A Consistency Test for AHP Decision Makers

Saaty's analytic hierarchy process assessed attribute importance by soliciting decision makers' (DM) importance ratios to compute weights. Saaty suggested a decision rule for accepting DM judgments based on a consistency measure derived from the DM's importance ratios. This paper investigates the distribution of random inconsistency and decision rule implications. Stricter consistency requirements for three- and four-attribute criteria matrices are suggested.     

The Effects of an Interactive Graphics-Based DSS to Support Problem Structuring

Graphical, interactive, problem-structuring tools were devised and incorporated into a DSS. These tools were based on concepts of structural modeling and motivated by propositions and findings in memory theory and imagery theory. A laboratory study was then conducted to investigate the relationship between decision-maker characteristics and use of the problem-structuring aid. The experiment was designed to determine whether use of the tool to assist in problem formulation led to a better understanding of problem structure. A moderately complex management simulation and its companion DSS were used in the experiment. Eighty-four subjects were divided into two groups: a control group that did not use the Graphical Interactive Structural Modeling Option (GISMO), and an experimental group that did. The results showed a statistically significant effect between cognitive ability and use of GISMO. Field independents (i.e., high cognitive ability) effectively combined GISMO use with image processing skills to understand the structure of the problem domain, but field dependents (i.e., low cognitive ability) were ineffective. There was very little difference in problem understanding in field dependents who used the tool and those who did not.     

A Sequential Selection Problem*

In this study we formulate a sequential selection problem. In a setting where a choice sequence among candidates is established for filling a job position, the analysis explicitly takes into account the benefits from the hiring, the risk of rejection of the job offer, and the costs due to delays in filling the position. The proposed solution, which is both intuitive and simple, is able to capture analytically the decision process. We also illustrate the versatility of the analysis by considering several other relevant sequential selection settings.     

Contractor Accreditation: A Probabilistic Model

The selection of competent contractors is a critical function in all business organizations. In contrast to other types of vendors (e.g., distributors, manufacturers, etc.), contractors are typically accredited before any business transaction takes place. In such situations, there is often a considerable amount of uncertainty associated with the accreditation process. This research presents a probabilistic model for accrediting contractors. We discuss a methodology in which probability measures are used to capture the uncertainty inherent in the decision process. These probabilities are estimated from data on (i) past applicants and (ii) their eventual performance, if accredited. Furthermore, these probabilities are used to determine when additional information about an applicant should be collected, as well as what kind of information would be most relevant for the vendor under consideration.     

A MATHEMATICAL PROGRAMMING APPROACH TO SENSITIVITY ANALYSIS IN SINGLE-STAGE DECISION MAKING

This paper presents sensitivity analyses for a single-stage decision problem where an action which will maximize the expected payoff must be chosen from a finite number of actions given the states of nature, their probabilities, and the payoffs corresponding to each action and state of nature. Three types of sensitivity analysis are developed: (1) sensitivity analysis on the probabilities keeping the payoff numbers fixed, (2) sensitivity analysis on the payoffs keeping the probabilities fixed, and (3) joint sensitivity analysis on the payoffs and the probabilities. The approach is illustrated with an example. Quite often the sensitivity analysis can be conducted by solving an appropriate linear or quadratic programming problem.     

MAXIMIZING THE PROBABILITY OF ACHIEVING A TARGET PROFIT IN A TWO-PRODUCT NEWSBOY PROBLEM*

This paper explores the two-product newsboy problem. Solution procedures are developed to find the optimal production quantities of each product that will maximize the probability of achieving a profit target. The problem is shown to be surprisingly challenging, and numerical results obtained for only the more restrictive cases exhibit interesting behavior with important decision implications. For example, the results suggest that, if a firm has two single-product divisions and each will receive a bonus for achieving a profit target, it is beneficial for the two divisions to cooperate if the targets are lax and the profit margins are high, but not if the targets are tight and the profit margins low. Also, between two products having different profit margins, one should produce more of the product with the lower profit margin if the target is sufficiently lax. This exploration motivates further efforts in solving the two-product problem for the more general cases and also in extending the problem to three or more products.     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

MULTIPLE-CRITERIA DECISION MAKING INCLUDING QUALITATIVE FACTORS: THE POST-MODEL ANALYSIS APPROACH

As a method of solving multiple-criteria decision making problems with a single quantitative objective and multiple qualitative objectives, the post-model analysis (PMA) approach is proposed. The essence of PMA is to support the trade-offs between a quantitative objective and multiple qualitative objectives so that the decision maker can find a perceived most preferred nondominated solution. To this end, the optimal solution of a quantitative model is found first, without regard for qualitative factors. The solution is then evaluated in terms of qualitative objectives. When the initial quantitatively optimal solution is adjusted to allow improvement of qualitative goals, opportunity costs of achieving qualitative goals are incurred. In this process, an expert system and/or graphical display can be used. PMA therefore provides a way to incorporate quantitative models into knowledge-based expert systems.     

