Thesis (Ph. D.)--Virginia Commonwealth University, 2006. / Prepared for: School of Social Work. Bibliography: leaves 278-297. Also available online via the Internet.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains vi, 65 p. Includes abstract. Includes bibliographical references (p. 45-50).

Risk based audit (RBA) approaches represent a major trend in current audit methodology. The approach is based on risk analysis used to identify business strategy risk. The RBA has created a new set of research issues that need investigation. In particular, this approach has important implications for risk identification and risk assessment. The success of the RBA approach is contingent on understanding what factors improve or interfere with the accuracy of these risk judgments. I examine how budget constraints and decision aid use affect risk identification and risk assessment. Unlike previous budget pressure studies, I cast budget constraints as a positive influence on auditors. I expect more stringent budget constraints to be motivating to the auditor as they provide a goal for the auditor to achieve. I also expect budget constraints to induce feelings of pressure leading to the use of time-pressure adaptation strategies. When auditors have use of a decision aid, they take advantage of these motivational goals and/or use beneficial adaptive strategies. Overall, I find that auditor participants tend to be more accurate when identifying financial statement risks compared to business risks. Budget constraints have no effect on risk identification for financial or business risks; they also have no effect on financial risk assessments. On the other hand, business risk assessments are improved by implementing more stringent budget constraints, but only when a decision aid is also provided. Budget constraints can affect performance through a goal theory route or a time-pressure adaptation route. I investigate the paths through which budget constraints improve business risk assessments under decision aid use. I find that budget constraints directly affect performance, supporting a goal theory route. However, I do not find that budget constraints are mediated by perceived budget pressure as expected. Auditors appear to use a positive adaptive strategy to respond to perceived budget pressure, however perceived budget pressure is not induced by providing a more stringent budget.

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. The fundamental viewpoint that permeates this thesis is that RE decision-making can be substantially improved by RE decision support systems (REDSS) based on the actual needs of RE decision-makers as well as the actual generic human decision-making activities that take place in the RE decision processes. Thus, a first step toward better decision support in requirements engineering is to understand complex decision situations of decision-makers. In order to gain a holistic view of the decision situation from a decision-maker’s perspective, a decision situation framework has been created. The framework evolved through an analysis of decision support systems literature and decision-making theories. The decision situation of RE decision-makers has been studied at a systems engineering company and is depicted in this thesis. These situations are described in terms of, for example, RE decision matters, RE decision-making activities, and RE decision processes. Factors that affect RE decision-makers are also identified. Each factor consists of problems and difficulties. Based on the empirical findings, a number of desirable characteristics of a visionary REDSS are suggested. Examples of characteristics are to reduce the cognitive load, to support creativity and idea generation, and to support decision communication. One or more guiding principles are proposed for each characteristic and available techniques are described. The purpose of the principles and techniques is to direct further efforts concerning how to find a solution that can fulfil the characteristic. Our contributions are intended to serve as a road map that can direct the efforts of researchers addressing RE decision-making and RE decision support problems. Our intention is to widen the scope and provide new lines of thought about how decision-making in RE can be supported and improved.

Context: Rectal cancer treatment decisions involve tradeoffs between outcomes like living with a permanent stoma versus long-term bowel dysfunction. The needs of rectal cancer patients and practitioners to partake in shared decision making are unknown. For such a complex decision, a patient decision aid that prepares patients to make informed, values-based decisions is warranted. Methods: 1) A systematic review, to characterize the prevalence of long-term dysfunction 2) Needs assessments, conducted with rectal cancer patients and practitioners, 3) Development of a decision aid. Results: 1) Significant variability exists in reporting rectal cancer outcomes. The rate of bowel dysfunction is high. 2) Rectal cancer patients recall little of the outcomes discussed preoperatively. They do not perceive having any surgical options. Practitioners are inconsistently engaging patients in shared decision-making. 3) A patient decision aid was developed that a) incorporated systematic review results and; b) addressed the needs, barriers and facilitators raised. Conclusions: Shared decision-making in rectal cancer surgery is limited. A decision aid to improve patient decision-making was developed.

This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and challenging areas of Operations Research. Every day people make many decisions: today's decisions impact tomorrow's and tomorrow's will impact the ones made the day after. Problems in Engineering, Science, and Business often pose similar challenges: a large number of options and uncertainty about the future. MDP is one of the most powerful tools for solving such problems. There are several standard methods for finding optimal or approximately optimal policies for MDP. Approaches widely employed to solve MDP problems include value iteration and policy iteration. Although simple to implement, these approaches are, nevertheless, limited in the size of problems that can be solved, due to excessive computation required to find close-to-optimal solutions. My thesis proposes a new value iteration and modified policy iteration methods for classes of the expected discounted MDPs and average cost MDPs. We establish a class of operators that can be integrated into value iteration and modified policy iteration algorithms for Markov Decision Processes, so as to speed up the convergence of the iterative search. Application of these operators requires a little additional computation per iteration but reduces the number of iterations significantly. The development of the acceleration operators relies on two key properties of Markov operator, namely contraction mapping and monotonicity in a restricted region. Since Markov operators of the classical value iteration and modified policy iteration methods for average cost MDPs do not possess the contraction mapping property, for these models we restrict our study to average cost problems that can be formulated as the stochastic shortest path problem. The performance improvement is significant, while the implementation of the operator into the value iteration is trivial. Numerical studies show that the accelerated methods can be hundreds of times more efficient for solving MDP problems than the other known approaches. The computational savings can be significant especially when the discount factor approaches 1 and the transition probability matrix becomes dense, in which case the standard iterative algorithms suffer from slow convergence.

