Because of limited tester time and memory, a primary goal of digital circuit manufacture test generation is to create compact test sets. Test generation programs that use Ordered Binary Decision Diagrams (OBDDs) as their primary functional representation excel at this task. Unfortunately, the use of OBDDs limits the application of these test generation programs to small circuits. This is because the size of the OBDD used to represent a function can be exponential in the number of the function's switching variables. Working with these functions can cause OBDD-based programs to exceed acceptable time and memory limits. This research proposes using Ordered Partial Decision Diagrams (OPDDs) instead as the primary functional representation for test generation systems. By limiting the number of vertices allowed in a single OPDD, complex functions can be partially represented in order to save time and memory. An OPDD-based test generation system is developed and techniques which improve its performance are evaluated on a small benchmark circuit. The new system is then demonstrated on larger and more complex circuits than its OBDD-based counterpart allows.

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.

Objective: To explore the decision making approaches used by nurses who chose to work in critical care and factors influencing the process of decision making. Design and methods Qualitative descriptive methods using semi-structured interviews with nurses who had chosen to work in critical care within the past year. Results Ten nurses weighed staying in their current positions with moving to critical care. Two nurses considered two or more specialty units. The nurses used rational-intuitive and satisficing decision making approaches in making their employment decision. Limited knowledge and unrealistic expectations of work life in critical care, pressure from others, and inadequate professional support made nurses’ employment decisions more difficult. Social support, personal values for growth and learning, and paid educational incentives within employment offers facilitated their employment decisions. Conclusions Several factors negatively influenced nurses’ decision making approaches to making an employment decision. Findings revealed the need for decision support interventions focused on making employment choices for nurses.

Objective: To explore the decision making approaches used by nurses who chose to work in critical care and factors influencing the process of decision making. Design and methods Qualitative descriptive methods using semi-structured interviews with nurses who had chosen to work in critical care within the past year. Results Ten nurses weighed staying in their current positions with moving to critical care. Two nurses considered two or more specialty units. The nurses used rational-intuitive and satisficing decision making approaches in making their employment decision. Limited knowledge and unrealistic expectations of work life in critical care, pressure from others, and inadequate professional support made nurses’ employment decisions more difficult. Social support, personal values for growth and learning, and paid educational incentives within employment offers facilitated their employment decisions. Conclusions Several factors negatively influenced nurses’ decision making approaches to making an employment decision. Findings revealed the need for decision support interventions focused on making employment choices for nurses.

Although everyone knows that rational thinking is so important, people still always make some decisions by irrational emotion. Previous studies of making decisions suggest that decision makers mainly maximize their own profit based on economical principle and also with the ability to assess every decision selection¡¦s benefit and cost. They also indicate that most management level decision makers may use analytical, rational and logical ways to analyze every problem they encounter and when it refers to decision making. In our daily life, what do we exactly conform to the suggestion of rational decision making theories? Is irrational decision making with emotional involvement consistent with our daily choice and important decision? People make all kinds of decisions no matter they are important or easy decision for himself /herself or for the others. In other word, decisions build up and make up our life, so it is important to understand what kind of factors affects our step of decision making. Through reviewing of the essays and theses, induction of the interview information, we realize rational and irrational factors intervene into the process of decision making. In terms to decision making, we choose ¡§marriage¡¨ as our research scope, discuss rational and irrational factor affect decision makers¡¦ thinking through the four step of decision making: information collection, development of programs, selection of program and implementation of program. In the step of information collection, individual will search for rational consideration and irrational element. Decision maker will make the decision and influence by irrational factors which include affection, emotion and feeling. When it comes to implementation, some ideas emerge in their mind.

Because of limited tester time and memory, a primary goal of digital circuit manufacture test generation is to create compact test sets. Test generation programs that use Ordered Binary Decision Diagrams (OBDDs) as their primary functional representation excel at this task. Unfortunately, the use of OBDDs limits the application of these test generation programs to small circuits. This is because the size of the OBDD used to represent a function can be exponential in the number of the function's switching variables. Working with these functions can cause OBDD-based programs to exceed acceptable time and memory limits. This research proposes using Ordered Partial Decision Diagrams (OPDDs) instead as the primary functional representation for test generation systems. By limiting the number of vertices allowed in a single OPDD, complex functions can be partially represented in order to save time and memory. An OPDD-based test generation system is developed and techniques which improve its performance are evaluated on a small benchmark circuit. The new system is then demonstrated on larger and more complex circuits than its OBDD-based counterpart allows.

New conceptual work in the judgment and decision-making research arena has suggested a nonconsequentialist perspective to decision-making. From this perspective, an emphasis is placed on emotions during the decision-making process, specifically positing that concurrent emotions may lead to decisions that are nonconsequentialist in nature. In the current study I develop the Nonconsequentialist Decision-Making Model (NDMM) and include indecisiveness as a vital construct in the model. In tune with much new research on emotions during the decision-making process, I examine how being indecisive is a product of negative concurrent emotions, and how indecisiveness affects the decision-making process. Using a natural decision-making setting, the current study had participants discuss the "biggest" decision they are currently facing in their lives. Data was collected regarding indecisiveness, nonconsequentialist dysfunctional decisional coping behavior, and decision difficulty. The findings show strong support for the NDMM and the nonconsequentialist perspective. Furthermore, the indecisiveness construct was measured successfully and showed to be a critical part of the decision-making process when dealing with difficult decisions.

<p>Historically, decision support systems have been used in organizations to facilitate better decisions. Business Intelligence has become important in recent years because the business environment is more complex and changes faster than ever before. Organizations have started to realize the value of existing information in operational, managerial, and strategic decision making. By using analytical methods and data warehousing, decision support can now be used in a flexible way and assist decision makers in decision making processes. Increasing investments in Business Intelligence indicate that it can bring value to organizations. Benefits such as the ability to access relevant and timely decision support when it is needed can be of tremendous value when the use of existing information has become more a question of survival or bankruptcy for an organization, than profit or loss. Thus, it would be interesting to see how decision support and decision making have changed in organizations after implementing a Business Intelligence system. The purpose of this thesis is to investigate if and how Business Intelligence has changed decision support and decision making processes.A deductive approach using a qualitative method has been used with semi-structured elite interviews. The thesis aims to investigate the manufacturing industry located in the Jönköping region in Sweden. The interviewed organizations are Husqvarna AB, Fläkt Woods AB, Myresjöhus AB, and Kinnarps AB. Our analysis shows positive effects of Business Intelligence in organizations with improvements of decision support due to timeliness, accessibility, quality, and better control of organizational information. As improvements in decision support has occurred, decision making has become better. Complicated problems are now easier to interpret by decision makers. Our research also concludes that intuition still has a major impact in decision making processes.</p>

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Nita Lewis Miller, Saverio Manago, Matt Chesney. Includes bibliographical references (p. 118-120). Also available online.

Dissertation (M.A. (Department of Dogmatics and Christian Ethics))--University of Pretoria, 2010. / Includes bibliographical references.