International Journal of Information Technology & Decision Making

We introduce the uncertain generalized OWA (UGOWA) operator. This operator is an extension of the OWA operator that uses generalized means and uncertain information represented as interval numbers. By using UGOWA, it is possible to obtain a wide range of uncertain aggregation operators such as the uncertain average (UA), the uncertain weighted average (UWA), the uncertain OWA (UOWA) operator, the uncertain ordered weighted geometric (UOWG) operator, the uncertain ordered weighted quadratic averaging (UOWQA) operator, the uncertain generalized mean (UGM), and many specialized operators. We study some of its main properties, and we further generalize the UGOWA operator using quasi-arithmetic means. The result is the Quasi-UOWA operator. We end the paper by presenting an application to a decision-making problem regarding the selection of financial strategies.

Group awareness information represents things such as group members' roles and responsibilities, their positions on an issue, their status, and the state of various group processes that group members know about when they work together. The group awareness information presented in this paper is designed to capture group member activities and their behaviors in web-based collaborative work. It consists of activity, availability, and commitment/disposition information. The first two parameters appear as a visual display representing cumulative data and changes accordingly when the group begins working together. The last parameter is captured during group work and is summarized at the end of the group work task. The application of this proposed scheme was designed and implemented in a web-based group decision support system. This paper reports on the results of a study in a controlled experiment and a field test experiment that examined group performance on a given task in a web-based group decision support system with and without group awareness information. In particular, the study examined how group awareness information impacts the quality of the work effort and a given task, group decision making by members in the same group and different groups, the communications among group members during the completion of an online collaborative authoring task, the cohesiveness among group members in a web-based group decision support system, and the commitment/disposition of engagement of each member of the work group.

A line of credit is one of the most flexible financing tools for companies. Banks give companies lines of credit to strengthen their profitability and competitive ability. On the other hand, companies draw the lines of credit that will increase banks' credit risk. It is very difficult for banks to determine the lines of credit for an enterprise group. Based on principle of credit risk evaluation and structural model, this paper first defines bank's tolerable default risk and lines of credit, and then analyzes integrated lines of credit of parent and subsidiary companies. The results of this research indicate that the lines of credit of single company is related to its asset value growth, and integrated lines of credit of an enterprise group is also related to member's asset value growth and the associated relationships. Furthermore, this study shows that the integrated lines of credit of an enterprise group can be determined by the weighted sum of member's lines of credit and the computational formula of weight. This study provides a quantitative analysis tool to ascertain the enterprise group's integrated lines of credit and analyze how the associated relationships affect the integrated lines of credit.

Utility computing services constitute business opportunities in the customer-to-customer (C2C) marketplace. Providers deal with the risks and uncertainties faced by users while transacting with unfamiliar counterparts. Ensuring trustworthy transactions among unfamiliar users is an important condition of utility-computing service. Many peer-to-peer (P2P) sites utilize reputation systems; however, such systems are subject to lack of reviewer objectivity and robustness against attacks as well as unfair ratings. In this study, we propose the manual adjustment of reputation ratings by means of transaction monitoring and establish that the resulting enhanced objectivity and robustness in utility-computing service transactions will strengthen the trust management system. We also propose the optimal level of monitoring and penalizing activities based on the level of privacy concerns among users and the appropriate complementary service.

This paper outlines a decision framework that incorporates state-of-the-art earthquake engineering information and decision maker preferences into multicriteria decision models to support earthquake risk mitigation decisions. Seismic risk analysis of a structure is utilized for probabilistic estimation of the anticipated seismic losses, which in turn is used as inputs to the decision analysis for seismic rehabilitation of the structure. Three decision models are used to provide insight into the value of system interventions to reduce earthquake risks: (1) an equivalent cost model, (2) multi-attribute utility theory, and (3) joint probability decision making. Guidelines for selecting and applying multicriteria decision models for seismic rehabilitation of building structures are derived based on preferences for including risk attitudes and for measuring values. The detailed procedures for the selection and application of the decision models for seismic rehabilitation of building structures are demonstrated through a case study, where a collection of hospitals in a metropolitan area is examined.

Fuzzy regression has been applied to marketing, management, and sales forecasting for many years. In this paper, two types of forecasting methods within the framework of fuzzy regression analysis are discussed. The first type is a conventional forecasting method, which intends to find the value of a dependent variable when the given values of independent variables are beyond the range covered by historical data. The second type considers forecasting in which the necessary values of independent variables are desired when the given value of a dependent variable does not fall within the range of historical data. While the first type can be applied in planning, the second one is useful for control and management. Numerical examples are provided for illustration.

This paper develops a model of pricing for state-owned enterprise (SOE) property rights transfer in a dynamic economic environment. We incorporate competition and imperfect information in the model, and determine the equilibrium selling price and optimal timing of takeovers by solving a real options exercise game between bidders and a target. The main conclusions are consistent with real-world experience. The model generates new results as the equilibrium transfer price and the optimal timing relate to industry, the target, the transfer costs, the expected synergies, the re-divestiture option, and the transfer markets' microstructure. The more the bidders' expected synergy effects, the faster the acquisitions. The expected synergy effects differ among various bidders, and auction is the only way to reflect the synergy effects correctly. An active market for corporate control will be helpful for the transfer of SOE property rights. The model's applications include pricing M&A, venture capital, and IPOs.

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.