Showing papers on "R-CAST published in 2011"

Multiple Attribute Decision Making: Methods and Applications

Abstract: Decision makers are often faced with several conflicting alternatives [1]. How do they evaluate trade-offs when there are more than three criteria? To help people make optimal decisions, scholars in the discipline of multiple criteria decision making (MCDM) continue to develop new methods for structuring preferences and determining the correct relative weights for criteria. A compilation of modern decision-making techniques, Multiple Attribute Decision Making: Methods and Applications focuses on the fuzzy set approach to multiple attribute decision making (MADM). Drawing on their experience, the authors bring together current methods and real-life applications of MADM techniques for decision analysis. They also propose a novel hybrid MADM model that combines DEMATEL and analytic network process (ANP) with VIKOR procedures.

The ethical dangers of deliberative decision making.

Abstract: Research on ethical decision making has been heavily influenced by normative decision theories that view intelligent choices as involving conscious deliberation and analysis. Recent developments in...

A framework for dynamic multiple-criteria decision making

Abstract: 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.

The importance of communication in collaborative decision making: facilitating shared mind and the management of uncertainty.

Abstract: Rationale Quality medical decision making requires that clinicians and patients incorporate the best available clinical evidence with the patients' values and preferences to develop a mutually agreed upon treatment plan The interactive process involved in medical decision making is complex and requires patients and clinicians to use both cognitive and communicative skills to reach a shared understanding of the decision Aims and objectives The purpose of this paper is to present a communication model to help better understand quality medical decision making, and how patient-centered, collaborative communication enhances the decision-making process Methods We present research on shared mind and cognitive and communicative skills to highlight how they can facilitate the management of uncertainty during the interactive process involved in medical decision making We provide simple examples about how to frame messages to achieve shared mind and foster uncertainty tolerance Conclusions Strategies such as providing clear explanations, checking for understanding, eliciting the patient's values, concerns, needs, finding common ground, reaching consensus on a treatment plan, and establishing a mutually acceptable follow-up plan can facilitate collaborative decision making Future research should explore ways to implement collaborative decision-making processes in existing health care systems

Fuzzy-Like Multiple Objective Decision Making

Abstract: Decision makers usually face multiple, conflicting objectives and the complicated fuzzy-like environments in the real world. What are the fuzzy-like environments? How do we model the multiple objective decision making problems under fuzzy-like environments? How do you deal with these models? In order to answer these questions, this book provides an up-to-date methodology system for fuzzy-like multiple objective decision making, which includes modelling system, model analysis system, algorithm system and application system in structure optimization problem, selection problem, purchasing problem, inventory problem, logistics problem and so on. Researchers, practitioners and students in management science, operations research, information science, system science and engineering science will find this work a useful reference.

A Framework for Adopting Collaboration 2.0 Tools for Virtual Group Decision Making

Abstract: Decision making in virtual teams is gaining momentum due to globalization, mobility of employees, and the need for collective and rapid decision making by members who are in different locations. These factors resulted in a proliferation of virtual team software support tools for decision making, the latest of which is social software (also known as collaboration 2.0), which includes tools such as wikis, blogs, microblogs, discussion forums, and social networking platforms. This paper describes the potential use of collaboration 2.0 software for improving the process and the specific tasks in virtual group decision making. The paper proposes a framework for exploring the fitness between social software and the major activities in the group decision making process and how such tools can be successfully adopted. Specifically, we use a fit-viability model to help assessing whether social software fit a decision task and what organizational factors are important for such tools to be effective. Representative research issues related to the use of such tools are also presented.

Enabling Safe Autonomous Driving in Real-World City Traffic Using Multiple Criteria Decision Making

Abstract: This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.

Decision Support Systems for Business Intelligence

Abstract: I. Introduction to Decision Support Systems. Chapter 1. Introduction. Chapter 2. Decision Making. II. DSS Components. Chapter 3. The Data Component. Chapter 4. The Model Component. Chapter 4S. Intelligence and Decision Support Systems. Chapter 5. The User Interface. III. Issues of Design. Chapter 6. International Decision Support Systems. Chapter 7. Designing a Decision Support System. Chapter 8. Object Oriented Technologies and Decision Support Systems Design. Chapter 9. Implementation Strategy. IV. Extension of Decision Support Systems. Chapter 10. Executive Systems and Dashboards. Chapter 11. Group Decision Support Systems.

