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.     

基于前景理论的交互式群组风险决策方法

为了克服现有方法因立论假设过于理想而存在决策结果的科学性和满意性较差以及决策过程效率低下等缺陷,基于前景理论构建了能够吸纳多种模糊信息且能从中识别有效前景的优选模型及判别定理,并针对结果价值的交互学习提出了具有收敛性的交互式决策方法。数值模拟分析表明,所提出的方法既能反映群组成员风险偏好,保证决策结果具有科学性、满意性,又能平衡信息完善程度与其获取成本之间的矛盾关系,保证决策过程具有有效性和可行性。     

The use of ordered weighted averaging method for decision making under uncertainty

A decision making under uncertainty (DMUU) prevails at the outset and often evolves into a decision making under partial uncertainty as information on the states of nature, for example, a probability distribution, is advanced. Many methods have emerged for solving the DMUU problems, which includes the classical decision criteria and the domain criterion. Yager (1988) introduced a new approach, the so‐called ordered weighted averaging (OWA) as a viable method for solving the DMUU problems. The OWA weights to be used in the aggregation are generated under the degree of optimism provided by a decision maker and then combined with the reordered payoffs to produce aggregated payoffs for each strategy. The reordering process, one of the characterizing features of the OWA method, enables us to perform various types of aggregations including maximax, maximin, and Hurwicz‐α index in conjunction with the generated weights. The OWA method obviously extends the Hurwicz approach by taking into account the tradeoffs among the entire payoffs while the Hurwicz approach considers a tradeoff only between the two extremes, the maximum and the minimum payoffs. In this paper, we examine the features of the OWA method in light of Milnor's set of requirements for reasonable decision criteria, thus providing a solid methodological foundation for the DMUU. The OWA method can also be used to solve a group DMUU problem by exploiting individual decision results in the situation when the use of a fuzzy majority is advocated.     

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.     

A preference theory approach to decision analysis in resource allocation

This paper studies the allocation of discrete resources among multiple agents from a preference theory perspective. More specifically, the paper explores the process of decision making where:

A multi-criteria decision framework to support measurement-system design for bridge load testing

Due to conservative design models and safe construction practices, infrastructure usually has unknown amounts of reserve capacity that exceed code requirements. Quantification of this reserve capacity has the potential to lead to better asset-management decisions by avoiding unnecessary replacement and by lowering maintenance expenses. However, such quantification is challenging due to systematic uncertainties that are present in typical structural models. Field measurements, collected during load tests, combined with good structural-identification methodologies may improve the accuracy of model predictions. In most structural-identification tasks, engineers usually select and place sensors based on experience and high signal-to-noise estimations. Since the success of structural identification depends on the measurement system, research into measurement system design has been carried out over several decades. Despite the multi-criteria nature of the problem, most researchers have focused only on the information gained by the measurement system. This study presents a framework to evaluate and rank possible measurement-system designs based on a tiered multi-criteria strategy. Performance criteria for the design of measurement systems include monitoring costs, information gain, ability to detect outliers and impact of loss of information in case of sensor failure. Through including conflicting criteria, such as cost of monitoring and information gain, the optimal measuring system becomes a Pareto-like choice that ultimately depends on asset-manager preference hierarchies. Several potential preference scenarios are generated and results are compared using a full-scale test study, the Exeter Bascule Bridge. The framework successfully supports an informed design of measurement systems by providing an extensive set of alternatives, including the best solution defined probabilistically and for specific conditions when other near-optimal solutions might be preferred.     

A framework to support educational decision making in mobile learning

Learning Analytics in Mobile Learning is a challenging research topic, due to the distinguishing features of mobile learning. In fact, mobile learning is characterized by the learners’ mobility, the possibility of having localized data and information, the large amount of data that can be collected during a learning session, the affordances provided by the technologies and the social dynamics that characterize the context in which learning takes place. As a consequence, Learning Analytics in mobile learning requires original methodological approaches which enrich techniques already tested in different learning contexts (e.g., in Virtual Learning Environments) with specific strategies to deal with the complexity of mobile learning and manage the corresponding datasets. This paper presents a task-interaction framework to support educational decision-making in mobile learning. The framework is based on the relationships between the different types of interactions occurring in a mobile learning activity and the tasks which are pedagogically relevant for the learning activity. A case study has been designed to demonstrate the application of the task-interaction framework to learning scenarios based on the use of mobile devices.     

基于扩展原理的混合型证据推理不确定决策方法

提出一种基于扩展原理的混合证据推理不确定决策模型.通过α截集将同一决策问题中各属性使用的精确数、区间数和模糊数等异构评估信度统一分解为区间结构,采用区间证据推理方法求解各隶属度下的效用区间,并按隶属度次序重组方案效用;化简模糊数质心公式,并用于模糊定量评估的信度计算和方案模糊效用的排序;最后,通过具体实例验证了所提出方法的有效性和可行性.将该方法在算例中的适用情况进行比较和分析,结果表明所提出的方法具有良好的适应性.     

