The impact of problem size on decision processes: an experimental investigation on very large choice problems with support of decision support systems

Abstract: Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision‐making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer‐aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems.     

Applying a belief rule-base inference methodology to a guideline-based clinical decision support system

Abstract: A critical issue in the clinical decision support system (CDSS) research area is how to represent and reason with both uncertain medical domain knowledge and clinical symptoms to arrive at accurate conclusions. Although a number of methods and tools have been developed in the past two decades for modelling clinical guidelines, few of those modelling methods have capabilities of handling the uncertainties that exist in almost every stage of a clinical decision-making process. This paper describes how to apply a recently developed generic rule-base inference methodology using the evidential reasoning approach (RIMER) to model clinical guidelines and the clinical inference process in a CDSS. In RIMER, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness and non-linear causal relationships, while traditional IF–THEN rules can be represented as a special case. Inference in such a rule base is implemented using the evidential reasoning approach which has the capability of handling different types and degrees of uncertainty in both medical domain knowledge and clinical symptoms. A case study demonstrates that employing RIMER in developing a guideline-based CDSS is a valid novel approach.     

Adaptive decision support systems

The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations     

A metalanguage design for sessions with group decision support systems

This paper proposes a metalanguage for representing communication among participants in a session of a group decision support system (GDSS). Such a vehicle can be used for setting standards and designing specific user interfaces with GDSS.Three different roles of participants are identified: chairperson, chauffeur and regular participant. The tasks and activities of each role are analyzed. Based on the analysis, communications requirements are derived. The requirements lead to a design of a metalanguage consisting of 15 different commands. The paper concludes by displaying the structure and syntax of each command.The work described here is conceptual and could be considered as a proposal for GDSS language design.     

Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology

Introduction

Maintaining a large diagnostic knowledge base (KB) is a demanding task for any person or organization. Future clinical decision support system (CDSS) may rely on multiple, smaller and more focused KBs developed and maintained at different locations that work together seamlessly. A cross-domain inference tool has great clinical import and utility.

Methods

We developed a modified multi-membership Bayes formulation to facilitate the cross-domain probabilistic inferencing among KBs with overlapping diseases. Two KBs developed for evaluation were non-infectious generalized blistering diseases (GBD) and autoimmune diseases (AID). After the KBs were finalized, they were evaluated separately for validity.

Result

Ten cases from medical journal case reports were used to evaluate this “cross-domain” inference across the two KBs. The resultant non-error rate (NER) was 90%, and the average of probabilities assigned to the correct diagnosis (AVP) was 64.8% for cross-domain consultations.

Conclusion

A novel formulation is now available to deal with problems occurring in a clinical diagnostic decision support system with multi-domain KBs. The utilization of this formulation will help in the development of more integrated KBs with greater focused knowledge domains.     

Multiple criteria decision making and decision support systems — Guest editor's introduction

Integration of MCDM with DSS brings benefit to both fields. MCDM tools are useful in identifying and evaluating incompatible alternatives for DSS, while DSS can implement MCDM approaches and help maintain and retrieve MCDM models. Over the years, MCDM has made considerable contribution to the development of various DSS subspecialties. This special issue on Multiple Criteria Decision Making and Decision Support Systems consists of 9 selected papers from the 20th International Conference on Multiple Criteria Decision Making. The guest editors highlight the key ideas and contributions of the papers in the special issue.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

RCM模型决策系统设计

对RCM模型决策系统进行了需求分析,确定了进行模型辅助RCM决策所需的信息,数据处理方式以及如何利用模型辅助决策。所需的信息包括故障信息、维修信息和费用信息等,数据处理主要是对原始故障及维修数据的统计分析,模型主要用于计算最佳工作间隔期和评估工作组合。设计了RCM模型决策系统框架结构及详细流程,改变了以前RCM分析的工作方式,实现了主客观相结合,加强了数据审核,为RCM分析质量提供了保证。提出了系统实现的关键技术,给出了可供参考的实现方法。     

Expert systems for decision support in EDP auditing

The complexities of computer auditing have created a need for decision support for the EDP auditor. Traditional statistical techniques have proven valuable; however, there are important qualitative components which must be incorporated in the analysis. More importantly, there is a need for decision aids which not only produce analysis and probability estimates-but are able to explain their analysis and conclusions. Recent developments in artificial intelligence have made possible the development of expert systems which provide these capabilities. In this paper we present the motivation, framework, and development strategy for a decision support system for EDP auditing.This project was funded by a grant from the Peat, Marwick, Mitchell Foundation through its Research Opportunities in Auditing program. The views expressed herein are those of the authors and do not necessarily reflect the view of the Peat, Marwick, Mitchell Foundation. The authors are indebted to the participants of the workshops at the University of Florida and the University of Wisconsin for their helpful comments. In particular, we would like to thank Rowland Ataise, Alan Friedberg, Steve Johnson, Andy Luzi, Stan Biggs, Lynn McKell, and Marshall Romney.     

