The dissertation investigates some important aspects of managerial decision making under conditions of uncertainty. In the last three decades two prominent general approaches have evolved to deal explicitly with risk in managerial decisions. They are: (1) the central tendency-dispersion trade off approach, and (2) expected utility analysis. The first task undertaken in this investigation is to integrate these two approaches. This is accomplished by identifying those situations in which decision rules obtained by either approach are equivalent. Once equivalence between the two basic approaches to decision making under uncertainty is established, the focus shifts to the extension of these decision theories into situations involving multi-attribute outcome spaces. In particular, stochastic dominance rules for multivariate outcome distributions are developed. Two applications of stochastic dominance criteria are then presented, illustrating the relevance of the approach to theory development and management of resource systems. The first illustration demonstrates the application of stochastic dominance to portfolio diversification problems. Several results are obtained describing the sensitivity of optimal mixes with respect to changes in opportunities for investment. The second illustration demonstrates the role stochastic dominance criteria can play in ecosystem policy analysis. A methodology of stochastic dominance policy screening for forest management systems is developed and applied. / Business, Sauder School of / Graduate

The problem of selecting appropriate mathematical models for use in studying hydrological phenomena has created a situation in which the choice of suitable models by hydrologic practitioners has become exceedingly complex. The extensive comments in the literature indicate that neither the traditional system of technical journals nor the more modern computer -based retrieval schemes have really solved the problem. Further examination shows that similar problems have arisen in many fields, hence a well organized attack on the specific problem of hydrologic model choice can have a more general application. The present problem is identified as a requirement to codify and make accessible to users information in a more directly user oriented format. The problem of model choice arises at several levels, ranging from decision on what fundamental structure to use, to choice of parameters, and on to model calibration and validation. This paper is focused on a scheme to aid in model structure choice. The essential ingredients of model structure choice, and indeed of many choice processes, are extracted and embedded in a generalized set theoretic mathematical notational framework in order to give some insight into the nature of the problem. Within this framework the specialized features of the model choice problem are analyzed, and a specialized model is developed for assisting in model choice and all problems similarly situated. These considerations lead to the development of a finite vector of objective statements with codified responses prepared by a panel of qualified researchers who are willing and able to construct the essential information in a user oriented format. It is required that the panel not only couch their information in objective oriented terms but that they also generate value judgments for the individual components. In this way, those using the system can take advantage of the expert opinions embedded in the model while, at the same time, tailoring the choice to meet their own specific needs and aspirations. This results in what is defined as a mathematical CHOICEMODEL. The implementation of a system for interactive computation of the CHOICEMODEL is described in detail, and the associated computer programs are presented in appendices. A detailed instruction manual is given, and the implementation of the method is illustrated by an easily understood model of the ingredients of the problem of selecting an 8 -track stereo tape deck for home use. The plan is outlined whereby hydrologic choice models can be developed within the CHOICEMODEL system by a selected panel of expert EVALUATORs.

A variety of species distribution models (SDMs) were fit to data collected by a 15,000km road-side visual survey of Aloidendron dichotomum populations in the Northern Cape region of South Africa, and Namibia. We fit traditional presence/absence SDMs as well as SDMs on how proportions are distributed across three species stage classes (juvenile, adult, dead). Using five candidate machine learning methods and an ensemble model, we compared a number of approaches, including the role of balanced class (presence/absence) datasets in species distribution modelling. Secondary to this was whether or not the addition of species’ absences, generated where the species is known not to exist have an impact on findings. The goal of the analysis was to map the distribution of Aloidendron dichotomum under different scenarios. Precipitation-based variables were generally more deterministic of species presence or lack thereof. Visual interpretation of the estimated Aloidendron dichotomum population under current climate conditions, suggested a reasonably well fit model, having a large overlap with the sampled area. There however were some conditions estimated to be suitable for species incidence outside of the sampled range, where Aloidendron dichotomum are not known to occur. Habitat suitability for juvenile individuals was largely decreasing in concentration towards Windhoek. The largest proportion of dead individuals was estimated to be on the northern edge of the Riemvasmaak Conservancy, along the South African/Namibian boarder, reaching up to a 60% composition of the population. The adult stage class maintained overall proportional dominance. Under future climate scenarios, despite maintaining a bulk of the currently habitable conditions, a noticeable negative shift in habitat suitability for the species was observed. A temporal analysis of Aloidendron dichotomum’s latitudinal and longitudinal range revealed a potential south-easterly shift in suitable species conditions. Results were however met with some uncertainty as SDMs were uncovered to be extrapolating into a substantial amount of the study area. We found that balancing response class frequencies within the data proved not to be an effective error reduction technique overall, having no considerable impact on species detection accuracy. Balancing the classes however did improve the accuracy on the presence class, at the cost of accuracy of the observed absence class. Furthermore, overall model accuracy increased as more absences from outside the study area were added, only because these generated absences were predicted well. The resulting models had lower estimated suitability outside of the survey area and noticeably different suitability distributions within the survey area. This made the addition of the generated absences undesirable. Results highlighted the potential vulnerability of Aloidendron dichotomum given the pessimistic, yet likely future climate scenarios.

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The purpose of this study is to investigate the relationship between personality and certain characteristics of decision-making behavior in a modified two-choice probability learning situation. More specifically, this study addresses itself to the following questions: (1) Can personality correlates of the decision-making process be demonstrated? (2) Are personality factors related to individual differences in maximizing tendency, risk-taking, and decisiveness? (3) If such relationships, exist, who do they fit into the framework of existing decision-making theory?