A framework for applying ethics-by-design to decision support systems for emergency management

The development and utilisation of new information and communication technologies presents opportunities and risks, which bring ethical issues to the forefront. Any attempt to minimise the potential negative consequences to individuals, organisations and society resulting from the use of these technologies is challenging. In order to address these challenges, this paper presents an ethics-by-design approach that has been developed and implemented in the context of Decision Supports Systems for Emergency Management. Such systems help manage large and cross-border disasters by supporting decision makers to respond on emergencies in a reasonable way by taking follow-up actions into account. The approach taken in this paper specifically provides means to support the ethical dimensions of these decisions. Actions taken during disasters can have ramifications that persist long after a disaster has passed. The ethics-by-design approach presented here not only informs the design of systems, but also considers the role and training of the decision makers in the design process. The paper builds on the literature on ethics in information systems and makes a contribution to theory by providing a framework to ensure ethical considerations are embedded into the design of systems.     

资源分配问题的协商模型

研究Agent多次协商过程中的策略调整问题,目的是使得Agent在协商过程中具有自学能力,对环境和协商对手更敏感。结合资源分配问题,讨论Agent协商过程中的学习问题,基于博弈论分别分析了单次协商和多次协商模型,给出了协商过程中在不同信息条件下遵循的策略,并进行了证明。     

Consensus-based decision support for multicriteria group decision making

Consensus decision making is complex and challenging in multicriteria group decision making due to the involvement of several decision makers, the presence of multiple, and often conflicting criteria, and the existence of subjectiveness and imprecision in the decision making process. To ensure effective decisions being made, the interest of all the decision makers usually represented by the degree of consensus in the decision making process has to be adequately considered. This paper presents a consensus-based approach for effectively solving the multicriteria group decision making problem. The subjectiveness and imprecision of the decision making process is adequately handled by using intuitionistic fuzzy numbers. An interactive algorithm is developed for consensus building in the group decision making process. A decision support system framework is presented for improving the effectiveness of the consensus building process. An example is presented for demonstrating the applicability of the proposed approach for solving the multicriteria group decision making problem in real world situations.     

JUDGES: A descriptive group decision support system for the ranking of items

A set of tools for group decision support are presented. Decision problems involving several decision makers, here-after called judges, that have to rank several alternatives, are considered. The toolbox is called JUDGES. It includes the four following procedures:

• - a hierarchical representation of the judges allows to display the existing conflicts between groups of judges,

• - enhanced box-plots representations of the alternatives are generated in order to detect those that are responsible for the major conflicts,

• - specific advice is issued to each judge in order to reach more easily a consensus,

• - a general framework for a pairwise group preference structure is proposed, and can be used to finalise the decision.

These procedures are embedded in an interactive software, implemented on micro-computer, which currently simulates the use on a network. Actual network implementation is foreseen in the near future. Several applications are presented and future developments are discussed.

Attention allocation for decision making queues

We consider the optimal servicing of a queue with sigmoid server performance. There are various systems with sigmoid server performance, including systems involving human decision making, visual perception, human–machine communication and advertising response. Tasks arrive at the server according to a Poisson process. Each task has a deadline that is incorporated as a latency penalty. We investigate the trade-off between the reward obtained by processing the current task and the penalty incurred due to the tasks waiting in the queue. We study this optimization problem in a Markov decision process (MDP) framework. We characterize the properties of the optimal policy for the MDP and show that the optimal policy may drop some tasks; that is, may not process a task at all. We determine an approximate solution to the MDP using the certainty-equivalent receding horizon optimization framework and derive performance bounds on the proposed receding horizon policy. We also suggest guidelines for the design of such queues.     

Testing the scenario hypothesis: An experimental comparison of scenarios and forecasts for decision support in a complex decision environment

Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts. In this research, we asked participants to recommend a fisheries management strategy that achieved multiple objectives in the face of significant uncertainty. A decision support tool with one of two conditions—Scenario or Forecast—encouraged participants to explore a large set of diversified decision options. We found that participants in the two conditions explored the options similarly, but chose differently. Participants in the Scenario Condition chose the strategies that performed well over the full range of uncertainties (robust strategies) significantly more frequently than did those in the Forecast Condition. This difference seems largely to be because participants in the Scenario Condition paid increased attention to worst-case futures. The results offer lessons for designing decision support tools.     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

Fuzzy decision support software for crisis management in gas transmission networks

In this work, a new software for fuzzy decision support system, with a new decision making structure, to control the crisis conditions in the gas transmission network is developed. This system after receiving both functional variables of the gas transmission network and faults signals makes right decisions to eliminate and repair the conditions of the transmission network according to its database established through experience gathered from experts. These decisions are expressed in the form of some scenarios with different desirability degrees. Desirability degrees measure the outcomes of the decisions taken for the aforementioned conditions; this in turn will assist the managers in choosing the best ones. The user interface properly developed is graphical and provides the manager a good facility to easily use the system without any hesitation at the occurrence time of crisis.     

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.