Stelios H. Zanakis

Multi-attribute decision making: A simulation comparison of select methods

Abstract Several methods have been proposed for solving multi-attribute decision making problems (MADM). A major criticism of MADM is that different techniques may yield different results when applied to the same problem. The problem considered in this study consists of a decision matrix input of N criteria weights and ratings of L alternatives on each criterion. The comparative performance of some methods has been investigated in a few, mostly field, studies. In this simulation experiment we investigate the performance of eight methods: ELECTRE, TOPSIS, Multiplicative Exponential Weighting (MEW), Simple Additive Weighting (SAW), and four versions of AHP (original vs. geometric scale and right eigenvector vs. mean transformation solution). Simulation parameters are the number of alternatives, criteria and their distribution. The solutions are analyzed using twelve measures of similarity of performance. Similarities and differences in the behavior of these methods are investigated. Dissimilarities in weights produced by these methods become stronger in problems with few alternatives; however, the corresponding final rankings of the alternatives vary across methods more in problems with many alternatives. Although less significant, the distribution of criterion weights affects the methods differently. In general, all AHP versions behave similarly and closer to SAW than the other methods. ELECTRE is the least similar to SAW (except for closer matching the top-ranked alternative), followed by MEW. TOPSIS behaves closer to AHP and differently from ELECTRE and MEW, except for problems with few criteria. A similar rank-reversal experiment produced the following performance order of methods: SAW and MEW (best), followed by TOPSIS, AHPs and ELECTRE. It should be noted that the ELECTRE version used was adapted to the common MADM problem and therefore it did not take advantage of the methods capabilities in handling problems with ordinal or imprecise information.

A survey of business failures with an emphasis on prediction methods and industrial applications

Abstract The considerable interest in the prediction of business failures is reflected in the large number of studies presented in the literature. Various methods have been used to construct prediction models. This paper provides a review of the literature and a framework for the presentation of this information. Articles can be classified according to the country, industrial sector and period of data, as well as the financial ratios and models or methods employed. Relationships and research trends in the prediction of business failure are discussed.

A Monte Carlo investigation of incomplete pairwise comparison matrices in AHP

The Analytic Hierarchy Process (AHP) is a decision analysis technique used to evaluate complex multiattribute alternatives among one or more decision makers. It imposes a hierarchical structure on any complex multicriterion problem. However, a major drawback of the AHP is that a large number of pairwise comparisons is needed to calibrate the hierarchy. When there are a few levels and sublevels, the AHP can be applied in a straightforward manner to derive the weights (relative preference for each alternative). As the size of the hierarchy increases, the number of pairwise comparisons increases rapidly. It is well established in the marketing and consumer behavior literature that in a very long interview, even under the best circumstances, the respondent is likely to suffer from information overload. Recognition of this problem was the motivation which led to the investigation of a modification of AHP which required less data collection, i.e., a reduction in the threat of information overload. The first question to be answered is the effect on AHP weights due to different patterns of missing data likely to result from reallife data collection. In this study, a Monte Carlo simulation was conducted, which uses the Incomplete Pairwise Comparisons (IPC) algorithm [14], to investigate the effect of reduced sets of pairwise comparisons in the AHP. Data for the study were generated with known structure and comparisons made between complete and incomplete matrices. The results of the simulation suggest that incomplete sets of pairwise comparison matrices can capture the attribute level weights without significant loss of accuracy and independent of decision model (form and amount of error) considered.

Heuristic “Optimization”: Why, When, and How to Use It

The pressing need of real-world problems for quick, simple, and implementable solutions coupled with a recently increased research productivity on improved and rigorously evaluated heuristic methods are rapidly increasing the need, usage, and respect for heuristics. This paper presents a framework for heuristic “optimization” by systematically examining this change in attitudes towards heuristics, their desirable features and proper usage.

Engaging Group E-Learning in Virtual Worlds

Abstract: E-learning has seen tremendous growth in recent years. More and more, university courses are now available online to a potentially global audience. However, a significant shortcoming of e-learning technologies has been poor support for group-oriented learning. We believe that virtual worlds offer a potential solution. Unlike videoconferencing (for instance), virtual worlds provide a shared visual space for students to meet and interact (via avatars). Not only do students share the quasi-realism of a 3D environment where participants can see and hear one another, they also have the capability to manipulate artifacts together. These factors provide a strong sense of group presence, which leads to engaging group learning interactions.

