Alexander I. Mechitov

Ordinal judgments in multiattribute decision analysis

Abstract The article discusses the contradiction between the ambiguity of human judgment in a multicriterion environment and the exactness of the assessments required in the majority of the decision-making methods. Preferential information from the decision makers in the ordinal form (e.g., “more preferable”, “less preferable”, etc.) is argued to be more stable and more reliable than cardinal input. Ways of obtaining and using ordinal judgments for rank ordering of multiattribute alternatives are discussed. The effectiveness of the step-wise procedure of using ordinal tradeoffs for comparison of alternatives is evaluated. We introduce the notion of ordinal tradeoffs, presentation of ordinal tradeoffs as a flexible three-stage process, a paired joint ordinal scale (PJOS), and evaluation of the effectiveness of the three-stage process. Simulation results examine the sensitivity of the number of pairwise comparisons required for given numbers of criteria and categories within criteria, as well as the number of alternatives analyzed. This simulation shows that ordinal pairwise comparisons provide sufficient power to discriminate between 75% and 80% of the alternatives compared. While the proportional number of pairwise comparisons relative to the maximum possible decreases with the number of criteria and categories, the method is relatively insensitive to the number of alternatives considered.

Rule induction in data mining: effect of ordinal scales

Abstract Many classification tasks can be viewed as ordinal. Use of numeric information usually provides possibilities for more powerful analysis than ordinal data. On the other hand, ordinal data allows more powerful analysis when compared to nominal data. It is therefore important not to overlook knowledge about ordinal dependencies in data sets used in data mining. This paper investigates data mining support available from ordinal data. The effect of considering ordinal dependencies in the data set on the overall results of constructing decision trees and induction rules is illustrated. The degree of improved prediction of ordinal over nominal data is demonstrated. When data was very representative and consistent, use of ordinal information reduced the number of final rules with a lower error rate. Data treatment alternatives are presented to deal with data sets having greater imperfections.

Knowledge acquisition tool for case-based reasoning s ystems

Abstract Knowledge acquisition is an important aspect of intelligent systems. The usefulness of such systems depends on system completeness and consistency. Case-based reasoning is a useful means of matching a natural human mode of dealing with complex problems through systematic recording of experience. However, this approach requires that representative case sets be considered in the knowledge acquisition phase, and once these case sets have been acquired, an efficient means of retrieving them is needed. M-CLASS is an ordinal classification model that gives structure to a knowledge acquisition environment where cases could be the source of knowledge. The M-CLASS system assures consistency and provides a means of checking for completeness. The system is demonstrated with a medical example.

Verbal Decision Analysis: Foundations and Trends

The primary goal of research in multiple criteria decision analysis is to develop tools to help people make more reasonable decisions. In many cases, the development of such tools requires the combination of knowledge derived from such areas as applied mathematics, cognitive psychology, and organizational behavior. Verbal Decision Analysis (VDA) is an example of such a combination. It is based on valid mathematical principles, takes into account peculiarities of human information processing system, and fits the decision process into existing organizational environments. The basic underpinnings of Verbal Decision Analysis are demonstrated by early VDA methods, such as ZAPROS and ORCLASS. New trends in their later modifications are discussed. Published applications of VDA methods are presented to support the findings.

Problems of decision rule elicitation in a classification task

Abstract Intelligent decision support requires knowledge elicitation processes. Two primary approaches for knowledge elicitation in a multiattribute classification task are 1) direct elicitation of decision rules in the form of productions, and 2) classification of multiattribute objects by an expert as a basis for development of the underlying decision rules. This study reports an experiment using a simple classification task, to compare these two forms of knowledge elicitation. Relative consistency and complexity of the resulting rule bases are analyzed. System CLASS was used as a tool for the second approach, as well as a means of analysis for the first approach. It was found that it was easier for subjects to accomplish the task using object classification than it was to formulate production rules directly. High degrees of inconsistency and incomplete rule bases resulted when there was no computer aid for the process of knowledge elicitation.

