A preference disaggregation decision support system for financial classification problems

Classification is one of the most extensively studied problems in the fields of multivariate statistical analysis, operations research and artificial intelligence. Decisions involving a classification of the alternative solutions are of major interest in finance, since several financial decision problems are best studied by classifying a set of alternative solutions (firms, loan applications, investment projects, etc.) in predefined classes. This paper proposes an alternative approach to the classical statistical methodologies that have been extensively used for the study of financial classification problems. The proposed methodology combines the preference disaggregation approach (a multicriteria decision aid method) with decision support systems. More specifically, the FINancial CLASsification (FINCLAS) multicriteria decision support system is presented. The system incorporates a plethora of financial modeling tools, along with powerful preference disaggregation methods that lead to the development of additive utility models for the classification of the considered alternatives into predefined classes. An application in credit granting is used to illustrate the capabilities of the system.     

Regularized estimation for preference disaggregation in multiple criteria decision making

Disaggregation methods have been extensively used in multiple criteria decision making to infer preferential information from reference examples, using linear programming techniques. This paper proposes simple extensions of existing formulations, based on the concept of regularization which has been introduced within the context of the statistical learning theory. The properties of the resulting new formulations are analyzed for both ranking and classification problems and experimental results are presented demonstrating the improved performance of the proposed formulations over the ones traditionally used in preference disaggregation analysis.     

Preference disaggregation: 20 years of MCDA experience

The philosophy of preference disaggregation in multicriteria decision-aid systems (MCDA) is to assess/infer global preference models from the given preferential structures and to address decision-aiding activities. This paper presents a panorama of preference disaggregation methods and summarises the most important results and applications over the last 20 years.     

Fuzzy preference orderings in group decision making

In this paper, some use of fuzzy preference orderings in group decision making is discussed. First, fuzzy preference orderings are defined as fuzzy binary relations satisfying reciprocity and max-min transitivity. Then, particularly in the case where individual preferences are represented by utility functions (utility values), group fuzzy preference orderings of which fuzziness is caused by differences or diversity of individual opinions are defined. Those orderings might be useful for proceeding the group decision making process smoothly, in the same manner as the extended contributive rule method.     

The problem of highly constrained tasks in group decision support systems

Most experimental uses of group decision support systems (GDSS) are associated with relatively unrestricted domains, for example, idea generation and preference specification, where few restrictions on potential solutions exist. However, an important GDSS task is that of resource allocation across functional areas of the organization, including supply chain applications. These types of tasks, such as budget planning and production planning, are typically highly constrained and difficult to solve optimally, necessitating the use of decision aids, such as those found in GDSS.We use a model based on adaptive search of a genetic algorithm as the analogy for the group decision making process. We apply this model to experimental data gathered from GDSS groups solving a production planning task. The results indicate very low estimated crossover rates in the experimental data. We also run computational experiments based on adaptive search to mimic the GDSS data and find that the low estimated crossover rate might be due to the highly constrained search space explored by the decision making groups. The results suggest further investigation into the presumed beneficial effects of group interaction in such highly constrained task domains, as it appears very little true information exchange occurs between group members in such an environment. Furthermore, the simulation technique can be used to help predict certain GDSS behaviors, thus improving the entire GDSS process.     

Composite alternatives in group decision support

Decision theory models of group decision processes usually assume a given set of alternatives, from which the group has to choose. In realistic group decision situations, however, alternatives are often not specified a priori, but are created during the group process from different components introduced by the group members. This paper develops methods for systematically creating such composite alternatives, also taking into account the necessity to keep both the computational effort and the cognitive load to group members within reasonable limits.Paper presented at the International Conference on Support Systems for Decision and Negotiation Processes DNS-92, Warsaw, 1992.     

Preference disaggregation and statistical learning for multicriteria decision support: A review

Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields.     

User acceptance of multi-criteria decision support systems: The impact of preference elicitation techniques

Previous research indicates that decision makers are often reluctant to use potentially beneficial multi-criteria decision support systems (MCDSS). Prior research has not examined the specific impact of preference elicitation techniques on user acceptance of MCDSS. The present research begins to fill this gap by examining the effect on users’ MCDSS evaluations of two commonly used preference elicitation techniques, absolute measurement and pairwise comparisons, while holding constant all other aspects of the MCDSS and decision making task. Experimental results (N = 153) indicate that users consider MCDSS with pairwise comparisons to be higher in decisional conflict, more effortful, less accurate, and overall less desirable to use than MCDSS with absolute measurements. Thus, any potential normative superiority of a preference elicitation technique must be balanced against its potentially adverse effects on user acceptance of the MCDSS within which it is employed. We present a research agenda for exploring the tradeoffs between objective validity and user acceptance in the design of decision analysis tools.     

A framework for group decision support systems: Combining AI tools and OR techniques

Work on the implementation of Group Decision Support Systems has to exploit recent advancements of computer science. Existing frameworks for single-user Decision Support Systems, based on well-established Operations Research methods such as Multicriteria Decision Making techniques, have to be integrated with successful technical developments in electronic communication and computing. Starting from the presentation of the related Operations Research background, this paper proceeds by discussing challenges coming from the areas of Computer-Supported Cooperative Work and Information Systems on the World Wide Web platform. Based on this discussion, a framework for an ‘open’, computer-mediated Group Decision Support System is proposed. The term ‘open’ is related to a platform-independent system, which can efficiently support alternative types of goals and control protocols between its users.     

Human centered processes and decision support systems

This paper emphasizes the role of human factors in Decision Support Systems and related assisting tools that can be used in the Operational Research field. It links both historical information and real life realizations concerning the human centered processes. The historical points mentioned in the paper give only partial emphasis, according to the feeling of the authors. The aim, here, is essentially to review some tools (e.g., utility theory, cognitive modeling, etc.) that are or might be used to tackle new problems in the context of anthropocentered systems, especially when considering the recent evolution of Information Systems towards distributed ones. Several real-life problems (mostly in an industrial setting) are reviewed. They all concern applications on which the authors have worked (or are working) together.     