The integration of economic, social and environmental dimensions is essential for corporate sustainability. Integration requires that there be no a priori priority among these dimensions. Economic priorities, however, often dominate decision-making processes in for-profit organisations. This thesis asks how do organisations integrate predominant economic dimensions, on the one hand, and social-ecological dimensions, on the other? The question is focused on the middle management level, where relatively little is known about how competing organisational aspects are integrated. The study addresses a gap in theory relating to tensions in corporate sustainability by drawing on paradox, organisational ambidexterity and organisational identity literatures. The case study explored the research question from the lived experience of purposefully sampled research participants in a century-old mining company. The study focused on the integration of economic and social-ecological (E&SE) dimensions in the cross-functional decision-making process where mining projects are developed. Findings from the inductive analysis before and after the introduction of an intentional integration process revealed five dimensions of differentiation that were further explored. The analysis culminated in a process model of E&SE integration. I argue that E&SE integration on the middle management level is characterised by tensions between competing, interrelated priorities that constrain integration. Notwithstanding organisational commitment to corporate sustainability and E&SE integration, failure to manage these tensions perpetuates unsustainable outcomes in decision-making processes. The overarching contribution to corporate sustainability literature is a process model of E&SE integration on the middle management level that addresses the tensions that constrain integration. Integration is enabled by suspending premature convergence on a single option and by bringing social-ecological dimensions to the forefront in order to explore how E&SE dimensions are interdependent, before making binding choices. The study contributes to organisational ambidexterity literature by showing how the integration of strategic priorities on the middle management level is distinct from integration on the senior management level with respect to the quality of the decision and the locus of integration. The study also contributes to an emerging scholarly conversation regarding organisational purpose by identifying how reframing purpose into an integrative metaframe can enable commitment to an integrated decision-making process.

"The purpose of this paper is to present two methods for proving that a statistical estimate is admissible and minimax. The Bayes method was introduced by Wald, and Theorems 2.2 and 2.3 illustrate the technique. The second way is due to Hodges and Lehmann and is based on a lower bound for the variance of an estimate. In Theorem 3.2 the Hodges-Lehmann method for proving admissibility is given. The last chapter is devoted to an extension of the Hodges and Lehmann technique to the Bhattacharyya bounds"--Introduction. / "August, 1954." / Typescript. / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: A. V. Fend, Professor Directing Paper. / Includes bibliographical references (leaves 44-45).

This dissertation aims to extend on the idea of Bollerslev (1987), estimating ARCH models with Student-t distributed errors, to estimating Stochastic Volatility (SV) models with Student-t distributed errors. It is unclear whether Gaussian distributed errors sufficiently account for the observed leptokurtosis in financial time series and hence the extension to examine Student-t distributed errors for these models. The quasi-maximum likelihood estimation approach introduced by Harvey (1989) and the conventional Kalman filter technique are described so that the SV model with Gaussian distributed errors and SV model with Student-t distributed errors can be estimated. Estimation of GARCH (1,1) models is also described using the method maximum likelihood. The empirical study estimated four models using data on four different share return series and one index return, namely: Anglo American, BHP, FirstRand, Standard Bank Group and JSE Top 40 index. The GARCH and SV model with Student-t distributed errors both perform best on the series examined in this dissertation. The metric used to determine the best performing model was the Akaike information criterion (AIC).

During the past decade, building energy consumption has risen significantly. Meanwhile, the building area is being increased at a high speed. The conflict between high building energy consumption and low energy efficiency has attracted great attention in the construction industry. HVAC system contributes to most of the whole building energy consumption. Thus, it is imperative to study and analyze the means of HVAC system’s energy conservation. This study aims at addressing two specific challenges: (1) the lack of knowledge to know the kind of HVAC performance that can be evaluated as the criteria for decision making; and (2) the lack of efficient methods for collecting HVAC system and equipment data to comprehend the information used by decision makers. An effective way to minimize these challenges is to predict the HVAC performance of a new building at the conceptual design stage through the application of energy simulation tools. However, the development process of these tools is usually isolated, which results in having the information of a building model that is created by other tools cannot be shared. On another side, there is a need to establish an energy conservation expert system to use during the design of the HVAC systems for buildings. Based on the above, this study integrates Building Information Modeling (BIM) and decision-making system to select HVAC systems for buildings. First, the basic of HVAC components and systems are collected and stored in specific database that will be used for the optimization of HVAC design. Various types of heating/cooling equipment are presented based on ASHRAE standards. Second, the environmental, economic, technical performance and green building rating system are summarized as the criteria for evaluating HVAC performance. Then a combined AHP (Analytic Hierarchy Process) and Entropy structure for HVAC system is introduced as the Decision-making method. Finally, the interoperability of BIM tool is developed to bridge the connection between BIM tool and the HVAC decision making systems through the whole life cycle of buildings. The entire model is coded in Visual Studio via C#. The model is tested through a project to prove its workability and dependency.

Discrete phylogeographic models enable the inference of the geographic history of biological organisms along phylogenetic trees. Frequently applied in the context of epidemiological modelling, phylogeographic generalised linear models were developed to allow for the evaluation of multiple predictors of spatial diffusion. The standard phylogeographic generalised linear model formulation, however, assumes that rates of spatial diffusion are a noiseless deterministic function of the set of covariates, admitting no other unobserved sources of variation. Under a variety of simulation scenarios, we demonstrate that the lack of a term modelling stochastic noise results in high false positive rates for predictors of spatial diffusion. We further show that the false positive rate can be controlled by including a random effect term, thus allowing unobserved sources of rate variation. Finally, we apply this random effects model to three recently published datasets and contrast the results of analysing these datasets with those obtained using the standard model. Our study demonstrates the prevalence of false positive results for predictors under the standard phylogeographic model in multiple simulation scenarios and, using empirical data from the literature, highlights the importance of a model accounting for random variation.