Decision maps: A framework for multi-criteria decision support under severe uncertainty

Abstract: In complex strategic decision-making situations the need for well-structured support arises. To evaluate decision alternatives, information about the situation and its development must be determined, managed and processed by the best available experts. For various types of information different reasoning principles have been developed: deterministic, probabilistic, fuzzy and techniques for reasoning under ignorance (i.e., the likelihood of an event cannot be quantified). We propose a new approach based on Decision Maps supporting decision makers under fundamental uncertainty by generating descriptions of different possible situation developments (scenarios) in a distributed manner. The scenarios are evaluated using Multi-Criteria Decision Analysis techniques.

The design of patient decision support interventions : addressing the theory-practice gap

Abstract: Background Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory We argue the importance of using theory to guide design The aim of this work was to address this theory–practice gap and to examine how a range of selected decision-making theories could inform the design and evaluation of decision support interventions Methods We reviewed the decision-making literature and selected relevant theories We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory-based outcome measures Results Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy-trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational–emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting Some theories have strong relevance to the information design (eg prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity) None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions It was also clear that most work in theory-building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions It is not surprising therefore that a large theory–practice gap exists as we consider decision support for patients There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions Discussion Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions

Group decision making problems in a linguistic and dynamic context

Abstract: The aim of this paper is to present a new model of decision support system for group decision making problems based on a linguistic approach and dynamic sets of alternatives. The model incorporates a mechanism that allows to manage dynamic decision situations in which some information about the problem is not constant in time. We assume that the set of alternatives can change during the decision making process. The model is presented in a mobile and dynamic context where the experts' preferences can be incomplete. The linguistic approach is used to represent both the experts' preferences about the alternatives and the agreement degrees to manage the change of some alternatives. A prototype of such mobile decision support system in which the experts use mobile devices to provide their linguistic preferences at anytime and anywhere has been implemented. In such a way, we provide a new linguistic group decision making framework that is mobile and dynamic.

Consumer decision making in knowledge-based recommendation

Abstract: In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales experts. In such situations recommender applications help to identify the products and/or services that fit the user's wishes and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision strategies of users. These decision strategies are explained in different models of human decision making. In this paper we provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide an outlook on future research issues.

The Naturalistic Decision Making Perspective

Abstract: The naturalistic decision making (NDM) approach seeks to understand human cognitive performance by studying how individuals and teams choose among alternatives in real-world settings. Unlike laboratory studies, NDM research captures human interactions with actual work settings that are typically uncertain, complex, fluid, and time-pressured. The NDM approach emphasizes the role of experience in making it possible for individuals to quickly categorize situations in order to make effective decisions. Models of behavior that have resulted from these studies inform research and performance in high hazard work settings such as revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support cognitive work. Keywords: cognition; decision making; naturalistic decision making

Arguing to motivate decisions

Abstract: Decision makers often have to make difficult trade-offs in situations where multiple aspects matter that are different by nature For example, in a crisis scenario with a big fire in a factory, trade-offs may have to be made between the safety of the victims and personnel, and the effects on the environment When making a complex decision, argumentation plays an important role This thesis addresses the main research question of how a computer can use argumentation to support a person in making a complex decision To accomplish this, the thesis focuses on answering the following two research questions: (1) how can a computer argue about why a person should prefer one decision to another, and (2) how can a computer effectively support a person in this process Decision theory literature describes methods to decompose what a person values into general areas of concern, intermediate objectives, and specific evaluation criteria Although expressive, the quantitative nature complicates their use in supporting complex decisions in a natural way Argumentation literature describes methods to make decisions Although arguing is natural, these methods cannot be used to justify preferences between decisions in more complex situations The thesis proposes a model designed to support evaluating decisions from different perspectives, determining what perspectives should be considered, and how important these perspectives should be This model combines decision theory with argumentation theory In my model, value is seen from a perspective and decisions are compared from the perspectives the decision maker cares about To understand what the decision maker cares about, his perspective is decomposed into perspectives representing the general areas of concern that he has (ie, the values that he holds) These general areas of concern are further decomposed into intermediate objectives and specific evaluation criteria In this way, abstract values are made concrete, which enables the computer to compare decisions on these criteria Given an understanding of the perspectives the decision maker cares about, the computer can then use a number of argumentation schemes to justify why the decision maker should prefer one decision to another Moreover, several argumentation schemes are proposed to justify why one argument for a decision is stronger than another In this way, the computer can reason about what the decision maker should do Dialogues are used to support the decision maker in a natural way Using a dialogue, the decision maker can put forward a counterargument when he disagrees with an argument of the computer To accomplish this, an existing dialogue system is extended such that the computer and decision maker can not only exchange arguments, but also argue about the strength of these arguments While exchanging arguments, the computer learns more about what the decision maker values and can use this information to advance more persuasive arguments Finally,a method is proposed that use my value model to select the most persuasive argument in the dialogue

Decision making in surgical oncology.

