Typescript (photocopy). / Digitized by Kansas Correctional Industries

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In decision making problems under uncertainty, Mean Variance Model (MVM) consistent with Expected Utility Theory (EUT) plays an important role in ranking preferences for various alternative options. Despite its wide use, this model is appropriate only when random variables representing the alternative options are normally distributed and the utility function to be maximized is quadratic; both are undesirable properties to be satisfied with actual applications. In this research, a novel methodology has been adopted in developing generalized models that can reduce the deficiency of the existing models to solve large-scale decision problems, along with applications to real-world disputes. More specifically, for eliciting preferences for pairs of alternative options, two approaches are developed: one is based on Mean Variance Model (MVM), which is consistent with Expected Utility Theory (EUT), and the second is based on Analytic Hierarchy Processes (AHP). The main innovation in the first approach is in reformulating MVM to be based on cumulative functions using simulation. Two models under this approach are introduced: the first deals with ranking preferences for pairs of lotteries/options with non-negative outcomes only while the second, which is for risk modelling, is a risk-preference model that concerns normalized lotteries representing risk factors each is obtained from a multiplication decomposition of a lottery into its mean multiplied by a risk factor. Both approximation models, which are preference-based using the determined values for expected utility, have the potential to accommodate various distribution functions with different utility functions and capable of handling decision problems especially those encountered in financial economics. The study then reformulates the second approach, AHP; a new algorithm, using simulation, introduces an approximation method that restricts the level of inherent uncertainty to a certain limit. The research further focuses on proposing an integrated preference-based AHP model introducing a novel approximation stepwise algorithm that combines the two modified approaches, namely MVM and AHP; it multiplies the determined value for expected utility, which results from implementing the modified MVM, by the one obtained from processing AHP to obtain an aggregated weight indicator. The new integrated weight scale represents an accurate and flexible tool that can be employed efficiently to solve decision making problems for possible scenarios that concern financial economics Finally, to illustrate how the integrated model can be used as a practical methodology to solve real life selection problems, this research explores the first empirical case study on Tender Selection Process (TSP) in Kurdistan Region (KR) of Iraq; it is considered as an inductive and a comprehensive investigation on TSP, which has received minimum consideration in the region, and regarded as a significant contribution to this research. The implementation of the proposed model to this case study shows that, for the evaluation of construction tenders, the integrated approach is an appropriate model, which can be easily modified to assume specific conditions of the proposed project. Using simulation, generated data allows creation of a feedback system that can be utilized for the evaluation of future projects in addition to its capability to make data handling easier and the evaluation process less complex and time consuming.

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This work gives an account of an empirical study on the assessment of judgments of individuals in a group. The phenomenon of judgment or decision making in groups appears in various contexts. However we are interested in situations where each member of a group is required to give independently of other members of the group, his most informed and reasoned judgment on a controversial issue. Nonetheless, it is of interest to gain knowledge about the importance of various judgments about the issue, and also of the arguments (or reasons) put forward by the judges to support their judgments. Such situations of judgmentmaking raise methodological problems for collecting judgmental data, and methods, such as, face-to-face discussion or the Delphi method may not be appropriate. To circumvent this problem, a new method called 'Qualitative Controlled Feedback' (Q.C.F.) was developed by Press [13]. Our aim in the present work is to examine the workings of the method by its application to a real world situation. With this aim, judgments (and other data of interest) were collected, using a three-stage Q.C.F. survey, from a random sample group of Faculty and Staff members of the University of British Columbia on a question related to the issue of whether or not the University should build an Indoor Aquatic Center on the campus. The data was analysed from an exploratory viewpoint. It was observed that qualitative controlled feedback creates a good interaction (in the sense of exchanging arguments and reasons) among the group members. Change in judgment occurred as subjects went from one stage to another after having qualitative feedback of information. By comparing with a control group of subjects, it was also found that qualitative feedback was able to produce more rational judgments than without any feedback. The distributions of judgment obtained in this empirical study bear significant implications for decision making. The distributions were found to be bimodal and represented two opposing groups of thought. Other results involve, regression analysis, transition probabilities of judgment change from one stage to another, analysis of judgment change behavior, importance of reasons, effect of non-response on judgment distributions and analysis of confidence in judgment. Finally, it was found that the method of Qualitative Controlled Feedback can be fruitfully applied to situations of practical interest. / Science, Faculty of / Mathematics, Department of / Graduate

The basic problem of decision making is to choose the best alternative from a set of competing alternatives that are evaluated under conflicting criteria. In general, the process is to evaluate decision elements by quantifying the subjective judgments. The Analytic Hierarchy Process (AHP) provides us with a comprehensive framework for solving such problems. As pointed out by Saaty, AHP "enables us to cope with the intuitive, the rational, and the irrational, all at the same time, when we make multicriteria and multiactor decisions". Furthermore, in most organizations decisions are made collectively, regardless of whether the organization is public or private. It is sometimes difficult to achieve consensus among group members, or for all members of a group to meet. The purpose of this dissertation was two-fold: First, we developed a new aggregation method - Minimum Distance Method (MDM) - to support group decision process and to help the decision makers achieve consensus under the framework of AHP. Second, we evaluated the performance of aggregation methods by using accuracy and group disagreement criteria. The evaluations were performed through simulation and empirical tests. MDM • employs the general distance concept, which is very appealing to the compromise nature of a group decision making. • preserves all of the characteristics of the functional equations approach proposed by Aczel and Saaty. • is based on a goal programming model, which is easy to solve by using a commercial software such as LINDO. • provides the weighted membership capability for participants. • allows for sensitivity analysis to investigate the effect of importance levels of decision makers in the group. The conclusions include the following: • Simulation and empirical tests show that the two most important factors in the aggregation of pairwise comparison judgments are the probability distribution of error terms and the aggregation method. • Selection of the appropriate aggregation method can result in significant improvements in decision quality. • The MDM outperforms the other aggregation methods when the pairwise comparison judgments have large variances. • Some of the prioritization methods, such as EV[AA'], EV[A'A], arithmetic and geometric mean of EV[AA'] and EV[A'A], can be dropped from consideration due to their poor performance

published_or_final_version / Real Estate and Construction / Master / Master of Science in Construction Project Management

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfilment for the degree of Master of Science in Engineering Johannesburg 2015 / The Analytic Hierarchy Process (AHP) is a Multi Criteria Decision-Making (MCDM) tool, which has gained wide acceptance in all disciplines in science and engineering. Although it has been used in mining engineering applications, it is only recently gaining significant momentum in the mining industry. Given its simplicity, it may seem surprising that it has not received wide acceptance, but this is probably due to a lack of both publicity and a user-friendly methodology. This report introduces a simple methodology that can be employed by anyone who possesses basic knowledge of arithmetic and spreadsheets, without having to know or understand fully the mathematics that the process is based on. / MT2017