Decisions for asset allocation and protection are predicated upon accurate knowledge of the current operating environment as well as correctly characterizing the evolution of the environment over time. The desired kinematic and kinetic states of objects in question cannot be measured directly in most cases and instead are inferred or estimated from available measurements using a filtering process. Often, nonlinear transformations between the measurement domain and desired state domain distort the state domain probability density function yielding a form which does not necessarily resemble the form assumed in the filtering algorithm. The distortion effect must be understood in greater detail and appropriately accounted for so that even if sensors, state estimation algorithms, and state propagation algorithms operate in different domains, they can all be effectively utilized without any information loss due to domain transformations. This research presents an analytical investigation into understanding how non-linear transformations of stochastic, but characterizable, processes affect state and uncertainty estimation with direct application to space object surveillance and space- craft attitude determination. Analysis is performed with attention to construction of the state domain probability density function since state uncertainty and correlation are derived from the statistical moments of the probability density function. Analytical characterization of the effect nonlinear transformations impart on the structure of state probability density functions has direct application to conventional non- linear filtering and propagation algorithms in three areas: (1) understanding how smoothing algorithms used to estimate indirectly observed states impact state uncertainty, (2) justification or refutation of assumed state uncertainty distribution for more realistic uncertainty quantification, and (3) analytic automation of initial state estimate and covariance in lieu of user tuning. A nonlinear filtering algorithm based upon Bayes’ Theorem is presented to ac- count for the impact nonlinear domain transformations impart on probability density functions during the measurement update and propagation phases. The algorithm is able to accommodate different combinations of sensors for state estimation which can also be used to hypothesize system parameters or unknown states from available measurements because information is able to appropriately accounted for.

Background: A university is characterized as organized anarchy. According to Cohen, et al. (1972) decision-making occurs in form of A Garbage Can Model (GCM) in such organizations. This model is influenced by some factors such as organizational structure. The influences of some types of organizational structure have been studied based on a computer simulation by Cohen and his colleagues in 1972. However, the study was based on numerical statistics and excluded the influence of genuine characteristics of an organizational structure. Aim: This thesis aims to understand the influence of the dynamic and real characteristics of an organizational structure on a university’s decision-making process. Therefore, this research is conducted to explore how matrix structure influences on decision-making process in Linköping University by reducing uncertainty characteristics of decision-making process. Definitions: A Garbage Can Model: provides a theory framework in order to understand how decisions are made in organizations under condition of organized anarchy. This Model has four heterogeneous streams include decision, solution, decision maker, and choice opportunity. All these streams are moving around within the organization and need to match up in the choice opportunities to make decisions. Matrix Structure: is a structure with focus on multiple dimensions through multiple lines of authority and lateral communication. Results: The Matrix structure reduces the uncertainty of decision-making in Linköping University by managing the information process through the lateral communication. More specifically, the lateral communication occurs based on different approaches such as informal meetings, network of people, management group, and external information. However, the authority aspect in Linköping University has certain tendency towards the single unity of command (centralization), which is in contrast with matrix structure characteristics.

Software requirements, and how they are constructed, shared and translated across software organizations, express uncertainties that software developers need to address through appropriate structuring of the process and the organization at large. To gain new insights into this important phenomenon, we rely on theory of work design and the travelling metaphor to undertake an in-depth qualitative inquiry into recurrent development of packaged software for the utility industry. Using the particular context of software provider GridCo, we examine how requirements are constructed, shared, and translated as they travel across vertical and horizontal boundaries. In revealing insights into these practices, we contribute to theory by conceptualizing how requirements travel, not just locally, but across organizations and time, thereby uncovering new knowledge about the responses to requirement uncertainty in development of packaged software. We also contribute to theory by providing narrative accounts of in situ requirements processes and by revealing practical consequences of organization structure on managing uncertainty.

Aircraft operators demand an ever increasing availability of their fleets with constant reduction of their operational costs. With the age of many fleets measured in decades, the options to face these challenges are limited. Integrated Vehicle Health Management (IVHM) uses data gathered through sensors in the aircraft to assess the condition of components to detect and isolate faults or even estimate their Remaining Useful Life (RUL). This information can then be used to improve the planning of maintenance operations and even logistics and operational planning, resulting in shorter maintenance stops and lower cost. Retrofitting health monitoring technology onto legacy aircraft has the capability to deliver what operators and maintainers demand, but working on aging platforms presents numerous challenges. This thesis presents a novel methodology to select the combination of diagnostic and prognostic tools for legacy aircraft that best suits the stakeholders’ needs based on economic return and financial risk. The methodology is comprised of different steps in which a series of quantitative analyses are carried out to reach an objective solution. Beginning with the identification of which components could bring higher reduction of maintenance cost and time if monitored, the methodology also provides a method to define the requirements for diagnostic and prognostic tools capable of monitoring these components. It then continues to analyse how combining these tools affects the economic return and financial risk. Each possible combination is analysed to identify which of them should be retrofitted. Whilst computer models of maintenance operations can be used to analyse the effect of retrofitting IVHM technology on a legacy fleet, the number of possible combinations of diagnostic and prognostic tools is too big for this approach to be practicable. Nevertheless, computer models can go beyond the economic analysis performed thus far and simulations are used as part of the methodology to get an insight of other effects or retrofitting the chosen toolset.