Choice Behaviour in a Computer-Aided Multiattribute Decision Task

Choice behaviour in an interactive multiple-criteria decision-making environment was examined experimentally. The main purpose was to investigate whether subjects are more comfortable in processing criterion/attribute information simultaneously (in parallel) or sequentially. As a research instrument, three different interactive software systems were used on a microcomputer by management students at the Helsinki School of Economics and Business Administration and the Institute of National Economy in Moscow to solve essentially the same problem of buying/ leasing a home tailored to the respective decision environments. The experiments also provided us with a possibility to learn useful lessons about how human subjects make computer-supported choices. The results of the experiments are discussed. Furthermore, questions for future research are suggested. & 1997 by John Wiley & Sons, Ltd.

The role of rules and examples in the process of knowledge acquisition in direct classification tasks

Abstract Shells provide a means for experts to easily develop expert systems for their area of expertise. However, rule bases need to be complete and free of contradictions. A set of 30 subjects, unfamiliar with shells except for initial orientation and training, were asked to develop a system for their personal preferences for a decision problem. The results of these systems were analyzed, leading to a number of conclusions. First, three types of rules used by the subjects were identified. Cutoff rules reflect preemptive treatment of decision rules. Examples reflect an attempt to enumerate all combinations of decision factors. Compensatory rules reflect attempts to balance trade-offs among the relative performance of decision cases. The implications of using these three types of rules are evaluated. Subjects validated their systems on a test bank of 18 cases. Subject responses to the impact of these test cases were evaluated, revealing that they thought that the test cases yielded more complete systems. Posttest evaluation of their systems for completeness and consistency also revealed that the systems still included significant gaps in rules. We conclude that computer aids to assist experts need to include means to assure consistency and completeness of knowledge bases. Further, at least some compensatory rules should be included for those cases that involve trade-offs.

Comparison of MCDA Paradigms

The underlying concepts of MAUT, SMART, AHP, preference cones, ZAPROS, and outranking methods are compared. Learning systems are considered. The learning view is that decision makers initially do not fully understand all of the criteria that are important. Therefore, rather than uncovering an underlying utility function, what must be uncovered are the full ramifications involved in selecting one alternative over another. This paradigm can involve an evolutionary problem, where criteria can be added or discarded during the analysis. Methods are also reviewed with respect to their psychological validity in generating input data. Past experiments conducted by the authors are reviewed, with conclusions drawn relative to subject comfort in using each method. Subjects typically make errors, in that they have inconsistent ratings of scores across systems, and will occasionally have reversal of relative importance of criteria across systems. This emphasizes the need to be careful of input in decision models, and strengthens the argument for more robust input information. Furthermore, systems based on the same model have been found to yield different results for some. In a study exposing both US and Russian students were compared. Each group found it more comfortable to use systems developed within their own culture.

Cognitive Effort and Learning Features of Decision Aids: Review of Experiments

ABSTRACT Decision aids are computer systems intended to help decision makers select from a set of alternatives when considering multiple criteria. There are a number of basic ideas behind such systems, including multiattribute utility analysis, analytic hierarchy process, and outranking. Some systems focused on identification of decision maker preference functions. Recently there have been a number of systems focusing on the idea of enhancing decision maker learning. This paper reviews the fundamental ideas of a number of decision aids and considers the cognitive effort on the part of decision makers required to use each system. Recent empirical studies from a number of sources are reviewed and evaluated for support of theories about this cognitive effort, as well as how learning systems are implemented.

Russian Business Schools in a Time of Transition.

In this study, the authors reviewed the development of Russian business education in the past decade. This development, fueled by historic changes in Russian society, has affected all aspects of business education, including its organizational structures, demand in different business areas, and mode of teaching. In a short period of time, Russian business education has substantially adjusted to new market requirements and transformed itself into one of the most influential parts of the Russian academic community. However, business education in Russia still has many challenging problems inherited from the Soviet-era planning mentality, which emphasized theory at the expense of practical problem solving.