Showing papers on "R-CAST published in 2014"
TL;DR: A consensus model suitable to manage large scales of decision makers is presented, which incorporates a fuzzy clustering-based scheme to detect and manage individual and subgroup noncooperative behaviors.
Abstract: Consensus reaching processes in group decision making attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate consensus reaching processes. Classical models focus on solving group decision making problems where few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scales of decision makers, such as e-democracy and social networks, add a new requirement to the solution of consensus-based group decision making problems. Dealing with such large groups implies the need for mechanisms to detect decision makers’ noncooperative behaviors in consensus, which might bias the consensus reaching process. This paper presents a consensus model suitable to manage large scales of decision makers, which incorporates a fuzzy clustering-based scheme to detect and manage individual and subgroup noncooperative behaviors. The model is complemented with a visual analysis tool of the overall consensus reaching process based on self-organizing maps, which facilitates the monitoring of the process performance across the time. The consensus model presented is aimed to the solution of consensus processes involving large groups.
404 citations
TL;DR: Evidence-based medicine (EBM) and shared decision making (SDM) are both essential to quality health care, yet the interdependence between these 2 approaches is not generally appreciated.
Abstract: Evidence-based medicine (EBM) and shared decision making (SDM) are both essential to quality health care, yet the interdependence between these 2 approaches is not generally appreciated. Evidence-based medicine should begin and end with the patient: after finding and appraising the evidence and integrating its inferences with their expertise, clinicians attempt a decision that reflects their patient’s values and circumstances. Incorporating patient values, preferences, and circumstances is probably the most difficult and poorly mapped step—yet it receives the least attention.1 This has led to a common criticism that EBM ignores patients’ values and preferences—explicitly not its intention.2 Shared decision making is the process of clinician and patient jointly participating in a health decision after discussing the options, the benefits and harms, and considering the patient’s values, preferences, and circumstances. It is the intersection of patient-centered communication skills and EBM, in the pinnacle of good patient care (Figure).
333 citations
TL;DR: A selective review of the literature suggests that twelve of the most commonly perceived barriers to scaling up shared decision making across the healthcare spectrum should be termed myths as they can be dispelled by evidence.
313 citations
Posted Content•
TL;DR: This paper provides guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question, and gives an overview of its design.
Abstract: When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.
148 citations
TL;DR: The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.
Abstract: Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.
136 citations
TL;DR: Having a clear and unique understanding of clinical decision making will allow for consistent use of the term, which is relevant given the changing educational requirements for nurse practitioners and broadening scope of practice.
122 citations
TL;DR: In this article, the conceptual link between supported decision making and legal capacity is explored by outlining three conceptualisations that are influencing the development of practice and the difference between support with decision-making and support without decision making.
Abstract: This article aims to help readers to understand the conceptual link between supported decision making and legal capacity and how this is influencing the development of practice. It examines how the concept has been defined as: a process of supporting a person with decision making; a system that affords legal status; and a means of bringing a person's will and preference to the centre of any substituted decision-making process. The conceptual link between supported decision making and legal capacity is explored by outlining three conceptualisations that are influencing the development of practice. It is important to understand the difference between supported decision making and support with decision making. Both involve offering support to a person who is unable to navigate decision making independently. However, the key difference is whether or not the process results in greater legal capacity for the individual. Additionally, supported decision making requires the development of legal mechanisms that le...
109 citations
Posted Content•
TL;DR: The Bill & Melinda Gates Foundation, which has funded four initiatives promoting strategic use of data at the state and district levels, requested that Mathematica develop a conceptual framework for data-driven decision making (DDDM) based upon knowledge generated fromMathematica’s evaluation of the strategic data use initiatives and from existing literature on data use in education.
Abstract: The Bill & Melinda Gates Foundation, which has funded four initiatives promoting strategic use of data at the state and district levels, requested that Mathematica develop a conceptual framework for data-driven decision making (DDDM) based upon knowledge generated from Mathematica’s evaluation of the strategic data use initiatives and from existing literature on data use in education.
101 citations
23 Oct 2014
TL;DR: The experiments performed in this paper conclude that agents developed from a priori defined objectives can express human decision making styles and that they are more generalizable and versatile than Q-learning and hand-crafted agents.
