The effectiveness of virtual facilitation in supporting GDSS appropriation and structured group decision making

Since their introduction a quarter of a century ago, group decision support systems (GDSS) have evolved from applications designed primarily to support decision making for groups in face-to-face settings, to their growing use for “web conferencing,” online collaboration, and distributed group decision-making. Indeed, it is only recently that such groupware applications for conducting face-to-face, as well as “virtual meetings” among dispersed workgroups have achieved mainstream status, as evidenced by Microsoft’s ubiquitous advertising campaign promoting its “Live Meeting” electronic meeting systems (EMS) software. As these applications become more widely adopted, issues relating to their effective utilization are becoming increasingly relevant. This research addresses an area of growing interest in the study of group decision support systems, and one which holds promise for improving the effective utilization of advanced information technologies in general: the feasibility of using virtual facilitation (system-directed multi-modal user support) for supporting the GDSS appropriation process and for improving structured group decision-making efficiency and effectiveness. A multi-modal application for automating the GDSS facilitation process is used to compare conventional GDSS-supported groups with groups using virtual facilitation, as well as groups interacting without computerized decision-making support. A hidden-profile task designed to compare GDSS appropriation levels, user satisfaction, and decision-making efficiency and effectiveness is utilized in an experiment employing auditors, accountants, and IT security professionals as participants. The results of the experiment are analyzed and possible directions for future research efforts are discussed

Multi-Objective Optimization in Negotiation Support

The paper reviews the methodology of multi-objective modeling and optimization used in decision support based on computerized analytical models (as opposed to logical models used in expert systems) that represent expert knowledge in a given field. The essential aspects of this methodology relate to its flexibility: modeling and optimization methods are treated not as goals in themselves but as tools that help a sovereign user (an analyst or a decision maker) to interact with the model, to generate and analyze various decision options, to learn about possible outcomes of these decisions. Although the application of such methods in negotiation and mediation support is scarce yet, their flexibility increases essentially the chances of such applications. Various aspects of negotiation and mediation methods related to multi-objective optimization and game theory are also reviewed

Multi User Context-Aware Service Selection for Mobile Environments - A Heuristic Technique

Modern service systems build on top of service dominant designs which encompass contextualization (value-in-context) and collaboration (value-in-use) between users and service providers. Processes in this domain often require the consideration of both context information (e.g., location or time of day) and multiple participating users where each user probably has its own preferences and constraints (e.g., restricted overall budget). However, selecting a suitable service provider for each action of a process, especially when some of these actions are conducted together by several users, can be a complex decision problem in multi user context-aware service systems. Consequently, exact approaches are not fit to solve such a service selection problem in appropriate time. Thus, the paper proposes a heuristic technique applying a decomposition of the users’ global constraints and a local service selection. In this way, the aim is to determine a feasible service composition for each participating user while taking the users’ individual preferences and constraints as well as context information into account. The evaluation of the heuristic technique shows, based on a real-world scenario in the tourism domain, that the proposed approach is able to achieve close-to-optimal solutions while efficiently scaling with problem size and therefore can support decision makers in multi user context-aware service Systems

ISSEC: A Socio-technical Decision Support System for Information Security Planning

The traditional notion of information security, rooted in a solidly technical foundation, has within the past decade seen wide criticism within academia - much of which has originated from the social sciences community - as being narrow and technology-centric instead of holistic and organizational in its focus. As information security awareness encompasses an ever-greater scope of organizational dynamics, it becomes necessary for us to develop design methodologies and ultimately, systems, capable of dealing practically with the complex and multifaceted nature of the decision-making of information systems security which is entailed by the emerging notions of a new paradigm for security. To this end, we present an architecture which implements a web-based multi-user decision support system (DSS) driven by an operational security model within a qualitative multi-criteria framework that utilizes AHP as its inference engine. The system is then demonstrated in action, by addressing a multi-criteria security control selection decision

ISSEC: A socio-technical DSS for information security planning

The traditional notion of information security, rooted in a solidly technical foundation, has within the past decade seen wide criticism within academia - much of which has originated from the social sciences community - as being narrow and technology-centric instead of holistic and organizational in its focus. As information security awareness encompasses an ever-greater scope of organizational dynamics, it becomes necessary for us to develop design methodologies and ultimately, systems, capable of dealing practically with the complex and multifaceted nature of the decision-making of information systems security which is entailed by the emerging notions of a new paradigm for security. To this end, we present an architecture which implements a web-based multi-user decision support system (DSS) driven by an operational security model within a qualitative multi-criteria framework that utilizes AHP as its inference engine. The system is then demonstrated in action, by addressing a multi-criteria security control selection decision

