International Journal of Information Technology & Decision Making

Using high-level tasks typical of managerial decisions, this experimental study examined the influence of computer assistance on solving ill-structured problems. Of specific interest were the influences of complex and simple assistance on decision performance and decision maker attitudes. Our findings suggest that performance and user attitudes can be enhanced by technology that provides clear and simple instruction in good problem-solving practices. However, when that technology adds a complex array of technical options and features, the assistance may fail to improve or/and may even diminish performance and damage user attitudes. Familiarization with such complex technology may not improve these outcomes. The findings regarding the relationship between assistance complexity and decision performance are consistent with those of studies that suggest complexity has a curvilinear influence on performance. When considering the application of technological assistance to ill-structured decision tasks, the complexity of the assistance should not be ignored.

This paper presents a two-phase interactive satisfying optimization method for fuzzy multiple objectives optimization with linguistic preference. This proposed approach utilizes the view that the more important objective has the higher desirable satisfying degree. The originally complex optimization problem is simplified and divided into two parts that are solved one by one. The decision maker can acquire satisfying solution of all the objectives under linguistic preference. Numerical example shows the efficiency, flexibility, and sensitivity of the proposed method.

When therapy using interferon medication for chronic hepatitis patients, various conceptual knowledge/rules will benefit for giving a treatment. The paper describes our work on cooperatively using various data mining agents including GDT-RS, learning with ordered information (LOI), and peculiarity oriented mining (POM) in a spiral discovery process with the multi-phase such as pre-processing, rule mining, and post-processing, for multi-aspect analysis of the hepatitis data and meta learning. GDT-RS is an inductive learning system for discovering decision rules. LOI discovers ordering rules and important features. POM finds peculiarity data/rules. Our methodology and experimental results show that the perspective of medical doctors will be changed from a single type of experimental data analysis towards a holistic view, by using our multi-aspect mining approach.

A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

In this paper, the problems of fuzzy multi-attribute group decision making in which the attribute values are given in the form of linguistic fuzzy numbers are studied. First of all, a new method called fuzzy dominance is given for ranking trapezoidal fuzzy numbers based on the credibility theory. Then the TOWA operator is presented to aggregate the trapezoidal fuzzy numbers. Furthermore, a new model is presented for the problems of fuzzy multi-attribute group decision making via TOWA operator, fuzzy dominance method and gray relative degree. Finally, a decision-making example is given to demonstrate the feasibility and rationality of this new model.

Although the theory of YinYang WuXing (YYWX) or YinYang-5-Elements (subsystems of human body) has been a major analytical basis for Traditional Chinese Medicine (TCM) for thousands of years, the theory has been primarily empirical in nature without a formal mathematical or scientific foundation. Based on bipolar set theory, YinYang bipolar linear algebra is introduced and an equilibrium- or nonequilibrium-based computational model of YYWX is proposed. Nourishing and regulating relations are defined; YYWX dynamic equations are formulated; global and local equilibrium and nonequilibrium conditions are established and proved. Computer simulations of equilibrium and nonequilibrium processes show that the new approach provides a unique scientific basis for future research and development in herbal medicine, Qi (vital energy), QiGong, JingLuo (acupuncture channels), and acupuncture. A decision support system is prototyped and presented for diagnostic decision analysis in TCM. Furthermore, this work is expected to provide a basic YinYang cellular network architecture for modern scientific research in socioeconomics as well as in genomics, bioeconomics, metabolism, computational biology, aging, artificial intelligence, and biomedical engineering.

A nation's competitiveness has become more and more important in forming government strategy and business decision making. This study proposes an optimization model, instead of regression model or neural network model, to induce rules for dynamic nations' competitiveness based on the Major Competitiveness Indicators of the World Competitiveness Yearbook. Fourteen attributes are used to form the dynamic rules expressed in "IF…THEN…" forms. According to the induced rules, the strategic implications are suggested for various groups of nations to improve or to sustain their competitiveness.

Two technically feasible nuclear waste repository options have been identified in Belgium. To select one for implementation, a study was carried out to compare the public perception and acceptance of the two options. In this paper, it is described how the study and selection process can be supported, and how the diversity and uncertainty in public opinions can be rationally modeled and analyzed by applying the Evidential Reasoning (ER) approach. The ER approach is a recent advancement for multi-criteria decision analysis (MCDA). By using belief decision matrices to model MCDA problems, both qualitative and quantitative information with various types of uncertainties can be taken into account in decision-making processes in a unified and logical format. Following an illustration of the ER approach and an outline of the selection problem, it is described how the ER approach can be used to model the problem, aggregate information, and facilitate sensitivity analysis. Some suggestions are made for future assessment studies.

Because of a deterioration in the quality of the environment, this paper studies the effects of the environment and the economy on environmental technology licensing in a homogeneous Cournot duopoly model in order to reduce environmental pollution and hence improve social welfare. To this end, two licensing methods — namely, a fixed-fee licensing method and a royalty licensing method — are compared. It is found that a high emission tax rate induces the innovator to not license the environmental technology to the licensee under the fixed-fee licensing method. As for social welfare, a large innovation scale of environmental technology does not guarantee that social welfare will be maximized. Finally, a large innovation scale of environmental technology is likely to increase consumer surplus if the marginal environmental damage is significant. Consumers are likely to prefer royalty licensing to fixed-fee licensing. This conclusion differs from Wang's finding in 2002.