Abstract: This paper explores how evolved game playing agents can be used to represent a priori defined archetypical ways of playing a test-bed game, as procedural personas. The end goal of such procedural personas is substituting players when authoring game content manually, procedurally, or both (in a mixed-initiative setting). Building on previous work, we compare the performance of newly evolved agents to agents trained via Q-learning as well as a number of baseline agents. Comparisons are performed on the grounds of game playing ability, generalizability, and conformity among agents. Finally, all agents' decision making styles are matched to the decision making styles of human players in order to investigate whether the different methods can yield agents who mimic or differ from human decision making in similar ways. The experiments performed in this paper conclude that agents developed from a priori defined objectives can express human decision making styles and that they are more generalizable and versatile than Q-learning and hand-crafted agents.
91 citations
TL;DR: It has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for Group decision making using only AHP.
Abstract: Decision support for supplier selection is a highly researched theme in procurement management literature. However applications of group decision support theories are yet to be explored extensively in this domain. This study proposes an approach for group decision support for the supplier selection problem by integrating fuzzy Analytic Hierarchy Process (AHP) for group decision making and fuzzy goal programming for discriminant analysis. In the first step, the fuzzy AHP theory with the Geometric Mean Method has been used to prioritize and aggregate the preferences of a group of decision makers. Then consensus has been developed between these aggregated priorities using the Ordinal Consensus Improvement Approach. Subsequently, the consensual priorities of this group of decision makers have been integrated with fuzzy goal programming theory for discriminant analysis to provide predictive decision support. Finally it has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for group decision making using only AHP.
88 citations
TL;DR: A graphical monitoring tool based on Self-Organizing Maps so-called MENTOR is proposed, that provides a 2-D graphical interface whose information is related to experts' preferences and their evolution during group decision making problems, and facilitates the analysis of information about large-scale problems.
Abstract: Group decision making problems aim to manage situations in which two or more experts need to achieve a common solution to a decision problem. Different rules and processes can be applied to solve such problems (e.g. majority rule, consensus reaching, and so on), and several models have been proposed to deal with them. Some difficulties may arise in group decisions, being most of them caused by the presence of disagreement positions amongst experts. Given that group decision making problems have classically focused on a few number of experts, such difficulties have been relatively manageable by means of supporting tools based on textual or numerical information. However, such tools are not adequate when a large number of experts take part in the problem, therefore an alternate tool that provides decision makers with more easily interpretable information about the status of the problem becomes necessary. This paper proposes a graphical monitoring tool based on Self-Organizing Maps so-called MENTOR, that provides a 2-D graphical interface whose information is related to experts' preferences and their evolution during group decision making problems, and facilitates the analysis of information about large-scale problems.
TL;DR: This tutorial illustrates the procedural steps of the AHP in supporting group decision making about new healthcare technology, including identifying the decision goal, decision criteria, and alternative healthcare technologies to compare, and structuring the decision criteria.
Abstract: The analytic hierarchy process (AHP) has been increasingly applied as a technique for multi-criteria decision analysis in healthcare. The AHP can aid decision makers in selecting the most valuable technology for patients, while taking into account multiple, and even conflicting, decision criteria. This tutorial illustrates the procedural steps of the AHP in supporting group decision making about new healthcare technology, including (1) identifying the decision goal, decision criteria, and alternative healthcare technologies to compare, (2) structuring the decision criteria, (3) judging the value of the alternative technologies on each decision criterion, (4) judging the importance of the decision criteria, (5) calculating group judgments, (6) analyzing the inconsistency in judgments, (7) calculating the overall value of the technologies, and (8) conducting sensitivity analyses. The AHP is illustrated via a hypothetical example, adapted from an empirical AHP analysis on the benefits and risks of tissue regeneration to repair small cartilage lesions in the knee.
TL;DR: A novel consensus support system based on the multiagent system paradigm is presented, which automates and supports consensus reaching processes by providing agents with the necessary degree of autonomy to conduct discussion processes by themselves, with a semisupervised methodology.
Abstract: Consensus reaching processes as part of solving group decision-making problems attempt to reach a mutual agreement in the group before making a decision. Most consensus models and consensus support systems that are proposed in the literature present some noticeable drawbacks: the need for constant human supervision by experts to guarantee an effective process and the difficulty to manage large groups of experts, which are increasingly common in current decisions and may imply a higher cost and complexity to carry out such processes. In order to overcome these problems, this paper presents a novel consensus support system based on the multiagent system paradigm, which automates and supports consensus reaching processes by providing agents with the necessary degree of autonomy to conduct discussion processes by themselves, with a semisupervised methodology. The main novelty of such a system is the agent semisupervised autonomy approach it incorporates, which lets agents conduct most of the discussion process by themselves and allows them to interact with their corresponding human experts under certain circumstances in which human supervision might be convenient and necessary.