Student Anxiety and Attitudes in Business Statistics

Abstract In the past, infant mortality rates in Jewish communities throughout the world were dramatically lower than those of their host populations. Nineteenth-century Venice was no exception: whereas the Catholic rates were about 25–30 percent, the Jewish rate was as low as 14 percent or even less. Several factors have been put forward to explain such differentials, including genetic makeup, religious prescriptions, personal hygiene, austere habits, community welfare institutions and social cohesion, higher cultural level, fertility control, prolonged breastfeeding, and the like. A comparison between a sample of the Jewish population and two parishes with similar social composition shows that, in the Venetian case at least, most of the factors cannot account for such a striking difference. Furthermore, both descriptive and hazard analyses clearly indicate that, although levels were dramatically different, infant mortality patterns were remarkably similar among Venetian Jews and Catholics, who had almost...

Heuristic methods and applications: A categorized survey

An extensive search of journal publications on heuristic methods and applications produced 442 articles published in 37 journals during the last sixteen years. A scheme is employed to categorize each article according to 12 classes of heuristic approaches and 144 areas of applications (the latter taken from the OR/MS subject classification codes). An analysis of these data reveals some interesting historical patterns and directions for future work. This categorized survey should be helpful to students, teachers, researchers and practitioners interested in heuristics.

A categorized bibliographic survey of goal programming

A total of 240 goal programming articles that have so far appeared in over 60 English journal publications are compiled and classified according to technique and application areas. Analysis of these data provides interesting insights regarding goal programming article characteristics, literature trends and future needs.

Competitiveness of nations: A knowledge discovery examination

This paper presents the insights gained from the use of data mining and multivariate statistical techniques to identify important factors associated with a countrys competitiveness and the development of knowledge discovery in databases (KDD) models to predict it. In addition to stepwise regression and weighted non-linear programming techniques, intelligent learning techniques (artificial neural networks), and inferential techniques (classification and regression trees), were applied to a dataset of 43 countries from the World Competitiveness Yearbook (WCY). The dataset included 55 variables on economic, internationalization, governmental, financial, infrastructure, management, science and technology, as well as demographic and cultural characteristics. Exploratory data analysis and parameter calibration of the intelligent method architectures preceded the development and evaluation of reasonably accurate models (mean absolute error <5.5%), and subsequent out-of-sample validations. The strengths and weaknesses of each of the KDD techniques were assessed, along with their relative performance and the primary input variables influencing a countrys competitiveness. Our analysis reveals that the primary drivers of competitiveness are lower country risk rating and higher computer usage, in entrepreneurial urbanized societies with less male dominance and basic infrastructure, with higher gross domestic investment, savings and private consumption, more imports of goods and services than exports, increased purchase power parity GDP, larger and more productive but not less expensive labor force, and higher R&D expenditures. Without diminishing the role and importance of WCY reports, our approach can be useful to estimate the competitiveness of many countries not included in WCY, while our findings may benefit policy makers and international agencies to expand their own abilities, insights and establish priorities for improving country competitiveness.

Multicriteria Preference Disaggregation for Classification Problems with an Application to Global Investing Risk

Mathematical programming and multicriteria approaches to classification and discrimination are reviewed, with an emphasis on preference disaggregation. The latter include the UTADIS family and a new method, Multigroup Hierarchical DIScrimination (MHDIS). They are used to assess investing risk in 51 countries that have stock exchanges, according to 27 criteria. These criteria include quantitative and qualitative measures of market risk (volatility and currency fluctuations); range of investment opportunities; quantity and quality on market information; investor protection (security regulations treatment of minority shareholders); and administrative “headaches” (custody, settlement, and taxes). The model parameters are determined so that the results best match the risk level assigned to those countries by experienced international investment managers commissioned by The Wall Street Journal. Among the six evaluation models developed, one (MHDIS) classifies correctly all countries into the appropriate groups. Thus, this model is able to reproduce consistently the evaluation of the expert investment analysts. The most significant criteria and their weights for assessing global risk investing are also presented, along with their marginal utilities, leading to identifiers of risk groups and global utilities portraying the strength of each countrys risk classification. The same method, MHDIS, outperformed the other five methods in a 10-fold validation experiment. These results are promising for the study of emerging new markets in fast-growing regions, which present fertile areas for investment growth but also