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.     

Domain knowledge integration in data mining using decision tables: case studies in churn prediction

Companies' interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for churn prediction, viz. through the evaluation of coefficient signs in a logistic regression model, and secondly, by analysing a decision table (DT) extracted from a decision tree or rule-based classifier. An algorithm to check DTs for violations of monotonicity constraints is presented, which involves the repeated application of condition reordering and table contraction to detect counter-intuitive patterns. Both approaches are applied to two telecom data sets to empirically demonstrate how domain knowledge can be used to ensure the interpretability of the resulting models.     

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.     

Influence of model management systems on decision making: empirical evidence and implications

Model management (MM) regards decision models as an important organisational resource deserving prudent management. Despite the remarkable volume of model management literature compiled over the past twenty-odd years, very little is known about how decision makers actually benefit from employing model management systems (MMS). In this paper, we report findings from an experiment designed to verify the idea that the adequacy of modeling support provided by a MMS influences the decision maker's problem solving performance and behaviour. We show that the decision makers who receive adequate modelling support from MMS outperform those without such support. Also, we provide empirical evidence that the MMS help turn the decision makers' perception of problem solving from a number crunching task into development of solution strategies, consequently changing their decision making behaviour.     

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.     

The relationship between decision support systems and operational research: Health care examples

This paper explores, via selected case studies, the relationship between DSS and OR in the health field. Although I believe that DSS offers nothing really new, as described by various writers the term does emphasise particular features which should be desirable goals in any OR study. These features are: an emphasis on flexibility and adaptability, on ease of use and understanding by non-computer people and on models linked with traditional data access. The paper discusses such features and their desirability, in relation to hospital based planning/management on the one hand and community based planning on the other.     

Operational knowledge management: identification of knowledge objects,operation methods,and goals and means for the support function

Though much has been written about knowledge management, this field has not been described extensively from an operational management perspective. Consequently, knowledge management seems difficult to implement at the operational levels of the organisations. To solve this problem, the abstract notion of knowledge is translated in operational knowledge objects. These objects are the input and output of two operation methods: (1) transformation or learning; and (2) knowledge logistics. The article describes several activities of these operation methods, and gives a classification of operational goals and means for the operations support function. The author concludes with mentioning challenges for the field of operational knowledge management.     

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.     

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.     

Wisdom,decision support and paradigms of decision making

Many decision support tools have been developed over the last 20 years and, in general, they support what Simon termed substantive rationality. However, such tools are rarely suited to helping people tackle wicked problems, for which a form of procedural rationality is better suited. Procedurally rational approaches have appeared in both management science and computer science, examples being the soft OR approach of cognitive mapping and the design rationale based on IBIS. These approaches are reviewed and the development of Wisdom, a procedurally rational decision support process and accompanying tool, is discussed and evaluated.     

MCDA and preference disaggregation in group decision support systems

Within the frame of decision aid literature, group decision making has drawn the attention of researchers from a wide spectrum of disciplines. Group Decision Support Systems (GDSS) can play a critical role, in decision situations with multiple individuals, each having his/her own private point of view on the handling of the decision problem. In such an environment, the conflict between the members of the group is not a seldom situation. Multiple criteria decision aid (MCDA) methods can be proven as invaluable tools in handling such interpersonal conflicts where the aim is to achieve consensus between the group members or at least reduce the amount of conflict among participating individuals. This paper reviews some of the past approaches in the multiple criteria–multiple decision makers context.     

An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring

Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented.     

HADTS: A decision technology system to support Army housing management

The Department of Army must provide its personnel with acceptable housing at minimum cost within the vicinity of military installations. To achieve these housing objectives, the Army often must enter into agreements for the longterm construction of onpost housing or the leasing of existing offpost housing. A decision support system, called HANS, has been developed to project the necessary construction or leasing.HANS had some gaps in supporting the construction and leasing decisions. This paper describes the gaps and shows how a decision technology system, called the Housing Analysis Decision Technology System (HADTS), can help Army managers to overcome the support gaps. It also overviews HADTS's benefits, challenges, and limitations.     

Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.     

Decision support systems for water management: The Lake Como case study

We review an application-oriented study on the management of Lake Como, a natural multipurpose reservoir in Northern Italy. The emphasis is on the Decision Support System that resulted from this study and which is now being used by the manager to take his decisions on the amount of water to be released each day from the lake. The decision support system is based on the optimal solutions of complex multiobjective mathematical programming problems.     

Learning management knowledge for manufacturing systems in the early stages using time series data

Acquiring knowledge in manufacturing systems in the early stages always has a challenging task due to the lack of sufficient data. This makes it hard for the derived management model to reach a reliable and stable level. Li and Lin (2006) developed a useful method that can deal with the problem of knowledge acquisition based on a small data set. However, this method assumes all data are collected at the same time, since they treat the data set as a source (from one population) of a priori knowledge for learning. In fact, instead of being a random data set, these collected data can be time dependent, that is, they tend to be a sequence of observations, occurring at different times. The consideration of this dependence property in the data will benefit the knowledge acquisition in the early stages by expanding the learning model from an independent model to a dependent model. This research expanded the intervalized kernel density estimator (IKDE) presented in Li and Lin (2006) to a more general form to improve the learning model in the early stages. The general model, called GIKDE, joints the concepts of time series and stochastic processes in order to deal with both independent and dependent data sets. The Virtual Sample Generation process based on GIKDE was also developed to produce extra information for expediting the learning. Results obtained from the application of the model to a control charts data, using a back-propagation neural network as the learning tool, show that this unique approach is an effective method of knowledge acquisition for a manufacturing system in the early stages.     

Codifying or enabling: the challenge of knowledge management systems

This paper examines the complex interaction between technology and people in the knowledge-making and knowledge-management activities of organisations. Studies in two Australian universities are used to demonstrate the importance of adopting a holistic approach to knowledge management, which is cultural, historical, contextual and dynamic. The paper presents various perceptions of what knowledge management is, or should be, with the suggestion that the cultural historical activity theory be used as the theoretical framework for both the study of knowledge management and the design of knowledge management systems, which enable organisational learning and adaptation.