Decision making under uncertainty is an important yet challenging task; a number of alternative paradigms which address this problem have been proposed. Stochastic Programming (SP) and Robust Optimization (RO) are two such modelling ap-proaches, which we consider; these are natural extensions of Mathematical Pro-gramming modelling. The process that goes from the conceptualization of an SP model to its solution and the use of the optimization results is complex in respect to its deterministic counterpart. Many factors contribute to this complexity: (i) the representation of the random behaviour of the model parameters, (ii) the interfac-ing of the decision model with the model of randomness, (iii) the difficulty in solving (very) large model instances, (iv) the requirements for result analysis and perfor-mance evaluation through simulation techniques. An overview of the software tools which support stochastic programming modelling is given, and a conceptual struc-ture and the architecture of such tools are presented. This conceptualization is pre-sented as various interacting modules, namely (i) scenario generators, (ii) model generators, (iii) solvers and (iv) performance evaluation. Reflecting this research, we have redesigned and extended an established modelling system to support modelling under uncertainty. The collective system which integrates these other-wise disparate set of model formulations within a common framework is innovative and makes the resulting system a powerful modelling tool. The introduction of sce-nario generation in the ex-ante decision model and the integration with simulation and evaluation for the purpose of ex-post analysis by the use of workflows is novel and makes a contribution to knowledge.

This thesis contributes to the empirical literature about how uncertainty affects firm-level investment behavior and capital accumulation using a structural econometric approach. Chapter 2 surveys the literature and highlights that there are two key channels through which uncertainty may affect investment decisions. One reflects the non-linearity of operating profits in stochastic demand or productivity parameters, summarized as the Hartman-Abel-Caballero (HAC) effect. Another reflects frictions in capital adjustment, summarized by different forms of capital adjustment costs: partial irreversibility, a fixed cost of undertaking any investment and quadratic adjustment costs. Chapter 3 presents simulation evidence about the effects of uncertainty on investment dynamics and capital accumulation through different forms of adjustment costs. Using the Method of Simulated Moments, Chapters 4 and 5 estimate fully parametric structural investment models, for panels of Brazilian and UK manufacturing firms, respectively. Chapter 4 investigates the effects of reducing capital adjustment costs. Counterfactual simulations indicate that investment would be much more responsive to new information about profitability if firms in Brazil faced a lower level of adjustment costs. A lower level of adjustment costs would also induce firms to operate with substantially higher capital stocks. Both these effects are mainly due to the importance of the estimated quadratic adjustment costs. Chapter 5 then investigates the effects of changing the level of uncertainty. The estimated investment models predict a small effect of uncertainty on investment dynamics in the short-run, and a negative and potentially large effect of uncertainty on capital accumulation in the long-run. The long-run effect of uncertainty operates through the negative effect of quadratic adjustment costs in the baseline model, or through a richer combination of effects in an extended model that allows discount rates to vary with the level of uncertainty.

This Thesis explored a range of approaches to study the uncertainty and impacts associated with climate change at the farm scale in Scotland. The research objective was to use a process of uncertainty evaluation and simulation modelling to provide evidence of how primary production components of agriculture in Scotland may change under a future climate. The work used a generic Integrated Modelling Framework to structure the following sequence of investigations: Evaluate a Regional Climate Model‟s hindcast estimates (1960-1990) against observed weather data; Develop bias correction „downscaling factors‟ to be applied to the Regional Climate Model‟s future estimates; Evaluate the impacts of weather data sources (observed and modelled) on estimates made by a cropping systems model (CropSyst); Estimate values for a range of agro-meteorological metrics using observed and estimated downscaled future weather data; Simulate spring barley and winter wheat growth using CropSyst with observed and modelled weather data; Develop CropSyst in order to represent grass growth, evaluate estimates against a set of a priori criteria and determine suitability for use in a whole farm model. Conduct counter-factual assessments of the impacts of climate change and potential adaptation options using a whole farm model (LADSS). The study aimed to use tools on a spectrum of land use modelling complexity: agro-meteorological metrics (simple), CropSyst (intermediate), and the whole-farm integrated model (complex). Such an approach had a path dependency, in that to use the livestock system model component within the whole farm model, CropSyst had to make estimates of an acceptable quality for grass production. CropSyst however failed to meet the a priori evaluation criteria. This, coupled with technical and time constraints in running LADSS, led to the decision not to run the whole farm model. The findings were organised within the concepts of resilience and adaptive capacity. Results gained showed that the HadRM3 Regional Climate Model was capable of making both good and poor estimates of weather variables in the UK, and that downscaling improved the match between hindcast and observed weather data significantly. A sensitivity analysis involving introducing uncertainty from weather data sources within CropSyst showed that care was needed in interpreting estimates of future crop production. The agro-meteorological metrics indicated that whilst growing season length increases, the date of end of field capacity does not. The projected changes in crop production will likely be more positive if crop responses to elevated CO2 are considered. However, there will be additional constraints on crop growth due to increases in duration and magnitude of periods of growth limiting soil water deficits. Without adaptation to crop varieties with slower phenological development, yield decreases are seen in spring barley and winter wheat. The thesis concludes, whilst recognising the caveats and limitations of the methods used and the multiple range of external influencing issues, that the biophysical impacts at the farm scale in Scotland are within the boundaries of resilience, given that achievable adaptation options exist and are undertaken. The dynamics of farm scale management will need to adjust to cope with higher levels of water stress, but opportunities will also arise for greater flexibility in land use mixes. Crop yield can increase due to more favourable growing conditions and cultivar adaptations. These conclusions, when placed within the context of climate change impacts and adaptive cycles at a global scale, indicate that agriculture in Scotland has the potential to cope with the impacts but that substantial changes are required in farming practices.

