future hypervolume were also observed. (AU)

FAPESP's process: 12/16504-5 - A methodology for multicriteria stochastic anticipatory optimization
Grantee:Carlos Renato Belo Azevedo
Support type: Scholarships in Brazil - Doctorate