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In order to investigate the use of fuzzy logic in decision-support systems (DSS) for dairy cattle breeding, a first-generation prototype software system was developed. The objectives were to determine the advantages and limitations of fuzzy logic for this type of application, and to establish a basis for the development of more complete DSS in the future. The goal of the prototype DSS was to make culling decisions on the basis of monthly production data. An analysis of the development process of this prototype demonstrated the importance of creating a thorough methodology for the elicitation and implementation of knowledge. A framework for the development of fuzzy decision-support systems was established, consisting of four phases: the project groundwork phase, elicitation of knowledge from the expert, implementation of that knowledge, and system validation. In this framework, it is proposed that, in the case of multiple experts, knowledge can be amalgamated or aggregated. Once this framework was established, a second-generation prototype DSS was developed. Contrary to the first-generation prototype, where the encoded expertise was limited to three experts from the same domain, the second-generation prototype considered the knowledge of two individuals from each of three domains (Dairy researchers, Producers, and Dairy herd improvement specialists). An aggregation approach was used which involved the development and maintenance of separate modules, each containing the compiled expertise of one of the six experts.

This thesis evaluates how industrial product firms screen new product ideas. Its aims are to investigate: (1) the evaluative dimensions used when assessing proposals, (2) how these criteria relate to screening decisions and (3) how new product environments--market, product and firm--affect this decision model. / A two stage research approach involved: (1) the establishment of new product screening attributes relevant to industrial product firms and (2) a reassessment by managers of recently screened new product ideas (accepts and rejects) in terms of these attributes. / Managers' new product screening decisions supported the hypothesized decision model. Nine of the eleven composite dimensions characterizing new industrial products, were key determinants in explaining screening decisions. These dimensions were further classified according to: (1) four basic criteria categories, (2) the basic risk/return investment model and (3) the screening role played by each. Moreover, the project environment, particularly its market, had a significant influence on the screening model. The results have implications for developing operationally more effective screening approaches.

The high school to college transition may be especially influential in creating feelings of social loneliness in adolescents. Failure to establish social structures may lead to feelings of loneliness and an increased need for social approval from their peers in the new environment. It was therefore predicted that the more lonely a student is, the more likely he/she would look for approval from others and the more likely he/she would acquiesce to a decision to participate in unethical actions proposed by peers. This prediction was tested in a sample of 158 female and 133 male college students. The evidence suggests that participants who were the most lonely were most likely to report a willingness to engage in unethical behaviors when encouraged by others. However, contrary to expectations, the need for approval did not mediate the relationship between loneliness and unethical behavior. Gender differences were found in the endorsement of unethical behaviors. Male participants were more likely to report a willingness to engage in unethical acts than were female participants. / Department of Psychological Science

One employs the mathematical analysis of decision making when the state of nature is uncertain but further information about it can be obtained by experimentation. Bayesian Decision Theory concerns practical problems of decision making under conditions of uncertainty and also requires the use of statistical and mathematical methods.In this thesis, some basic risk sharing and group decision concepts are provided. Risk is the expected value of the Loss Function of Bayesian Estimators. Group decisions consider situations in which the individuals need to agree both on utilities for consequences and on conditional probability assessments for different experimental outcomes. / Department of Mathematical Sciences

Savo tyrime siekėme patikrinti sprendimo priėmimo grupėje struktūros modelį, kuris padėtų pagerinti sprendimų priėmimo grupėje proceso kokybę bei sprendimo priimtinumą grupės nariams. Siūlomas sprendimų priėmimo grupėje struktūros modelis remiasi procedūrinio racionalumo principais (Simon, 2003), Galotti (2002) siūlomu individualaus sprendimo struktūravimo modeliu bei multiatributinės naudos teorija. Sprendimo priėmimo proceso kokybę matavome atsižvelgdami į Deal ir Stroebe (1987, cit. pgl. Furnham) išskirtus trūkumus, būdingus sprendimų priėmimui grupėje, t.y. socialinio dykinėjimo efektą, vertinimo baimę bei produktyvumo blokavimo efektą. Tyrimo metu nustatėme, kad: • Tiriamiesiems, kurie priimdami sprendimą taikė struktūros modelį, priimtas sprendimas priimtinesnis nei tiems, kurie šio modelio netaikė. • Tiriamieji, kurie priimdami sprendimą grupėje taikė struktūros modelį, buvo labiau linkę bendradarbiauti nei tie, kurie šio modelio netaikė. • Bendra sprendimo priėmimo grupėje kokybė, lyginat struktūruoto ir nestruktūruoto sprendimų priėmimo grupes, nesiskyrė. / In this paper we wanted to offer such group decision making structure, which could improve the decision making process quality and decision acceptance. The structured decision making is based upon procedural rationality principles (Simon, 2003), Galotti (2002) individual decision structuring and Multi-attribute utility theory. In decision making process quality assessment we paid attention to presence of social loafing, evaluation apprehension and production blocking effects. Results of this study shows, that: • There is a significant difference in decision acceptance between structured and unstructured decision making groups. • There is no significant difference in total decision making process quality between structured and unstructured decision making groups. • There is a significant difference in cooperation activity between structured and unstructured decision making.

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.

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Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 201-208). / Microfiche. / xv, 208 leaves, bound ill., col. maps 29 cm