This study's objective was to compare a Group Support System (GSS), i.e., CyberQuest, with the Nominal Group Technique (NGT). Its basic assumption was that discussion outcomes are enhanced by decision interaction processes, the quality of group facilitation, the intensity of group interaction, the effectiveness of the group meeting, and the level of group satisfaction. The GSS of choice in the study was CyberQuest, which was developed at Virginia Tech by Professor John Dickey. Like most GSS, its purpose is to increase the effectiveness of individual and multiple decision makers. Unlike most GSS which for the most part feature various computerized problem solving tools, CyberQuest adds hypermedia hardware/software systems to stimulative and innovative group facilitation procedures and methodologies. The unit of analysis was a group meeting. Eight groups of randomly selected Town officials and citizens were asked to develop policy recommendations that address the need to encourage the retention and growth of a mix of retail services in Blacksburg, Virginia. Four groups were exposed to CyberQuest. Four were not; instead they were exposed to a modified version of the NGT. Prior to the administration of the experiment, an expert panel was polled to determine the criteria by which the policy was to be judged. The results of the experiment were not entirely favorable to CyberQuest sessions. There was no statistically significant difference between CyberQuest and NGT in decision product quality, quality of facilitation, or meeting effectiveness. Only group interaction and group satisfaction gave evidence of any statistically significant difference. There was sufficient evidence to conclude that the former was of greater intensity in CyberQuest driven sessions. On the other hand, there was evidence to conclude that the latter was greater in NGT driven sessions. / Ph. D.

Public policy and program implementation has come to be regarded as a significant problem area in the governance process. Research has provided insights but falls short of totally satisfactory prescriptions for operational success. The literature on policy and program implementation reflects a dichotomy of means between centralized control and delegation of substantial discretionary authority. The resulting theory leaves a gap with practice. Scenario writing is one of the techniques available to practitioners that might be employed to assist in the execution of their responsibilities. Scenarios can be useful tools, but their preparation is costly and time consuming. It was hypothesized that computer modeling techniques and artificial intelligence could be applied to scenario generation to create an effective, practical instrument to permit wider and more effective use of scenarios for planning and management. A computer supported procedure is presented for generating scenarios which describe alternative sequences of future events for a given situation and proposed policy. The generator design reflects a three-way compromise between processing flexibility, data-base structure, and user workload requirements. This prototype generator was subjected to exploratory trials. The lessons learned highlight some potentially valuable program improvements, the importance of focusing the scenario at a level useful to the reader, and the need for a common set of definitions. / Ph. D.

Decision-related activities, such as bottom-up and top-down policy development, analysis, and planning, stand to benefit from the development and application of computer-based models that are capable of representing spatiotemporal social human behavior in local contexts. This is especially the case with our efforts to understand and search for ways to mitigate the context-specific effects of climate change, in which case such models need to include interacting social and ecological components. The development and application of such models has been significantly hindered by the challenges in designing artificial agents whose behavior is grounded in both empirical evidence and theory and in testing the ability of artificial agents to represent the behavior of real-world decision-makers. This dissertation advances our ability to develop such models by overcoming these challenges through the creation of: (a) three new frameworks, (b) two new methods, and (c) two new open-source modeling tools. The three new frameworks include: (a) the SOSIEL framework, which provides a theoretically-grounded blueprint for the development of a new generation of cognitive, multi-agent, and knowledge-based models that consist of agents empowered with cognitive architectures; (b) a new framework for analyzing the bounded rationality of decision-makers, which offers insight into and facilitates the analysis of the relationship between a decision situation and a decision-maker's decision; and (c) a new framework for analyzing the doubly-bounded rationality (DBR) of artificial agents, which does the same for the relationship between a decision situation and an artificial agent's decision. The two new methods include: (a) the SOSIEL method for acquiring and operationalizing decision-making knowledge, which advances our ability to acquire, process, and represent decision-making knowledge for cognitive, multi-agent, and knowledge-based models; and (b) the DBR method for testing the ability of artificial agents to represent human decision-making. The two open-source modeling tools include: (a) the SOSIEL platform, which is a cognitive, multi-agent, and knowledge-based platform for simulating human decision-making; and (b) an application of the platform as the SOSIEL Human Extension (SHE) to an existing forest-climate change model, called LANDIS-II, allowing for the analysis of co-evolutionary human-forest-climate interactions. To provide a context for examples and also guidelines for knowledge acquisition, the dissertation includes a case study of social-ecological interactions in an area of the Ukrainian Carpathians where LANDIS-II with SHE are currently being applied. As a result, this dissertation advances science by: (a) providing a theoretical foundation for and demonstrating the implementation of a next generation of models that are cognitive, multi-agent, and knowledge-based; and (b) providing a new perspective for understanding, analyzing, and testing the ability of artificial agents to represent human decision-making that is rooted in psychology.

This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space. Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual. The original contributions of this thesis are: 1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description. 2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization. 3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals. 4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations. 5. Application of MOEAs to the computationally expensive flare pattern design domain. 6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms. 7. Development of a new method of using MOEAs for automatic design of pattern recognition systems.

Small-scale farmers rarely get enough yields to sustain themselves to the next harvest. Most of these farmers are located in marginal areas with poor soils and in semi-arid areas which receive little rainfall yet the farmers practice rainfed agriculture. A number of reasons can be attributed to the low yields characterizing these farms. Lack of relevant knowledge for decision-making and climate change are among the major reasons for poor yields. Whilst there is not much the small-scale farmers can do to influence climate, they can at least make informed decisions to improve their yields. The information necessary for agricultural decision-making include the climate forecast information and information about performance of new technologies be it fertilisers, varieties or other practices. The study aimed to answer the primary research question: What is the applicability of the APSIM model in decision-making by small-scale resource constrained farmers? This question was supported by secondary research questions namely: - How useful is the APSIM model in small-scale farmers' adaptation to future climate change? - What are the current farming systems of Lower Gweru farmers with regards to maize production? - What are farmers' perceptions of climate change and what changes have they noticed in the last 10 years? - How do small-scale farmers make crop management decisions? Data was gathered through five methods namely, Focus Group Discussions, resource allocation mapping technique, APSIM simulations, on-farm experimentation, and semi-structured interviews. Data was collected from a group of 30 small-scale farmers of Lower Gweru Communal area. The study concentrated on maize production due to the fact that it is the staple food and was grown by all farmers. All the farmers perceived climate to be changing. The changes noted included late start of the rain season, early cessation of rain season and temperature extremes. The majority of farmers highlighted that they were using local indicators to make decisions about climate or to forecast the nature of the coming season before they were exposed to SCF and APSIM. The data gathered from three selected resource allocation maps were used to run the APSIM model. For which farmers were convinced that the model was credible in yield prediction based on the simulated results which reasonably compared to observed yields. The what if questions raised by farmers during the discussions were also assessed and this further increased the farmers' confidence with the model, as they viewed it as a planning and guiding tool before one can actually commit resources. The semi-structured interviews showed that most farmers will continue to use the model outputs in their decision-making. The reasons being that it was a good planning and budgeting tool, it is cheaper and faster since one can assess a lot of options in a short time and would then decide on which options are viable in a given season. The few farmers who said they would not use the model or its outputs in decision-making cited reasons including lack of a computer to install the model and that it was complex for them. Semi-structured interviews confirmed the data collected in resource allocation mapping, focused group discussions and APSIM sessions. / Thesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.