This research focuses on Markov Decision Processes (MDP). MDP is one of the most important and challenging areas of Operations Research. Every day people make many decisions: today's decisions impact tomorrow's and tomorrow's will impact the ones made the day after. Problems in Engineering, Science, and Business often pose similar challenges: a large number of options and uncertainty about the future. MDP is one of the most powerful tools for solving such problems. There are several standard methods for finding optimal or approximately optimal policies for MDP. Approaches widely employed to solve MDP problems include value iteration and policy iteration. Although simple to implement, these approaches are, nevertheless, limited in the size of problems that can be solved, due to excessive computation required to find close-to-optimal solutions. My thesis proposes a new value iteration and modified policy iteration methods for classes of the expected discounted MDPs and average cost MDPs. We establish a class of operators that can be integrated into value iteration and modified policy iteration algorithms for Markov Decision Processes, so as to speed up the convergence of the iterative search. Application of these operators requires a little additional computation per iteration but reduces the number of iterations significantly. The development of the acceleration operators relies on two key properties of Markov operator, namely contraction mapping and monotonicity in a restricted region. Since Markov operators of the classical value iteration and modified policy iteration methods for average cost MDPs do not possess the contraction mapping property, for these models we restrict our study to average cost problems that can be formulated as the stochastic shortest path problem. The performance improvement is significant, while the implementation of the operator into the value iteration is trivial. Numerical studies show that the accelerated methods can be hundreds of times more efficient for solving MDP problems than the other known approaches. The computational savings can be significant especially when the discount factor approaches 1 and the transition probability matrix becomes dense, in which case the standard iterative algorithms suffer from slow convergence.

Context: Rectal cancer treatment decisions involve tradeoffs between outcomes like living with a permanent stoma versus long-term bowel dysfunction. The needs of rectal cancer patients and practitioners to partake in shared decision making are unknown. For such a complex decision, a patient decision aid that prepares patients to make informed, values-based decisions is warranted. Methods: 1) A systematic review, to characterize the prevalence of long-term dysfunction 2) Needs assessments, conducted with rectal cancer patients and practitioners, 3) Development of a decision aid. Results: 1) Significant variability exists in reporting rectal cancer outcomes. The rate of bowel dysfunction is high. 2) Rectal cancer patients recall little of the outcomes discussed preoperatively. They do not perceive having any surgical options. Practitioners are inconsistently engaging patients in shared decision-making. 3) A patient decision aid was developed that a) incorporated systematic review results and; b) addressed the needs, barriers and facilitators raised. Conclusions: Shared decision-making in rectal cancer surgery is limited. A decision aid to improve patient decision-making was developed.

The current study provides evidence that individuals tend to adopt an integrative choice strategy when making sequential decisions under conditions of uncertainty. This contrasts with prior literature which proposes that decisions are made one at a time in isolation from one another (Camerer et al., 1997). By creating an experimental work task where only wage quality and feedback are manipulated, the resulting changes in intertemporal substitution between work and leisure are observed. In Experiments 1 3, a positive relationship between wages and time spent working that did not depend on task experience was observed. These results suggest that decisions are being made in consideration of other decisions, as isolated decisions would yield a negative relationship between wages and time spent working. In Experiment 4 these results were mitigated by the difficulty in differentiating between low and high wage quality days. These findings are taken to suggest that the results of prior studies are primarily due to self-control issues that subjects faced, which are not present in the present study.

In the domain of reasoning and decision making, some dual-process theorists have suggested that people are highly efficient at detecting conflicting outputs engendered by competing intuitive and analytic processes (De Neys & Glumicic, 2008; De Neys, Vartanian & Goel, 2008). For example, De Neys and Glumicic (2008) demonstrated that participants’ reason longer about problems that are characterized by a conflict between a stereotypical personality description and a base-rate probability of group membership. Crucially, this increase occurred even when participants gave the nominally erroneous stereotypical response (i.e., “neglecting” the base-rate probability), indicating that their participants detected that there was a conflict and, as a result, engaged in slow, analytic processing to resolve it. However, this finding, and much of the additional support for the efficient conflict detection hypothesis, has come from base-rate neglect problems constructed with probabilities (e.g., 995 doctors and 5 nurses) that were much more extreme than typically used in studies of base-rate neglect. I varied the base-rate probabilities over five experiments and compared participants’ response time for conflict problems with non-conflict problems. It was demonstrated that the integral increase in response time for stereotypical responses to conflict problems was fully mediated by extreme probabilities. I conclude that humans are not as efficient at detecting when they are engaging in biased reasoning as De Neys and colleagues have claimed.