Expert systems approach to decision support

An approach to building decision support systems based on expert systems methods is proposed. The relatively weak basic assumptions include only stationarity (repeatability) of the decision in the same environment (circumstances) and ordering of the values of all the attributes with regard to the decision maker's preferences. The proposed approach is aimed at reflecting the experienced domain expert's and decision maker's knowledge and preferences, both in the form of facts and rasoning rules. Among the purposes of the described class of decision support systems there are the storage and retrieval of the expert's knowledge and decisions, decision making support and ranking of admissible decision alternatives. A general model of the decision process is proposed and a language for representation of the expert's knowledge is introduced in brief. The structure, reasoning control, and an example of application of the proposed system are discussed and possible further extensions are pointed out.     

Multicriteria decision support using rules that represent rough-graded preference relations

The approach described in this paper aims to support multicriteria choice and ranking of actions when the input preference information acquired from the decision maker is a graded comprehensive pairwise comparison (or ranking) of reference actions. It is based on decision-rule preference model induced from a rough approximation of the graded comprehensive preference relation among the reference actions. The set of decision rules applied to a new set of actions provides a graded fuzzy preference relation, which can be exploited by weighted-fuzzy net flow score or lexicographic-fuzzy net flow score procedure to obtain a final recommendation in terms of the best choice or of the ranking.     

A practical prioritization by multi-level group decision support

Military capability is proposed to be defined according to the DYNPOT scoring method. Multiobjective resource allocation of shared resources by group decision-making can combine analytic and qualitative modeling. Recently it has been pointed out that the goal programming model is superior to other models though it remained to be answered how to take into account hierarchy of decision makers (and objectives) (Stummer and Vetschera in Cent Eur J Oper Res 11:3–260, 2003). In this article it is tried to present, that the quantitative model can be easily adapted to the qualitative STT/QFD model of objectives of top-level group of decision-makers. The subsequent phases of the qualitative and the analytic solution of a multiobjective cooperative resource allocation problem can be applied within the group decision-making framework of defence requirements capability-based planning.     

Computerized support systems for emergency decision making

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.This work is partly supported by the State Science and Technology Commission of China, the National Key Laboratory on Industrial Control of China and Fok Ying Tung Education Foundation.     

Organizational decision support systems: Centralized support for decentralized organizations

Organizations are becoming increasingly decentralized in their operations and decision making. Advanced information technologies provide the glue that holds such organizations together and facilitates their operations. Organizational decision support systems (ODSSs) provide mechanisms for ensuring that the decisions being made throughout such organizations are consistent with each other and with the overall organization goals. Through means of an ODSS, information and guidance is automatically passed from higher levels to lower levels for use in decision-making models.     

A goal programming approach to group decision making based on multiplicative preference relations and fuzzy preference relations

This paper analyses the mechanisms through which binding finance constraints can induce debt-constrained firms to improve technical efficiency to guarantee positive profits. This hypothesis is tested on a sample of firms belonging to the Italian manufacturing. Technical efficiency scores are computed by estimating parametric production frontiers using the one stage approach as in Battese and Coelli [Battese, G., Coelli, T., 1995. A model for technical efficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20, 325–332]. The results support the hypothesis that a restriction in the availability of financial resources can affect positively efficiency.     

A compromise weight for multi-criteria group decision making with individual preference

In a multi-criteria group decision making process, it is often hard to obtain a solution due to the possible conflict preferences from different participants and the undeterministic weights assigned to each criterion. This problem can be defined as to identify a set of weights for the given criteria to achieve a compromise of the conflict on different preferences. When such a compromise weight does not exist, we need to adjust (to reverse or to withdraw) some or all of the preferences from different participants. This paper describes a minimax principle based procedure of preference adjustments with a finite number of steps to find the compromise weight. At each iteration, we either find the weight or identify some ‘wrong’ preferences. We also define a consistency index for each participant to measure the distance between the individuals' preference and the final group decision. Corresponding theoretical work is referred to in support of the procedure, and numerical examples are provided for illustration. This study is further extended to the case of multiple assessments.     

A fuzzy multiobjective programming algorithm in decision support systems

Multicriteria analysis is one of the analytical functions in the problem processing system of decision support systems (DSS). In this paper, an interactive and iterative fuzzy programming method for solving a quasi-optimization problem in complex decisions under constraints involving a multiple objective function is proposed. Comparing with an adapted gradient search method, a surrogate worth tradeoff method, and a Zionts—Wallenius method, an approximate preference structure is emphasized in the proposed method.     

User-model interaction in decision support systems for risk management

This paper describes and evaluates three different approaches to building decision support systems: the Operations Research/Management Science approach, the Decision Analysis/Multiattribute Utility approach, and the Artificial Intelligence/Expert Systems approach. It evaluates the usefulness of the three approaches for risk management. In particular, it defines evaluation objectives of risk analysts, risk managers, and laypeople and provides a subjective assessment how the three approaches stack up against their objectives. The paper concludes that for most risk management applications a combination of the three approaches would be most desirable.This paper was written under contract No. 2709-85-05 ED ISP D of the European Atomic Energy Community, Commission of the European Communities, Joint Research Centre, Ispra Establishment, Ispra, Italy to the Gemeinschaft für Entscheidungs- und Risikoanalyse, Berlin, West Germany. It was prepared for presentation at the Conference on Operations Research and Multiattribute Decision Analysis held in Passau, April 20–26, 1986. The views and opinions expressed in this paper are solely those of the author.