Probabilistic decline curve analysis (PDCA) methods have been developed to quantify uncertainty in production forecasts and reserves estimates. However, the application of PDCA in shale gas reservoirs is relatively new. Limited work has been done on the performance of PDCA methods when the available production data are limited. In addition, PDCA methods have often been coupled with Arp’s equations, which might not be the optimum decline curve analysis model (DCA) to use, as new DCA models for shale reservoirs have been developed. Also, decline curve methods are based on production data only and do not by themselves incorporate other types of information, such as volumetric data. My research objective was to integrate volumetric information with PDCA methods and DCA models to reliably quantify the uncertainty in production forecasts from hydraulically fractured horizontal shale gas wells, regardless of the stage of depletion. In this work, hindcasts of multiple DCA models coupled to different probabilistic methods were performed to determine the reliability of the probabilistic DCA methods. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. Most of the DCA models were well calibrated visually when used with an appropriate probabilistic method, regardless of the amount of production data available to match. Volumetric assessments, used as prior information, were incorporated to further enhance the calibration of production forecasts and reserves estimates when using the Markov Chain Monte Carlo (MCMC) as the PDCA method and the logistic growth DCA model. The proposed combination of the MCMC PDCA method, the logistic growth DCA model, and use of volumetric data provides an integrated procedure to reliably quantify the uncertainty in production forecasts and reserves estimates in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates, as well as more reliable assessment of upside and downside potential. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts and reserves estimates for early wells in the play.

Since uncertainty and irreversibility are inherent, environmental policy involves the problems of timing of implementation. Environmental policy based on cost-benefit analysis using certainty equivalent presents values can be misleading under the combined effect of irreversibility and uncertainty. Using real options method, the thesis analyzes the timing of early action investment in Canada's Kyoto commitment. Early action investment in emission reductions is irreversible. The thesis uses a simple two-period model, and then lays out a corresponding continuous-time model to show that under technological uncertainty, early action investment should be delayed until more information - the results of R&D - is revealed. In particular, the more uncertain the outcome of research, the more the firm should delay early action investment. The thesis argues that Canada's Kyoto commitment is well intentioned but not wisely implemented: early action investment on emission reductions may not be efficient. The results suggest that a more gradual Kyoto program would be favourable.

This paper uses stock market data from 2000-2010 to examine the role long-term growth (LTG) uncertainty plays in equity valuations. In theory, the convex relationship between LTG and per-share value suggests a positive relationship between LTG uncertainty and analysts’ price targets, with higher levels of LTG uncertainty leading to higher, less accurate price targets. However, this paper finds conclusive evidence that analysts are not incorporating LTG uncertainty into their pricing models. This leaves uncertainty regarding the discount rate and the perpetuity growth rate as the only remaining potential sources of upward pressure on analysts’ price targets that are attributable to uncertainty.

This study discusses possible effects on investment decisions among Gotland enterprises from the ongoing public discourse over the present and future Gotland ferry traffic to mainland Sweden. Central topics are a substantial freight cost disadvantage, level and classification of state funding. By way of critical discourse analysis of statements from the most visible, powerful and persistent stakeholders surrounding the Gotland ferry issue are two major discursive thrusts identified. The level of enterprise awareness of the ferry discourses is investigated in a survey and correlated to perceived political uncertainty. Also is uncertainty related to investment reluctance. Further is the share of mainland marketed products related to the share of value-added products, and finally are enterprises asked whether they have invested to enhance the share of value-added products or if such investments are planned. The findings indicate that enterprises are well aware of the ferry discourse which is bringing about a high level of political uncertainty, while the level of investment hesitancy is somewhat more modest. Still a majority of enterprises are postponing or even refraining from investments. Also of interest is the high degree of consistency between enterprises with a high share of mainland marketing and high share of value-added products. A minority of enterprises has chosen the strategy to enhance their share of value-added products or is planning investments to that end in order to compensate for the higher transport costs. Longitudinal studies comparing the transport cost share of overall turnover for Gotland enterprises and their mainland competitors in the agriculture/food, manufacturing and tourism sectors are suggested, together with a study whether a more strategic investment pattern might evolve among Gotland enterprises.

