No description available.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

No description available.

In modern organizations where managers must constantly be dealing with an overload of information, it is often observed that participants in group decision processes either are not clearly aware of their specific preferences or that they are not capable of properly formulating those preferences. When this happens, inconsistent or incomplete expression of personal preferences and their use in decision making may lead to an unjustifiable outcome for the group. Due to this problem, the strengths and effectiveness of GDSS-supported group meetings may, in some situations, not be apparent. This dissertation develops a new approach to supporting group decision making, focusing on preference knowledge of individual participants in a group. A system architecture for the design of an MCDM (Multiple Criteria Decision Making) GDSS which facilitates the process of eliciting, formulating, utilizing, aggregating, and analyzing preferences for individuals within groups is presented. The architecture integrates multi-criteria decision making paradigms with a group decision support environment. A prototype has been developed in order to demonstrate the design feasibility of an architecture that centers around four phases of choice making: alternative generation, preference specification, alternative evaluation, and preference aggregation. The prototype is designed to support managerial choice and judgment processes in collaborative meetings. The intended problem domain of the model is semi-structured managerial decisions for which decision variables (attributes) can be represented in quantitative terms to some extent, yet for which evaluation of alternatives requires a high degree of intuition and personal analysis. The process of prototyping the proposed architecture and the results from a qualitative study have provided some instructive conclusions relating to MCDM GDSS design: (1) support for human choice strategies can be integrated into a GDSS, (2) appropriate management of preferences of group participants will facilitate collaborative decision processes, (3) hierarchical decomposition of a decision problem can provide structure to a problem and thereby reduce problem complexity, and (4) managerial decisions are appropriate problems to which the current approach can be applied.

Master of Science / Department of Psychological Sciences / Christopher Lake / While hiring decisions are a frequent organizational occurrence that can substantially impact the decision maker, the organization, and/or society as a whole, employees do not always make optimal hiring decisions. This failure to make optimal decisions may occur because employees do not utilize deliberative processes (e.g., systematically gathering information, evaluating choice alternatives, taking time to decide etc.). Accordingly, the goal of the present study was to propose an integrative model of some antecedents and consequences of deliberative decision making within personnel selection. Data gathered from 322 hiring managers indicated that when managers felt accountable for their hiring decisions and possessed a deliberative decision making style, they were more likely to report making hiring decisions in a deliberative manner. This use of deliberation was, in turn, associated with high quality decisions (i.e., low regret, high satisfaction, and high performance ratings of the person that was hired). The results also indicated the relationship between accountability and decision quality was mediated by deliberative processes. These findings were consistent across multiple hiring decisions. Importantly, these results did not emerge when intuitive processes/style were examined. Collectively, these results help establish the ecological validity of various theories of decision making and specify that deliberative processes are associated with high quality selection decisions. These results can be leveraged by organizations who are interested in encouraging employees to utilize deliberative processes. Given the benefits of deliberative processes, these results may also be leveraged by workers who are interested in achieving higher task performance in their jobs.

The subject of enquiry is the variation seen in the results of a specific set of studies about methods for training people’s judgment. This review attempts to synthesise the studies’ findings, and tests hypotheses about the causes of the variation. Research questions ask if variation is attributable to differences in participant characteristics, different aspects of judgment having been targeted, different tasks having been performed or different training strategies having been used. Relevant literature was reviewed, and studies that reported a method for training an aspect of judgment were selected for further quantitative analysis if at least two groups had been randomly selected from a larger set of human adults, one of which received training that another did not, and where, during the test phase, members of no group had access to tools or resources, performed tasks, or received feedback which members of another group did not. A meta-analysis of statistical data from 39 published studies was conducted. The findings are interpreted as indicating variation in the effect of training is attributable to differences in task type and differences in training strategy. The effect of training is greatest in the studies that have diagnostic tasks (p<0.05). The studies that trained participants with examples have, on average, greater effect sizes than studies that did not (p<0.05). Implications, limitations, and avenues for further research are discussed. It is concluded that the findings indicate that different tasks and different training strategies account for a significant proportion of the variation in training effect seen between the selected studies.

This research explores the hypothesis that methods from decision theory and machine learning can be combined to provide practical solutions to current manufacturing control problems. This hypothesis is explored by developing an integrated approach to solving one manufacturing problem - the optimization of die-level functional test. An integrated circuit (IC) is an electronic circuit in which a number of devices are fabricated and interconnected on a single chip of semiconductor material. According to current manufacturing practice, integrated circuits are produced en masse in the form of processed silicon wafers. While still in wafer form the ICs are referred to as dice, an individual IC is called a die. The process of cutting the dice from wafers and embedding them into mountable containers is called packaging. During the manufacturing process the dice undergo a number of tests. One type of test is die-level functional test (DLFT). The conventional approach is to perform DLFT on all dice. An alternative to exhaustive die-level testing is selective testing. With this approach only a sample of the dice on each wafer is tested. Determining which dice to test and which to package is referred to as the "optimal test problem", and this problem provides the application focus for this research. In this study, the optimal test problem is formulated as a partially observable Markov decision model that is evaluated in real time to provide answers to test questions such as which dice to test, which dice to package, and when to stop testing. Principles from decision theory (expected utility, value of information) are employed to generate tractable decision models, and machine learning techniques (Expectation Maximization, Gibbs Sampling) are employed to acquire the real-valued parameters of these models. Several problem formulations are explored and empirical tests are performed on historical test data from Hewlett-Packard Company. There are two significant results: (1) the selective test approach produces an expected net profit in manufacturing costs as compared to the current testing policy, and (2) the selective test approach greatly reduces the amount of testing performed while maintaining an appropriate level of performance monitoring. / Graduation date: 1999

An Engineering Decision Support System, EDSS, was developed using Bayesian mathematics which incorporates knowledge and confidence components observed in alternative-criterion pair decision making. The separation of knowledge and confidence has been previously unaccounted for in decision-making methods. EDSS provides decision support to individuals or teams which must make choices between alternatives using alternative-criterion pair evaluations. Further, EDSS was instanciated into computer software. The EDSS decision support system was statistically tested using two variables, mechanical experience of the participants and the use of a decision method, at two different levels and in a replicated factorial experiment. The experiment consisted of teams of subjects solving a simple mechanical design problem. Data from the experiment was collected for eighteen different metrics in four categories. This research reports on each of eighteen metrics using the hypothesis that the use of EDSS will show improvements in, or positive impact on, the following four categories: the decision making productivity of idea processing, the decision-making process, the perception of the decisions made by the decision makers, and the ease of use of a computer decision support tool. Statistical results of the experiment showed that EDSS successfully matched ad-hoc and Pugh's decision matrix performance for sixteen of the eighteen metrics and statistically exceeded the remaining two. These two metrics are, the conduction of more evaluations of alternative-criterion pairs ,and increased problem understanding. This research also shows that a new alternative-criterion pair evaluation method has been successfully created that provides for: - A separation of knowledge and confidence in the Belief Model of decision making. - Decision results without complete evaluation of all alternative-criterion pairs. - Aggregation of preferences from team members. - A convenient means for investigating decision improvements. / Graduation date: 1997

Thesis (Ph. D.)--Ohio State University, 1974. / Includes vita. Includes bibliographical references (leaves 363-372). Available online via OhioLINK's ETD Center

Thesis (Ph. D.)--University of Rhode Island, 2006. / Typescript. Includes bibliographical references (leaves 118-126).