TL;DR: An overview of tools used in three Canadian public health departments involved in a study to develop capacity for evidence-informed decision making is provided and qualitative analysis of the perceived usefulness and usability of the tools are presented.
Abstract: Public health professionals are increasingly expected to engage in evidence-informed decision making to inform practice and policy decisions. Evidence-informed decision making involves the use of research evidence along with expertise, existing public health resources, knowledge about community health issues, the local context and community, and the political climate. The National Collaborating Centre for Methods and Tools has identified a seven step process for evidence-informed decision making. Tools have been developed to support public health professionals as they work through each of these steps. This paper provides an overview of tools used in three Canadian public health departments involved in a study to develop capacity for evidence-informed decision making. As part of a knowledge translation and exchange intervention, a Knowledge Broker worked with public health professionals to identify and apply tools for use with each of the steps of evidence-informed decision making. The Knowledge Broker maintained a reflective journal and interviews were conducted with a purposive sample of decision makers and public health professionals. This paper presents qualitative analysis of the perceived usefulness and usability of the tools. Tools were used in the health departments to assist in: question identification and clarification; searching for the best available research evidence; assessing the research evidence for quality through critical appraisal; deciphering the ‘actionable message(s)’ from the research evidence; tailoring messages to the local context to ensure their relevance and suitability; deciding whether and planning how to implement research evidence in the local context; and evaluating the effectiveness of implementation efforts. Decision makers provided descriptions of how the tools were used within the health departments and made suggestions for improvement. Overall, the tools were perceived as valuable for advancing and sustaining evidence-informed decision making. Tools are available to support the process of evidence-informed decision making among public health professionals. The usability and usefulness of these tools for advancing and sustaining evidence-informed decision making are discussed, including recommendations for the tools’ application in other public health settings beyond this study. Knowledge and awareness of these tools may assist other health professionals in their efforts to implement evidence-informed practice.
TL;DR: A theory-based guide for researchers and practitioners interested in using cultural targeting and tailoring to develop and test decision aids that move beyond a "one-size fits all" approach and thereby, improve SDM in the authors' multicultural world.
TL;DR: This work combines voting methods with a Monte-Carlo selection, in order to help with social choice making under uncertainty, to aid decision-makers with understanding of the risks associated with potential decision alternatives.
Abstract: Water resources policy making often involves consideration of a broader scope of environmental, economic, and social issues. This inevitably complicates policy making since consensus among multiple stakeholders with different interests is needed to implement decisions. This work employs several practical and popular voting methods to solve a multi-stakeholder hydro-environmental management problem. Conventionally, voting methods or social choice rules have been applied for consensus development in small groups and elections. This work combines voting methods with a Monte-Carlo selection, in order to help with social choice making under uncertainty. This process is intended to aid decision-makers with understanding of the risks associated with potential decision alternatives. The Sacramento-San Joaquin Delta’s water export conflict is solved here as a benchmark problem to illustrate the proposed framework for social decision making and analysis under uncertainty.
Book•
08 Aug 2014
TL;DR: The aim of this book is to shift the foundation of decision analysis and economic behavior from the realm bivalent logic to the realm fuzzy logic and Z-restriction, from external modeling of behavioral decisions to the framework of combined states.
Abstract: Every day decision making in complex human-centric systems are characterized by imperfect decision-relevant information. The principal problems with the existing decision theories are that they do not have capability to deal with situations in which probabilities and events are imprecise. In this book, we describe a new theory of decision making with imperfect information. The aim is to shift the foundation of decision analysis and economic behavior from the realm bivalent logic to the realm fuzzy logic and Z-restriction, from external modeling of behavioral decisions to the framework of combined states. This book will be helpful for professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics. Readership: Professionals, academics, managers and graduate students in fuzzy logic, decision sciences, artificial intelligence, mathematical economics, and computational economics.
TL;DR: By solving the case problem of CAS, the proposed integrated MADM approach demonstrates its superiority and helps managers make more robust and reliable decisions and also provides managers with a coefficient to help them readily check the group consensus.