The development of 'for experts systems' as heuristic reasoning platforms in risk decision support: a consideration of tool design, technology transfer and compatability with Bayesian decision analysis

This work considers the creation of two risk and decision support systems, one for the National Air Traffic Services of the UK and one for Unilever, a multi-national. Their development contributes to risk decision science in the area of decision support in particular. This contribution is based on the development real-life systems, it has three key elements. One, it addresses the fact that, for practical environments like these, the science of risk and decisions is insufficiently resolved to be accepted and easily used. Two, the systems share an arena with subjective Bayesian decision analysis. The benefits of a hybrid form of the two approaches to generate higher levels of user acceptance and organisational transfer is discussed. Three, they take the unique approach of being 'for experts' systems rather than 'expert systems'. This approach offers a number of benefits to applied user communities. These include: a decision support system which remains grounded within the reasoning world view of the decision makers; an expansion and refinement of the existing 'natural heuristics' that decision makers use currently; a scoring and visualisation environment which is both fast and flexible but allows for, previously unavailable, levels of reasoning transparency and comparison. Taken in total the combination of the tool design, the heuristic artefacts within them and their influence on the hosts organisations, the two systems have proven they can provide an effective and valued 'heuristic reasoning platform' for risks and issues. A future research direction is to explore ways in which the highly transferable heuristic artefacts in these systems, particularly measurement and data manipulation, might be strengthened via hybridisation with more powerful, but less transferred, formal systems like Bayes decision analysis

Analysis of scheduling in a diagnostic imaging department: A simulation study

In this thesis we present an Agent-Based Modelling Tool (ABMT) for use in the investigation of the impact that operational level changes have on diagnostic imaging scheduling and patient wait times. This tool represents a novel application of agent-based modelling in the outpatient scheduling/simulation fields. The ABMT is a decision support tool with a user friendly graphical user interface that is capable of modelling a wide array of outpatient scheduling scenarios. The tool was verified and validated using data and expertise from Hotel Dieu Grace Hospital, Windsor, Ontario, Canada. The ABMT represents a technological advancement in the modelling of multi-server, multi-priority class customer queueing systems with deterministic service times and uneven distribution of server up-time

Intelligent Processing of Remote Sensing Imagery for Decision Support in Environmental Resource Management: A Neural Computing Paradigm

In this study, we propose a new neural network (NN) computational paradigm to resolve the resource management decision support (DS) oriented problems based on reconstructive remote sensing (RS) imaging with the use/fusion of multi- sensor systems as required for enhanced DS in environmental resource management and other related fields in DS technologies. The developed NN paradigm addresses a framework for resolving the computational problems related to the end-user DS in environmental monitoring based on the intelligent RS image reconstruction/enhancement.Cinvesta

Model-Based Decision Support for Industry-Environment Interactions

Applied systems analysis is -- or should be -- a tool in the hands of planners and decision makers who have to deal with the complex and growing problems of modern society. There is, however, an obvious gap between the ever-increasing complexity and volume of scientific and technological information and tools of analysis relevant to large socio-technical and environmental systems, and the information requirements at a strategic planning and policy level. The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology, and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way. As an example of this approach to information and decision support systems, one of the components of an R&D project sponsored by the CEC's EURATOM Joint Research Centre (JRC) at Ispra, Italy, in the area of hazardous substances and industrial risk management, is described in this paper. The PDA (Production Distribution Area) is an interactive optimization code (based on DIDASS, one of a family of multicriteria decision support tools developed at IIASA) and a linear problem solver, for chemical industry structures, configured for the pesticide industry of a hypothetical region. The user can select optimization criteria, define allowable ranges or constraints on these criteria, define reference points for the multi-criteria trade-off, and display various levels of model output, including the waste streams generated by the different industrial structure alternatives. These waste streams can then be used to provide input conditions for the environmental impact models. With the emphasis on a directly understandable problem representation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, this is a step toward increased direct practical usability of IIASA's research results

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201