MATLAB在基于Web的决策支持系统中的应用研究*

探讨了MATLAB的应用程序接口原理,对B/S模式的两种应用程序接口方式进行了比较和分析。对于现有文献尚未涉及的COM组件接口方式,结合一个区域经济决策支持系统实例给出了具体的应用流程。     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

Field evaluation of a prototype laser safety decision support system

A field evaluation of a decision support system prototype is described. The system is designed to aid the decision making of laser safety hazard assessors and laser manufacturers. The aims of the evaluation were to establish the usefulness and usability of the system, and to indicate where design changes might be needed. Three complementary methods namely observation evaluation, expert evaluation, and survey evaluation were used. Fifteen laser safety hazard assessors took part in the evaluation as subjects. Objective and subjective data were analysed and areas of user difficulty with the system were identified. The system was well-received although some pointers to modification for the eventual delivery system were identified. It is concluded that the aims of the evaluation were successfully met.     

Bayesian inference in disputed authorship: A case study of cognitive errors and a new system for decision support

Bayesian inference provides a formal framework for assessing the odds of hypotheses in light of evidence. This makes Bayesian inference applicable to a wide range of diagnostic challenges in the field of chance discovery, including the problem of disputed authorship that arises in electronic commerce, counter-terrorism and other forensic applications. For example, when two documents are so similar that one is likely to be a hoax written from the other, the question is: Which document is most likely the source and which document is most likely the hoax? Here I review a Bayesian study of disputed authorship performed by a biblical scholar, and I show that the scholar makes critical errors with respect to several issues, namely: Causal Basis, Likelihood Judgment and Conditional Dependency. The scholar’s errors are important because they have a large effect on his conclusions and because similar errors often occur when people, both experts and novices, are faced with the challenges of Bayesian inference. As a practical solution, I introduce a graphical system designed to help prevent the observed errors. I discuss how this decision support system applies more generally to any problem of Bayesian inference, and how it differs from the graphical models of Bayesian Networks.     

企业集团分布式数据仓库及决策支持系统研究

为了构建支持企业集团决策分析的信息系统,分析了企业集团决策支持系统信息模型,给出了面向整个集团的基于分布式数据仓库的企业集团决策支持系统的体系结构;通过定义决策方案模型及决策任务模型,提出了模型驱动的企业集团决策支持系统解决方案,并给出了决策任务模型并行执行算法。应用表明,该体系结构是可行且有效的。     

Fuzzy cognitive map architectures for medical decision support systems

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

Patterns for context-based knowledge fusion in decision support systems

The here presented research focuses on the context-based knowledge fusion patterns. Patterns are discovered based on an analysis and investigation of knowledge fusion processes in a context aware decision support system at the operational stage of the system functioning. At this stage the context-based knowledge fusion processes are manifested around the context. The patterns are generalized in regard to the following three aspects: (1) the effects that the knowledge fusion processes produce in the system; (2) the preservation of internal structures for the context and multiple sources the information/knowledge is fused from; and (3) the preservation of multiple sources and the context autonomies. At that, seven knowledge fusion patterns have been discovered: simple fusion, extension, instantiated fusion, configured fusion, adaptation, flat fusion, and historical fusion.     

不确定性的综合描述及其智能决策支持系统

为了克服单一模型描述的不确定的片面性,提出了描述不确定性的综合描述模型,并给出了在智能决策支持系统中的应用。首先在不确定性内涵的阐释的基础上,建立了不确定性的综合描述模型。然后给出了基于不确定性的综合描述模型的不确定知识表示和推理方法。最后给出了基于综合描述模型的智能决策支持系统的体系结构。不确定性的综合描述模型具有全面客观描述不确定性的优点。     

Recommendation systems for decision support: An editorial introduction

This special section is focused on recommendation systems, computer-based information systems that provide suggestions based on previous usage behavior. The papers included in this section were selected from the Pacific Asia Conference on Information Systems (PACIS) and Workshop on Electronic Business (WEB). The application domains include website browsing aids, knowledge recommendation, document clustering, and team staff selection. They provide a snapshot of current development in using recommendation systems for decision support.     

基于决策支持度的决策树生成算法

从条件属性对决策支持程度不同的角度出发,引入了决策支持度的概念,提出了一种以其为启发式信息的决策树生成算法。实验分析表明,相对于传统的决策树生成算法,此算法改善了决策树的结构,有效提高了决策分类的精度。