Freedom of Choice and Creativity in Multicriteria Decision Making

被引:0
作者
Skulimowski, Andrzej M. J. [1 ]
机构
[1] AGH Univ Sci & Technol, Dept Decis Sci, Chair Automat Control, Al Mickiewicza 30, PL-30050 Krakow, Poland
来源
KNOWLEDGE, INFORMATION, AND CREATIVITY SUPPORT SYSTEMS | 2011年 / 6746卷
关键词
Multicriteria decision-making; freedom of choice; creativity; artificial autonomous decision systems; reference sets;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents new approaches to formulating and solving complex real-life decision-making problems, making use of the creativity concept. We assume that the decision-making process is embedded in the system of views and mutual relations between the decision-makers and their surrounding environment, so that creativity, as defined formally in Sec. 2, could play a primary role in the decision-making process. We will investigate multicriteria decision problems, where the decision-maker is unable to fully follow decision-making rules resulting from a standard mathematical formulation of multicriteria optimization problem. This is either due to external conditions (such as the need to make a quick decision, loss of data, or lack of data processing capabilities) or when the decision-maker can manifest creativity related to the hidden internal states of the decision-making process. We will provide a formal definition of freedom of choice (FOC), specifying three levels of FOC for multicriteria decision-making (MCDM) problems. Then we will point out that creativity in decision-making can be explained within the framework of autonomous and free decisions, and that decision-making freedom is a necessary prerequisite for creativity. The methods presented here can be applied to analyzing human decision-making processes and conditions allowing the expression of creativity as well as to designing pathways leading to creative decision-making in artificial autonomous decision systems (AADS). The applications of the latter include visual information retrieval, financial decision-making with feature identification, intelligent recommenders, to name just a few.
引用
收藏
页码:190 / +
页数:2
相关论文
共 15 条
  • [1] Etzioni O., 2003, 9 ACM SIGKDD INT C K, P119
  • [2] Hatzilygeroudis I., 2005, INT J HYBRID INTELL, V1, P111, DOI [10.3233/HIS-2004-13-401, DOI 10.3233/HIS-2004-13-401]
  • [3] Ishizaka A., 2006, J INT LEARNING RES, V17, P57
  • [4] A FORMAL MODEL OF CREATIVE DECISION-MAKING
    KIM, SH
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 1991, 8 (01) : 53 - 65
  • [5] Wrappers for feature subset selection
    Kohavi, R
    John, GH
    [J]. ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) : 273 - 324
  • [6] Miettinen K, 2012, NONLINEAR MULTIOBJEC, V12
  • [7] Feature selection algorithms: A survey and experimental evaluation
    Molina, LC
    Belanche, L
    Nebot, A
    [J]. 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2002, : 306 - 313
  • [8] Rotter P, 2009, ARTIF INTELL, P235
  • [9] Skulimowski A., 1996, MONOGRAPHS SERIES
  • [10] Skulimowski A. M. J., 1985, Foundations of Control Engineering, V10, P25