Luo, Xueying. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 108-120). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract and appendix also in Chinese.

This thesis aims to describe how enterprises can approach and reach older citizens in order to sell new technology products. For that they need information about aspects affecting elderlies purchase decisions regarding these kind of products. The study is based on 13 qualitative interviews where we have applied semi structured interviews and observations of the elderlies behaviour towards an Ipad which we used as a study object during the interviews. The results of this study shows several aspects which affect the elderlies purchase decision regarding new technology but it all comes down to one matter, lack of knowledge. In order to overcome that obstacle, enterprises need to modify their communication strategy. They will be needing information about elderlies preferences and their self-image, meaning how they perceive themselves which is connected to how receptive they are towards new technology. They will also need to communicate information about the products area of use, the quality of the product, that the product is user-friendly and that there is impeccable support and education available. All these aspects have shown to have huge impact on the purchase decision. Enterprises need to assure the elderly segment that they will be able to use these products without complications. Since the social environment, especially the younger segment has shown to have an impact on the elderlies purchase decisions, enterprises must also turn the attention and start communicating this information towards them also.

Processing decision information is a vital part of Computer Science fields in which pattern recognition problems arise. Decision information can be generalized as alternative decisions (or classes), attributes and attribute values, which are the basis for classification. Different classification approaches exist, such as decision trees, decision tables and Naïve Bayesian classifiers, which capture and manipulate decision information in order to construct a specific decision model (or classifier). These approaches are often tightly coupled to learning strategies, special data structures and the special characteristics of the decision information captured, etc. The approaches are also connected to the way of how certain problems are addressed, e.g., memory consumption, low accuracy, etc. This situation causes problems for a simple choice, comparison, combination and manipulation of different decision models learned over the same or different samples of decision information. The choice and comparison of decision models are not merely the choice of a model with a higher prediction accuracy and a comparison of prediction accuracies, respectively. We also need to take into account that a decision model, when used in a certain application, often has an impact on the application's performance. Often, the combination and manipulation of different decision models are implementation- or application-specific, thus, lacking the generality that leads to the construction of decision models with combined or modified decision information. They also become difficult to transfer from one application domain to another. In order to unify different approaches, we define Decision Algebra, a theoretical framework that presents decision models as higher order decision functions that abstract from their implementation details. Decision Algebra defines the operations necessary to decide, combine, approximate, and manipulate decision functions along with operation signatures and general algebraic laws. Due to its algebraic completeness (i.e., a complete algebraic semantics of operations and its implementation efficiency), defining and developing decision models is simple as such instances require implementing just one core operation based on which other operations can be derived. Another advantage of Decision Algebra is composability: it allows for combination of decision models constructed using different approaches. The accuracy and learning convergence properties of the combined model can be proven regardless of the actual approach. In addition, the applications that process decision information can be defined using Decision Algebra regardless of the different classification approaches. For example, we use Decision Algebra in a context-aware composition domain, where we showed that context-aware applications improve performance when using Decision Algebra. In addition, we suggest an approach to integrate this context-aware component into legacy applications.

Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.

Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 146-150). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Arizona, 1997. / eContent provider-neutral record in process. Description based on print version record.

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Computerized decision aids are powerful tools to assist with decision-making. Decision models are designed to incorporate and analyze available data in order to present a recommended solution to a problem. Business decision makers, including accountants, have much to gain from integrating decision support technology with their own skills and experience. Several studies have determined that there are many instances in which these decision aids perform favorably to human decision-makers. Despite this fact, studies have shown that reliance upon these aids is incomplete, even when they process data in a highly efficient manner. On the other hand, decision aids have limitations. If such a decision support system is not updated to match changing conditions, relying on the aid can lead to suboptimal decision-making. This study uses a laboratory experiment involving a managerial accounting task: prediction of manufacturing overhead costs. In the experimental scenario, a decision support system's recommended solutions become inaccurate due to a shift in environmental conditions. The first research objective is to determine whether subjects rely on the aid's advice before this change and, to their detriment, after the change. The second research objective is to examine whether the feedback environment, the timing of the decision aid's inclusion into the task, or the inherent confidence level of the task participant affect the tendency to rely on the aid in both of these environmental conditions. The results of the study provide evidence that decision-makers rely on decision aids, and are susceptible to over-reliance on them. These findings add to the results of prior studies that only examine a single trial task. Additionally, it is determined that the timing of a decision aid's recommendation can affect the degree to which it is relied upon. Next, there is evidence that feedback environment can help reliance and mitigate over-reliance. There is no evidence that task confidence affects reliance. Lastly, decision aids result in longer amounts of time used to complete the task. / Ph. D.