The Water Framework Directive (WFD, 2000/60/EC) was introduced in 2000. In implementing the WFD in Sweden expert knowledge in areas relating to the WFD was much needed resulting in establishment of national water management authorities, as the five water authorities, boarded in appointed county administrative boards across the country. As much noted in literature, uncertainties appear when implementing the WFD. Previous literature,with the exception of e.g. Raadgever et al (2011), Brugnach et al (2008) and Sigel et al (2007 and2010), has not put much focus on the types of uncertainty experienced by water management officials working with the implementation of the WFD on a daily basis. There are also few studies in Sweden, such as Gipperth & Elmgren (2010), Hammer et al (2011) and Entson & Gipperth(2010), touching upon the subject although not entirely focusing on practical experiences of uncertainty. Therefore, the aim of this study is to reveal the types of uncertainties experienced by water management officials from: SMHI, the county administrative board in Östergötland and Bothnia Bay, the water authority in the Southern Baltic Sea district, the Northern Baltic Sea and the North Sea, as well as the SEPA. In analysing the empirical data, six types of uncertainties emerged. They showed that water management officials experienced uncertainties in interpreting the WFD e.g. recommendation and manuals from supervisory authorities, measurement techniques, the typology of water bodies, what methods to use in e.g. assessments, knowing the effects of action programmes and lack of data ordata deficiency. In combating these uncertainties, the water management officials used communication as an strategy. Furthermore, responsibility in pointing out uncertainties and ways of reducing uncertainties was also seen as ways of handling uncertainties.

Nowadays, the trend towards globalization and internationalization of business has strong impact on companies’ strategy. When a company is thinking about strategy development, it needs to pay attention to variable aspects on micro- and macro-level. This study includes the overview of the factors which need to be considered while developing a strategy. Moreover, a significant emphasize is made on the contemporary deriving challenges at the market. The purpose of the study is to investigate a new approach to the strategic planning, named “Early warning system”. For that purpose, it was conducted qualitative research at the example of the Ukrainian telecommunication company – MTS Ukraine. I identified factors which influence the company and analyzed how company copes with deriving challenges. Moreover, it was found out the company’s strategic planning process is significantly influenced by specific market characteristics such as high level of bureaucracy, specific behavior of some competitors, growing market.

With increasing demand and pressures on groundwater resources, accurate and reliable groundwater prediction models are essential for sustainable groundwater management. Groundwater models are merely approximations of reality, and we are unable to either fully characterize or mathematically describe the true complexity of the hydrologic system; therefore, inherent in all models are varying degree of uncertainty. A robust management policy should consider uncertainties in both the imprecise nature of conceptual/numerical models and their parameters. This study addresses the critical question of whether the use of multiple conceptual models to explicitly account for conceptual model uncertainty improves the ability of the models to assist in management decisions. Twelve unique conceptual models, characterized by three alternative geological interpretations, two recharge estimations, and two boundary condition implementations, were formulated to estimate sustainable extraction rates from Thailand’s Thaphra Area, where increasing groundwater withdrawals may result in water level declination and saline water upconing. The models were developed with MODFLOW and calibrated using PEST with the same set of observed hydraulic head data. All of the models were found to reasonably produce predictions of the available heads data. To select the best among the alternative models, multiple criteria have been defined and applied to evaluate the quality of individual models. It was found that models perform differently with respect to different evaluation criteria, and that it is unlikely that a single inter-model comparison criterion will ever be sufficient for general use. The chosen alternative models were applied both individually and jointly to quantify uncertainty in the groundwater management context. Different model-averaging methods were assessed in terms of their ability to assist in quantifying uncertainty in sustainable yield estimation. The twelve groundwater simulation models were additionally linked with optimization techniques to determine appropriate groundwater abstraction rates in the TPA Phu Thok aquifer. The management models aim to obtain maximal yields while protecting water level decline. Despite similar performances among the calibrated models, total sustainable yield estimates vary substantially depending on the conceptual model used and range widely, by a factor of 0.6 in total, and by as much as a factor of 4 in each management area. The comparison results demonstrate that simple averaging achieves a better performance than formal and sophisticated averaging methods such as Maximum Likelihood Bayesian Model Averaging, and produce a similar performance to GLUE and combined-multiple criteria averaging methods for both validation testing and management applications, but is much simpler to implement and use, and computationally much less demanding. The joint assessment of parameter and conceptual model uncertainty was performed by generating the multiple realizations of random parameters from the feasible space for each calibrated model using a simple Monte Carlo approach. The multi-model averaging methods produce a higher percentage of predictive coverage than do any individual models. Using model-averaging predictions, lower optimal rates were obtained to minimize head constraint violations, which do not ensue if a single best model is used with parameter uncertainty analysis. Although accounting for all sources of uncertainty is very important in predicting environmental and management problems, the available techniques used in the literature may be too computationally demanding and, in some cases, unnecessary complex, particularly in data-poor systems. The methods presented here to account for the main sources of uncertainty provide the required practical and comprehensive uncertainty analysis and can be applied to other case studies to provide reliable and accurate predictions for groundwater management applications.

