No description available.

No description available.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Thesis (M. Sc.)--University of Alberta, 1997. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M. Sc.)--University of Alberta, 1997. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

No description available.

The thesis focuses on in two vital and interrelated aspects of modelling design support systems, they are: how innovative solutions may arise, and the knowledge-base's extension and maintenance. The dilemma 'reproduction versus creativity' is identified as one of the main deadlocks that the design methods' debate, research in Computer Aided Architectural Design, CAAD, and Artificial Intelligence, AI, have faced in the last thirty years. A hybrid approach is then proposed as a means of overcoming these difficulties, where a rudimentary evolving design support environment is developed. It draws inspiration from three areas of Artificial Intelligence: knowledge-base systems, connectionist models, and case-based reasoning (CBR). However, it differs fundamentally from conventional knowledge-base systems, connectionist models and CBR tools, in its architecture, although strongly inspired by these underlying theories. The main benefits and contributions of this hybrid system are an incremental selfextending feature able to minimise substantially the dependency on knowledge engineer intervention, and an interactive support to innovation by augmenting the designer's creativity.

Fung Chi Tim. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 53). / Abstract / Chapter I. --- Introduction --- p.1 / Chapter II. --- Objective --- p.4 / Chapter III. --- Methodology / Decision Methodology --- p.6 / Sensitivity Analysis and Re-iteration --- p.14 / Ease of use of the system --- p.22 / Chapter IV. --- Test Result --- p.26 / Chapter V. --- Future Research Direction --- p.28 / Chapter VI. --- Conclusion --- p.32 / Chapter VII. --- Appendix A Description of System --- p.35 / Chapter VIII. --- Appendix B User's Guide --- p.44 / Figures --- p.50 / References --- p.53