In this dissertation we have addressed the problem of modeling expertise in domains characterized by unquantifiable, often subjective, information, and using that model of expertise as the foundation for building computer-based decision support systems. The key feature of the expert model is to make explicit the essential characteristics of the knowledge experts use to process objective, quantitative information, for making decisions in environments rich in qualitative data. This model is then used as the basis for an "intelligent" interactive assistant that presents information appropriate for the context to operators who may not have developed the necessary expertise. The core of the assistant is a heuristic algorithm that reflects what an expert decision maker would actually do. The algorithm incorporates a set of production rules, i.e., if-then-else rules, to define relevance conditions of quantitative data. These rules employ a dominance principle, i.e., a heuristic association of the relevance of quantitative data with the attributes of qualitative data, characterized as a set of ordered values. The heuristic algorithm is embedded in the assistant and is used to assist non-expert operators in locating information useful for making decisions. The modeling methodology and the heuristic algorithm are applicable for modeling expertise in a class of decision problems characterized by large amounts of qualitative and quantitative data. The process of structuring the expert's knowledge requires empirical evidence from actual decision problems; this evidence feeds the algorithm with heuristic associations between qualitative and quantitative data. The algorithm uses the dominance principle to decide what information to present for a particular set of conditions.

A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.

Model development decisions are an important feature of engineering design. The quality of simulation models often dictates the quality of design decisions, seeing as models guide decision makers (DM) in choosing design decisions. A quality model accurately represents the modeled system and is helpful for exploring what-if scenarios, optimizing design parameters, estimating design performance, and predicting the effect of design changes. However, obtaining a quality model comes at a cost in terms of model development--in experimentation, labor, model development time, and simulation time. Thus, DMs must make appropriate trade-offs when considering model development decisions. The primary challenge in model development is making decisions under significant uncertainty. This thesis addresses model development in the conceptual design phase where uncertainty levels are high. In the conceptual design phase, there are many information constraints which may include an incomplete requirements list, unclear design goals, and/or undefined resource constrains. During the embodiment design phase, the overall objective of the design is more clearly defined, and model development decisions can be made with respect to an overall objective function. For example, the objective may be to maximize profit, where the profit is a known function of the model output. In the conceptual design phase, this level of clarity is not always present, so the DM must make decisions under significant model uncertainty and objective uncertainty. In this thesis, conjoint analysis is employed to solicit the preferences of the decision maker for various model attributes, and the preferences are used to formulate a quasi-objective function during the conceptual design phase--where the overall design goals are vague. Epistemic uncertainty (i.e., imprecision) in model attributes is represented as intervals and propagated through the proposed model development framework. The model development framework is used to evaluate the best course of action (i.e., model development decision) for a real-world packaging design problem. The optimization of medical product packaging is assessed via mass spring damper models which predict contact forces experienced during shipping and handling. Novel testing techniques are employed to gather information from drop tests, and preliminary models are developed based on limited information. Imprecision in preliminary test results are quantified, and multiple model options are considered. Ultimately, this thesis presents a model development framework in which decision makers have systematic guidance for choosing optimal model development decisions.

The aim of this study was to evaluate risk management strategies and decision support tools that might be useful to dryland farmers in southwest Queensland to improve their decision making. This topic was chosen because there has been little previous work done to examine the sources of risk faced by farmers in that area, the practical risk management strategies employed by these farmers, or their interests in and attitudes towards risk management. This study adopted an action-learning approach to present farmers with opportunities to use various tools that might help to manage the range of risks affecting their farm management. The study was designed to test farmers’ interests in existing tools, or the potential for developing new tools, to assist dryland farmers in southwest Queensland improve their risk management. The thesis is presented in three parts including an introduction to the study and an extensive review of the relevant literature on decision making and risk management (Part I), an overview of the area in southwest Queensland where this study was focused and the various research methods used in this study (Part II). Part III comprises four chapters reporting results and presents the conclusions from the study. The thesis also reviews the advantages and disadvantages mentioned in the literature about decision support systems (DSS) in Australian agriculture and examines some programming and simulation models that can be applied to risk management in agriculture. The research methods used in this study included a literature review, interviews, focus group discussions, an ‘expert’ survey, training workshops for farmers, and evaluation techniques. The observations and reflections from the preliminary inquiries identified soil moisture management and crop choice as the critical issues concerning dryland farmers in southwest Queensland when dealing with crop production risks. Those discussions suggested possibilities for developing a decision support tool to help farmers in the study area assess their planting options. In developing the options for a decision support tool for planting decisions, a series of workshops was conducted with farmers in the study area, while some observations of farmers who attended similar workshops in adjacent areas are also reported. These workshops provided the opportunity for participants to experience some existing risk management and decision support tools. They were also designed to collect inputs to develop an appropriate decision support tool for crop planting decisions. A ‘Key to dryland planting decisions’ for farmers in southwest Queensland was developed and the personal experiences and lessons that the author has learnt through the course of this research are reported. The thesis enhances the understanding of farmers’ attitudes to risk, contemporary risk management strategies, and decision supports tools used in agriculture. This research has contributed to knowledge in the following ways. It has presented a methodological framework for doing research of this type. This study has identified the different sources of risk faced by dryland farmers in southwest Queensland and the practical risk management strategies they employ. The research has introduced those farmers to some existing risk management and decision support tools. The research has contributed more specifically to improving their management decisions by developing a decision support tool that could help dryland farmers in southwest Queensland make better informed and more appropriate planting decisions in the very uncertain and risky conditions with which they have to cope.