Abstract: Decisions in surgical oncology are increasingly being made by multi-disciplinary teams (MDTs). Although MDTs have been widely accepted as the preferred model for cancer service delivery, the process of decision making has not been well described and there is little evidence pointing to the ideal structure of an MDT. Performance in surgery has been shown to depend on non-technical skills, such as decision making, as well as patient factors and the technical skills of the healthcare team. Application of this systems approach to MDT working allows the identification of factors that affect the quality of decision making for cancer patients. In this article we review the literature on decision making in surgical oncology and by drawing from the systems approach to surgical performance we provide a framework for understanding the process of decision making in MDTs. Technical factors that affect decision making include the information about patients, robust ICT and video-conferencing equipment, a minimum dataset with expert review of radiological and pathological information, implementation and recording of the MDTs decision. Non-technical factors with an impact on decision making include attendance of team members at meetings, leadership, teamwork, open discussion, consensus on decisions and communication with patients and primary care. Optimising these factors will strengthen the decision making process and raise the quality of care for cancer patients.

One-Shot Decision Theory

Abstract: This paper focuses on one-shot (one-time) decision problems, which concern the situations where a decision is experienced only once. Such one-shot decision problems are commonly encountered in business, economics, and social systems. The one-shot decision process comprises two steps. In the first step, a decision maker identifies which state of nature should be taken into account for each alternative among all available states of nature. These identified states of nature are called focus points. Twelve types of focus points are proposed according to a decision maker's attitudes about the possibility of a state of nature and satisfaction resulted by its occurrence. In the second step, a decision maker evaluates the alternatives based on focus points where the consequences provided by all alternatives are compared with each other to obtain the best alternative. The relationships between different focus points are analyzed. An example, which is a newsvendor problem of a new product with a short life cycle, is used to illustrate the one-shot decision. The results of analysis show that the proposed decision models can provide useful insights into understanding different behaviors of decision makers and help the decision maker in finding out the best solution according to his/her attitude about possibility and satisfaction.

Information Fusion Measures of Effectiveness (MOE) for Decision Support

Abstract: For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

The Main Factors beyond Decision Making

Abstract: This paper argues that the time has come to focus attention on the search for factors that affect decision making because decision making errors are costly and are growing more costly, decision makers are receptive, and academic insights are sure to follow from research on improvement. In addition to calling for research on improvement strategies, this paper aims to conclude the main factors that affect decision making, and how these factors have a great impact and influence on decision makers. The researcher focuses on literature review to come up with these main factors.

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

Abstract: 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.

Why values elicitation techniques enable people to make informed decisions about cancer trial participation.

Abstract: Background Decision aids help patients make informed treatment decisions. Values clarification (VC) techniques are part of decision aids that help patients assimilate the information with their personal values. There is little evidence that these techniques contribute to enhanced decision making over and above the provision of good quality information. Objectives To assess whether VC techniques are active ingredients in enhancing informed decision making and explain how and why they work. Methods Participants were randomly assigned to one of three groups: (i) information only, (ii) information plus implicit task, (iii) information plus explicit task. Thirty healthy women from a UK University participated by making a hypothetical choice between taking part in a clinical trial and having the standard treatment for breast cancer. Verbal protocols were elicited by think-aloud method and content analysed to assess informed decision making; a questionnaire was completed after the decision assessing decision preference, perceptions of decisional conflict and ambivalence. Data were analysed using multivariate statistics. Findings No participants changed their decision preference as a result of the VC techniques. Women in the explicit VC group evaluated more information in accord with personal values, expressed lower ambivalence, decisional uncertainty and greater clarity of personal values than those in the implicit VC and control groups. Feelings of ambivalence about both options were related to decisional conflict. Conclusion Explicit VC techniques are likely to be active ingredients in decision aids. They work by enabling people to deliberate about the decision information in accord with their personal values, which is associated with a better decision experience.