Artificial Intelligence and Generalized Qualitative-Response Models: An Empirical Test on Two Audit Decision-Making Domains

Machine learning methods are currently the object of considerable study by the artificial intelligence community. Research on machine learning carries implications for decision making in that it seeks computational methods that mimic input-output behaviors found in classes of decision-making examples. At the same time, research in statistics and econometrics has resulted in the development of qualitative-response models that can be applied to the same kind of problems addressed by machine-learning models—particularly those that involve a classification decision. This paper presents the theoretical structure of a generalized qualitative-response model and compares its performance to two seminal machine-learning models in two problem domains associated with audit decision making. The results suggest that the generalized qualitative-response model may be a useful alternative for certain problem domains.     

A Note on Consistency in Utility Assessment*

A methodology for determining a von Neumann-Morgenstern utility function is outlined based on the axioms crucial to such a function. Reconciliation of inconsistent judgments is facilitated using the theory of reciprocal matrices. Numerical measures of the collective divergence of a set of judgments from perfect consistency or coherency are provided.     

Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

Composite Forecasting with Dirichlet Priors*

In this paper, composite forecasting is considered from a Bayesian perspective. A forecast user combines two or more forecasts of an operationally relevant random variable. We consider the case where outperformance is modeled as a realization from a multinomial process. The user has prior beliefs about the probability that a particular method outperforms all others, information which is summarized by the Dirichlet distribution. An empirical example with hog prices in the United States illustrates the method.     

Production System Development for Expert Systems Using a Recursive Partitioning Induction Approach: An Application to Mortgage,Commercial, and Consumer Lending

The concepts of expert systems and decision support systems have received considerable attention recently. While systems have been proposed for various problem areas in business, difficulties still exist in the knowledge acquisition phase of development. This paper presents a recursive partitioning analysis (RPA) approach to knowledge acquisition. The RPA production system approach was applied to data sets representing the mortgage, commercial, and consumer lending problems. Comparison of the classification rates across these problems to the results of a generalized inductive inference production system (Quinlan's ID3 algorithm) and across the mortgage and commercial lending problems to traditional statistical modeling approaches indicated that the RPA approach provided superior results while using fewer variables.     

Mexican and Swedish Managers' Perceptions of the Impact of EIS on Organizational Intelligence,Decision Making,and Structure*

Although information technologies in business organizations around the world may be very similar, the meanings conveyed through the technologies may be dependent on managerial values and national culture. Cultural differences need to be understood before information technology developed for organizations in one country can be effectively implemented in organizations in another country. Drawing on survey responses from managers using Executive Information Systems (EIS) across many organizations in Mexico, Sweden, and the United States, the current study examined whether cultural differences influence perceptions of the relationship between Executive Information Systems' use and various outcomes related to decision-making behaviors and processes. The study found significant differences, predicted by cultural factors, in the impact of EIS use on senior management decision making. The findings confirm the notion that IT is used by executives to reinforce the decision-making behaviors valued in their culture.     

The Development Manager's Advisory System: A Knowledge-Based DSS Tool for Project Assessment*

This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

Feedback in DSS as a Source of Control: Experiments with the Timing of Feedback

Well-designed feedback can improve decision making but to date there has been no comprehensive study of feedback in decision support systems (DSS) that could guide developers in its design. This work examines the opportunities and means to enhance cognitive control in decision making by providing appropriate feedback with DSS. It concentrates on the timing of feedback, which has been shown to affect the use of feedback and also demonstrates the potential advantages of using information technology over and above manual decision environments. Two experiments test the effects of controlling the timing of cognitive feedback on the user's cognitive control. The first tests the effect of timing one source of cognitive feedback, and the second tests the effect of timing two sources of feedback. The findings suggest that the design of feedback should become a routine part of the development of DSS.