Architecting e-business applications is a complex design activity. This is mainly due to the numerous architectural decisions to be made, including the selection of alternative technologies, software components, design strategies, patterns, standards, protocols, platforms, etc. Further complexities arise due to the fact that these alternatives often vary considerably in their support for different quality attributes. Moreover, there are often different groups of stakeholders involved, with each having their own quality goals and criteria. Furthermore, different architectural decisions often include interdependent alternatives, where the selection of one alternative for one particular decision impacts the selections to be made for alternatives from other different decisions. There have been several research efforts aiming at providing sufficient mechanisms and tools for facilitating the architectural evaluation and design process. These approaches, however, address architectural decisions in isolation, where they focus on evaluating a limited set of alternatives belonging to one architectural decision. This has been the primary motivation behind the development of the Architectural DEcision-Making Support (ADEMS) framework, which basically aims at supporting stakeholders and architects during the architectural decision-making process by helping them determining a suitable combination of architectural alternatives. ADEMS framework is an iterative process that leverages rigorous quantitative decision-making techniques available in the literature of Management Science, particularly Multiple Attribute Decision-Making (MADM) methods and Integer Programming (IP). Furthermore, due to the number of architectural decisions involved as well as the variety of available alternatives, the architecture design space is expected to be huge. For this purpose, a query language has been developed, known as the Architecture Query Language (AQL), to aid architects in exploring and analyzing the design space in further depth, and also in examining different ???what-if??? architectural scenarios. In addition, in order to support leveraging ADEMS framework, a support tool has been implemented for carrying out the sophisticated set of mathematical computations and comparisons of the large number of architectural combinations, which might otherwise be hard to conduct using manual techniques. The primary contribution of the tool is in its help to identify, evaluate, and rank all potential combinations of alternatives based on their satisfaction to quality preferences provided by the different stakeholders. Finally, to assess the feasibility of ADEMS, three different case studies have been conducted relating to the architectural evaluation of different e-business and enterprise applications. Results obtained for the three case studies were quite positive as they showed an acceptable accuracy level for the decisions recommended by ADEMS, and at a reasonable time and effort costs for the different system stakeholders.

Smart computer algorithms and signal processing techniques have led to rapid development in the field of patient monitoring. Accelerated growth in the field of medical science has made data analysis more demanding and thus the complexity of decision-making procedures. Anaesthetists working in the operating theatre are responsible for carrying out a multitude of tasks which requires constant vigilance and thus a need for a smart decision support system has arisen. It is anticipated that such an automated decision support tool, capable of detecting pathological events can enhance the anaesthetist’s performance by providing the diagnostic information to the anaesthetist in an interactive and ergonomic display format. The main goal of this research was to develop a clinically useful diagnostic alarm system prototype for monitoring pathological events during anaesthesia. Several intelligent techniques, fuzzy logic, artificial neural networks, a probabilistic alarms and logistic regression were explored for developing the optimum diagnostic modules in detecting these events. New real-time diagnostic algorithms were developed and implemented in the form of a prototype system called real time – smart alarms for anaesthesia monitoring (RT-SAAM). Three diagnostic modules based on, fuzzy logic (Fuzzy Module), probabilistic alarms (Probabilistic Module) and respiration induced systolic pressure variations (SPV Module) were developed using MATLABTM and LabVIEWTM. In addition, a new data collection protocol was developed for acquiring data from the existing S/5 Datex-Ohmeda anaesthesia monitor in the operating theatre without disturbing the original setup. The raw physiological patient data acquired from the S/5 monitor were filtered, pre-processed and analysed for detecting anaesthesia related events like absolute hypovolemia (AHV) and fall in cardiac output (FCO) using SAAM. The accuracy of diagnoses generated by SAAM was validated by comparing its diagnostic information with the one provided by the anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and RT-SAAM’s diagnoses. In retrospective (offline) analysis, RT-SAAM that was tested with data from 18 patients gave an overall agreement level of 81% (which implies substantial agreement between SAAM and anaesthetist). RT-SAAM was further tested in real-time with 6-patients giving an agreement level of 71% (which implies fair level of agreement). More real-time tests are required to complete the real-time validation and development of RT-SAAM. This diagnostic alarm system prototype (RT-SAAM) has shown that evidence based expert diagnostic systems can accurately diagnose AHV and FCO events in anaesthetized patients and can be useful in providing decision support to the anaesthetists.

Thesis (Ph.D.)--University of Wollongong, 2005. / Typescript. Includes bibliographical references: leaf 241-259.

Thesis (M.S.)--University of Michigan, 2006. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 66-80).

Thesis (M.S.)--University of Michigan, 2006. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 66-80).

Dissertation (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.