Storm sewer systems play a very important role in urban areas. The design of a storm sewer system should be based on an appropriate level of preventing flooding. This thesis focuses on issues relevant to decision-making in storm sewer network design considering flood risk. Uncertainty analysis is often required in an integrated approach to a comprehensive assessment of flood risk. The first part of this thesis discusses the understanding and representation of uncertainty in general setting. It also develops methods for propagating uncertainty through a model under different situations when uncertainties are represented by various mathematical languages. The decision-making process for storm sewer network design considering flood risk is explored in this thesis. The pipe sizes and slopes of the network are determined for the design. Due to the uncertain character of the flood risk, the decision made is not unique but depends on the decision maker’s attitude towards risk. A flood risk based storm sewer network design method incorporating a multiple-objective optimization and a “choice” process is developed with different design criteria. The storm sewer network design considering flood risk can also be formed as a single-objective optimization provided that the decision criterion is given a priori. A framework for this approach with a single objective optimization is developed. The GA is adapted as the optimizer. The flood risk is evaluated with different methods either under several design storms or using sampling method. A method for generating samples represented by correlated variables is introduced. It is adapted from a literature method providing that the marginal distributions of variables as well as the correlations between them are known. The group method is developed aiming to facilitate the generation of correlated samples of large sizes. The method is successfully applied to the generation of rainfall event samples and the samples are used for storm sewer network design where the flood risk is evaluated with rainfall event samples.

CIVINS / The US Navy has identified a need for an autonomous, persistent, forward deployed system to Detect, Classify, and Locate submarines. In this context, we investigate a novel method for multiple sensor platforms acting cooperatively to locate an uncooperative target. Conventional tracking methods based on techniques such as Kalman filtering or particle filters have been used with great success for tracking targets from a single manned platform; the application of these methods can be difficult for a cooperative tracking scenario with multiple unmanned platforms that have considerable navigation error. This motivates investigation of an alternative, set-based tracking algorithm, first proposed by Detweiler et al. for sensor network localization, to the cooperative tracking problem. The Detweiler algorithm is appealing for its conceptual simplicity and minimal assumptions about the target motion. The key idea of this approach is to compute the temporal evolution of potential target positions in terms of bounded regions that grow between measurements as the target moves and shrink when measurements do occur based on an assumed worst-case bound for uncertainty. In this thesis, we adapt the Detweiler algorithm to the scenario of cooperative tracking for persistent undersea surveillance, and explore its limitations when applied to this domain. The algorithm has been fully implemented and tested both in simulation and with postprocessing of autonomous surface craft (ASC) data from the PLUSNet Monterey Bay 2006 experiment. The results indicate that the method provides disappointing performance when applied to this domain, especially in situations where communication links between the autonomous tracking platforms are poor. We conclude that the method is more appropriate for a large N tracking scenario, with a large number of small, expendable tracking nodes, instead of our intended scenario with a smaller number of more sophisticated mobile trackers. / CIVINS / US Navy (USN) author.

The purpose of this study is explaining the effect of trust on consumers mobile commerce adoption under the influence of cross-cultural effects in the tourism industry.  This research applied a quantitative method, with a total sample size of 200. The collected samples are coming from China and France in the tourism industry. The result of this study is in line with previous studies that trust plays a crucial role in mobile commerce(MC) adoption of consumers. Design, privacy and reputation are very important determinants of trust on MC adoption. This study shows that uncertainty avoidance(UA) has a negative impact on the relationship between trust and MC adoption, and the moderating effect of UA is weaker for the consumer who are accustomed to low UA culture, compared to those who are accustomed to high-level UA culture.