Bakgrund: Cancer som är en livshotande sjukdom orsakar inte bara fysiska symptom utan även psykiska. Sjukdomen medför ovisshet som rör många olika aspekter, så som sjukdomsutveckling, behandling och framtid. Syfte: Syftet med studien är att belysa upplevelser av ovisshet hos personer med cancer. Metod: Studien genomfördes som en litteraturstudie baserad på vetenskapliga artiklar. Sammanlagt användes nio artiklar till resultatet som alla handlade om ovisshet vid cancer. Resultat: Resultatet presenteras i tre huvudkategorier med tillhörande subkategorier. Huvudkategorin Vad är orsaken till sjukdomen? andlar om sjukdomens uppkomst och varför just individen har drabbats. Hur är det att leva med cancer? handlar om personers bristande kunskap om sjukdomen och ovisshet angående behandlingen samt tilliten till vården. Hur ser framtiden ut? handlar om sjukdomens utveckling, behandlingsbiverkningar och omgivningen. Diskussion: I metoddiskussionen granskas studiens metod med hjälp av begreppen överförbarhet, giltighet och trovärdighet. I resultatdiskussionen diskuteras studiens viktigaste fynd. Det som belyses är vikten av information, problematiken till att ta ett beslut om behandling, vikten av tillit till vården och en diskussion angående varför just den enskilde individen har blivit drabbad. / Background: Cancer is a life threatening disease; it causes not only physical symptoms but also psychological. The disease leads to uncertainty in many aspects, such as disease progression, the treatment and the future. Purpose: The aim of this study is to highlight the experiences of uncertainty in people with cancer. Method: The study was conducted as a literature study based on scientific articles. Altogether nine articles where assembled to the results. The articles focused on the experiences of uncertainty in people with cancer. Results: The results are presented in three main categories and related subcategories. What’s the cause of the disease? which includes information about the origin of the disease.  What’s it like to live with cancer? which includes information about people's lack of knowledge, uncertainty about the treatment and trust in health care. What does the future hold? deals with the development of the disease, treatment side effects and surroundings. Discussion:  The method is discussed from the terms transferability, validity and credibility. The results most important findings are discussed: importance of information, the issues about making a treatment decision, the importance of trusting in health care and a discussion about why the individual have become a victim of cancer.

Understanding uncertainty in emissions inventories is critical for evaluating both air quality modeling results as well as impacts of emissions reduction strategies. This study focused on quantification of uncertainty due to non-road emissions specifically for the state of Georgia using the EPA NONROAD emissions model. Nonroad engines contribute significantly to anthropogenic emissions inventories, with national estimates for various criteria pollutants ranging from 14% to 22%. The NONROAD model is designed to estimate emissions for any area in the United States based on population, activity, and emissions data. Information used in the model comes from a variety of sources collected over many years. A sensitivity analysis of the model determined the input variables that have significant effects on emissions. Results showed that model estimated emissions are significantly sensitive to increases in equipment population, activity, load factor, and emission factor. Increases in ambient temperature, fuel RVP, fuel sulfur (except on SO2), and average useful life have smaller effects. Emissions and activity data used in the NONROAD model were analyzed using statistical techniques to quantify uncertainty in the input parameters. Expert elicitation was also used to estimate uncertainties in emission factors, equipment population, activity, load factors, and geographic allocations of the emissions to the county level. A Monte Carlo approach using the derived parameter uncertainties and different input probability distributions was used to estimate the overall uncertainty of emissions from the NONROAD model for the state of Georgia. The uncertainties resulting from this analysis were significant, with 95% confidence intervals about the mean ranging from ?? to +61% for THC, -46 to +68% for NOx, -43% to 75% for CO, and ?? to +75% for PM. The sensitivity of ozone and CO for different regions in Georgia to NONROAD emissions in Georgia was also estimated. The analysis suggests that uncertainties in ozone and CO simulations due to NONROAD emissions uncertainties, averaged over the regions of interest, are not large, with resulting maximum coefficients of variation of 1% and 10% respectively.

A significant body of work has demonstrated both the promise and difficulty of quantifying uncertainty in reservoir simulation forecasts. It is generally accepted that accurate and complete quantification of uncertainty should lead to better decision making and greater profitability. Many of the techniques presented in past work attempt to quantify uncertainty without sampling the full parameter space, saving on the number of simulation runs, but inherently limiting and biasing the uncertainty quantification in the resulting forecasts. In addition, past work generally has looked at uncertainty in synthetic models and does not address the practical issues of quantifying uncertainty in an actual field. Both of these issues must be addressed in order to rigorously quantify uncertainty in practice. In this study a new approach to reservoir simulation is taken whereby the traditional one-time simulation study is replaced with a new continuous process potentially spanning the life of the reservoir. In this process, reservoir models are generated and run 24 hours a day, seven days a week, allowing many more runs than previously possible and yielding a more thorough exploration of possible reservoir descriptions. In turn, more runs enabled better estimates of uncertainty in resulting forecasts. A new technology to allow this process to run continuously with little human interaction is real-time production and pressure data, which can be automatically integrated into runs. Two tests of this continuous simulation process were conducted. The first test was conducted on the Production with Uncertainty Quantification (PUNQ) synthetic reservoir. Comparison of our results with previous studies shows that the continuous approach gives consistent and reasonable estimates of uncertainty. The second study was conducted in real time on a live field. This study demonstrates the continuous simulation process and shows that it is feasible and practical for real world applications.