Abstract: It is common that a decision maker′s utility depends on the degree to which the attribute performance matches the aspiration. However, aspiration is not considered in the traditional multi-attribute decision making (MADM) approaches. Using the Strategic Freight Forwarder Selection of China Southern Airlines (CSA) as a backdrop, this study proposes an integrated MADM approach for problems with consideration of decision maker’s aspirations. By solving the case problem of CAS, the proposed approach demonstrates its superiority. It helps managers make more robust and reliable decisions and also provides managers with a coefficient to help them readily check the group consensus. In addition, the approach can accommodate complex decision data, such as numerical values, interval numbers, linguistic terms and uncertain linguistic terms.
TL;DR: In this article, a common measure of five decision styles, along with a measure of the traits in the five-factor model of personality were used to predict decision success. And, there was clear evidence for incremental validity for specific decision styles when self-ratings were predicted.
TL;DR: The uniformity of these findings underlines the adequacy of the novel information intrusion paradigm and comprehensively promotes the notion of a uniform decision making mechanism as assumed by single-process models.
TL;DR: In this article, a high-level overview of various aspects relevant to multi-agent decision making is given, focussing on game theory, complex decision making, and on intelligent agents.
Abstract: In this article we give a high-level overview of various aspects relevant to multi-agent decision making. Classical decision theory makes the start. Then, we introduce multi-agent decision making, focussing on game theory, complex decision making, and on intelligent agents. Afterwards, we discuss methods for reaching agreements interactively, e.g. by negotiation, bargaining, and argumentation, followed by approaches to coordinate and to control agents’ decision making.
01 Dec 2014
TL;DR: The results show that ECM positively influences problem identification and definition, decision making speed and analysis, decision quality, and decision makers' satisfaction.
Abstract: Enterprise content management (ECM) systems help organizations cope with the increasing complexity and volume of data and information. Despite the growing popularity of ECM, published literature indicates that organizations primarily use ECM for operational benefits, while the strategic decision making capabilities are rarely considered. Thus, the most significant rewards of ECM implementation may be largely forgone. This study investigates the potential of ECM technology for decision support. A research model is proposed and validated via an empirical investigation. The results show that ECM positively influences problem identification and definition, decision making speed and analysis, decision quality, and decision makers' satisfaction. Investigate decision support capabilities of enterprise content management systemsDevelop conceptual model linking content stewardship with decision support activitiesHypotheses on impact of enterprise content management system use on decision makingOn-line survey of enterprise content management system users at large academic institutionECM systems help with problem identification and decision making speed and improve decision quality
TL;DR: This article aims to engage clinicians in the development of decision support systems that can meaningfully help with their decision-making tasks and to open a discussion about the future of automated clinical decision support as a part of healthcare delivery.
Abstract: Clinical decision support systems are interactive software systems designed to help clinicians with decision-making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the computer science community, but their inner workings are less well understood by, and known to, clinicians. This article provides a brief explanation of clinical decision support systems and some examples of real-world systems. It also describes some of the challenges to implementing these systems in clinical environments and posits some reasons for the limited adoption of decision-support systems in practice. It aims to engage clinicians in the development of decision support systems that can meaningfully help with their decision-making tasks and to open a discussion about the future of automated clinical decision support as a part of healthcare delivery.
TL;DR: A simple model of the process underlying evacuation decision making is offered, focused on how the evacuation threshold is set based on the anticipated costs of Type I versus Type II errors, and a series of propositions about the conditions under which nonsocially optimal evacuation decisions may be made are offered.
Abstract: Stakeholders often control vital resources for decision makers, and this can lead decision makers to take stakeholder opinions into account when making important decisions. This process can be complicated by a number of factors. First, many important decisions involve risk and uncertainty. When the outcome is uncertain, how does a decision maker take the views of stakeholders into account? Second, many decision makers are accountable to multiple different stakeholder groups with different preferences. How do these heterogeneous stakeholder groups affect the process of decision making? More generally, do these stakeholder considerations lead to decisions that are not socially optimal? We explore these and related questions by focusing on a specific type of high-stakes decision making in a context featuring significant risk and heterogeneous stakeholders—the decision to evacuate a community during the threat of a hurricane hitting land. There is research on weather forecasting techniques and individual evac...
05 May 2014
TL;DR: This work connects decision making with multi-agent argumentation and dialogues, using an existing argumentation-based dialogue framework, to show how two agents can argue towards ``good'' decisions in a distributed manner.
Abstract: Much research has been devoted in recent years to argumentation-based decision making. However, less attention has been given to argumentation-based decision making amongst multiple agents. We present a multi-agent decision framework based on Assumption-based Argumentation. In our model, agents have goals and decisions have attributes which satisfy goals. Our framework supports agents with different goals, candidate decisions, attributes and relations amongst them. Using an existing argumentation-based dialogue framework, we show how two agents can argue towards ``good'' decisions in a distributed manner. We show that, under specific conditions, ``good'' decisions correspond to (1) admissible arguments for the two agents and (2) claims of successful dialogues between the two agents. Thus, this work connects decision making with multi-agent argumentation and dialogues.
07 Apr 2014
TL;DR: In this paper, the authors study how practitioners make group decisions in architecting software systems, how practiced group decision-making techniques relate to state-of-the-art techniques, and challenges companies face when making architecture-related group decisions.
Abstract: When architecting software systems, architects (with the contribution of other stakeholders) make several design decisions. These decisions could be related to the selection of the right components and connectors, the architectural style to be used, the distribution of various components, the deployment of software components into hardware devices, etc. Many methods have been proposed by the research community to help documenting several aspects of architectural design decisions including design alternatives, stakeholder concerns, decisions and the rationale for making such decisions and enhancing the decision-making process. Still, very little has been done to truly understand how architectural design decisions are made by group of practitioners, what information is documented, the tools used for helping documenting and how conflicts are managed. This study, by looking at principles and techniques for group decision making coming from other disciplines, aims to understand: a) how practitioners make group decisions in architecting software systems, b) how practiced group decision-making techniques relate to state-of-the-art techniques, and c) challenges companies face when making architecture-related group decisions. The study is conducted by using a questionnaire distributed to practitioners and researchers involved in group design decisions in industry. The results are used to drive some recommendations to improve the current group design decision process.
01 Jan 2014
TL;DR: This chapter introduces current research undertaken to bring comparable advantages to education, with the goal of helping classroom- and school-level stakeholders incorporate DDDM as integral to their work.
Abstract: During the past decade, data-driven decision making (DDDM) has been at the forefront of many discussions on how to improve public education in the USA. Professions such as medicine, business, politics, engineering, etc. have embraced a data culture and built tools to systematically collect and facilitate analysis of performance data, resulting in dramatic performance improvements. Every day the public depends on companies like Google that collect and aggregate data in ways that help us make decisions about everything from online purchases, to stock investments, to candidate selection. This chapter introduces current research undertaken to bring comparable advantages to education, with the goal of helping classroom- and school-level stakeholders incorporate DDDM as integral to their work. The chapter outlines several different theoretical perspectives currently applied to the DDDM challenge, including the lenses of cultural change, assessment, implementation/adoption, and technology. The bulk of the chapter focuses on research related to models of successful local DDDM implementation, including the design of technological tools and processes to facilitate collection and analysis of actionable data in ways previously not possible. The chapter concludes with implications for research and development that are relevant to those in the fields of instructional technology and learning sciences.
TL;DR: The methodology will recognize and forecast opportunities and threats, making the decision to capitalize on the opportunities and mitigate the threats, in order to ultimately facilitate proactive decision making.
Abstract: This paper proposes a methodology for proactive event-driven decision making. Proper decisions are made by forecasting events prior to their occurrence. Motivation for proactive decision making stems from social and economic factors, and is based on the fact that prevention is often more effective than the cure. The decisions are made in real time and require swift and immediate processing of Big Data, that is, extremely large amounts of noisy data flooding in from various locations, as well as historical data. The methodology will recognize and forecast opportunities and threats, making the decision to capitalize on the opportunities and mitigate the threats. This will be explained through user-interaction and the decisions of human operators, in order to ultimately facilitate proactive decision making.
TL;DR: A comprehensive literature review of applying Fuzzy decision making techniques in personnel selection problem is presented in this article, where a wide range of tools that are able to deal with uncertainty in different types of problems are presented.
Abstract: Personnel selection determines the input quality of personnel, therefore, plays a decisive role in human resource management. Personnel selection problem has been studied extensively. Selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy decision making along with their extensions have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Fuzzy decision making methods have become increasingly popular in decision making for personnel selection. Various decision making approaches have been proposed to solve the problem. This paper presents a comprehensive literature review of the applying Fuzzy decision making techniques in personnel selection problem.