Dissertations / Theses on the topic 'Uncertainty risk'

Uncertainty is, it seems, more or less constantly present in our lives. Even so, grasping the concept philosophically is far from trivial. In this doctoral thesis, uncertainty and its conceptual companion information are studied. Axiomatic analyses are provided and numerical measures suggested. In addition to these basic conceptual analyses, the widespread practice of so-called safety factor use in societal regulation is analyzed along with the interplay between science and policy in European regulation of chemicals and construction.
QC 20120202

The negative effects of hazardous substances and possible measures that can be taken are evaluated in the environmental risk analysis process, consisting of risk assessment, risk communication and risk management. Uncertainty due to lack of knowledge and natural variability are always present in this process. The aim of this thesis is to evaluate some tools as well as discuss the management of uncertainty and variability, as it is necessary to treat them both in a reliable and transparent way to gain regulatory acceptance in decision making. The catalytic effects of various metals on the formation of chlorinated aromatic compounds during the heating of fly ash were investigated (paper I). Copper showed a positive catalytic effect, while cobalt, chromium and vanadium showed a catalytic effect for degradation. Knowledge of the catalytic effects may facilitate the choice and design of combustion processes to decrease emissions, but it also provides valuable information to identify and characterize the hazard. Exposure factors of importance in risk assessment (physiological parameters, time use factors and food consumption) were collected and evaluated (paper II). Interindividual variability was characterized by mean, standard deviation, skewness, kurtosis and multiple percentiles, while uncertainty in these parameters was estimated with confidence intervals. How these statistical parameters can be applied was shown in two exposure assessments (papers III and IV). Probability bounds analysis was used as a probabilistic approach, which enables separate propagation of uncertainty and variability even in cases where the availability of data is limited. In paper III it was determined that the exposure cannot be expected to cause any negative health effects for recreational users of a public bathing place. Paper IV concluded that the uncertainty interval in the estimated exposure increased when accounting for possible changes in climate-sensitive model variables. Risk managers often need to rely on precaution and an increased uncertainty may therefore have implications for risk management decisions. Paper V focuses on risk management and a questionnaire was sent to employees at all Swedish County Administrative Boards working with contaminated land. It was concluded that the gender, age and work experience of the employees, as well as the funding source of the risk assessment, all have an impact on the reviewing of risk assessments. Gender was the most significant factor, and it also affected the perception of knowledge.
Negativa effekter orsakade av skadliga ämnen och möjliga åtgärder bedöms och utvärderas i en miljöriskanalys, som kan delas i riskbedömning, riskkommunikation och riskhantering. Osäkerhet som beror på kunskapsbrist samt naturlig variabilitet finns alltid närvarande i denna process. Syftet med avhandlingen är att utvärdera några tillvägagångssätt samt diskutera hur osäkerhet och variabilitet hanteras då det är nödvändigt att båda hanteras trovärdigt och transparent för att riskbedömningen ska vara användbar för beslutsfattande. Metallers katalytiska effekt på bildning av klorerade aromatiska ämnen under upphettning av flygaska undersöktes (artikel I). Koppar visade en positiv katalytisk effekt medan kobolt, krom och vanadin istället katalyserade nedbrytningen. Kunskap om katalytisk potential för bildning av skadliga ämnen är viktigt vid val och design av förbränningsprocesser för att minska utsläppen, men det är också ett exempel på hur en fara kan identifieras och karaktäriseras. Information om exponeringsfaktorer som är viktiga i riskbedömning (fysiologiska parametrar, tidsanvändning och livsmedelskonsumtion) samlades in och analyserades (artikel II). Interindividuell variabilitet karaktäriserades av medel, standardavvikelse, skevhet, kurtosis (toppighet) och multipla percentiler medan osäkerhet i dessa parametrar skattades med konfidensintervall. Hur dessa statistiska parametrar kan tillämpas i exponeringsbedömningar visas i artikel III och IV. Probability bounds analysis användes som probabilistisk metod, vilket gör det möjligt att separera osäkerhet och variabilitet i bedömningen även när tillgången på data är begränsad. Exponeringsbedömningen i artikel III visade att vid nu rådande föroreningshalter i sediment i en badsjö så medför inte bad någon hälsofara. I artikel IV visades att osäkerhetsintervallet i den skattade exponeringen ökar när hänsyn tas till förändringar i klimatkänsliga modellvariabler. Riskhanterare måste ta hänsyn till försiktighetsprincipen och en ökad osäkerhet kan därmed få konsekvenser för riskhanteringsbesluten. Artikel V fokuserar på riskhantering och en enkät skickades till alla anställda som arbetar med förorenad mark på länsstyrelserna i Sverige. Det konstaterades att anställdas kön, ålder och erfarenhet har en inverkan på granskningsprocessen av riskbedömningar. Kön var den mest signifikanta variabeln, vilken också påverkade perceptionen av kunskap. Skillnader i de anställdas svar kunde också ses beroende på om riskbedömningen finansierades av statliga bidrag eller av en ansvarig verksamhetsutövare.

Uncertainty in situations involving risk is frequently modelled by assuming a plausible form of probability distribution for the uncertain quantities involved, and estimating the relevant parameters of that distribution based on the knowledge and judgement of informed experts or decision makers. The distributions assumed are usually uni-modal (and often bell-shaped) around some most likely value, with the Normal, Beta, Gamma and Triangular distributions being popular choices.

This doctoral thesis studies sovereign credit risk during periods of uncertainty about the state of a government's fiscal position. A new measure of fiscal uncertainty is introduced, based on the disagreement in official forecasts of the public budget deficit, and forecast revisions to approximate common uncertainty shocks. It is shown that in the aftermath of the global financial crisis, fiscal uncertainty increased substantially in advanced economies. The effects of fiscal uncertainty are largely unknown, in particular in the context of sovereign credit risk. To estimate the response of sovereign credit ratings to fiscal uncertainty, a new empirical framework is developed for the analysis of rating determinants. Rating transition is modelled as the joint outcome of two processes, which determine the frequency of rating changes, and their direction. This thesis finds that fiscal uncertainty is perceived a credit risk by rating agencies and increases the probability of a rating downgrade. Fiscal uncertainty also affects the attention paid to sovereign ratings. An event study analysis shows that the attention to rating announcements increases, the more noisy publicly available information about fiscal outcomes is.

Defence date: 25 September 2019
Examining Board: Prof. Evi Pappa, Universidad Carlos III Madrid (Supervisor); Prof. Axelle Ferrière, Paris School of Economics; Prof. Guido Ascari, University of Oxford; Prof. Johannes Pfeifer, University of Cologne
This thesis comprises three essays that analyze how uncertainty affects the macroeconomy. Each essay investigates a particular feature of uncertainty propagation. The first essay studies the effects of uncertainty shocks on economic activity, focusing on inflation. I consider standard New Keynesian models with Rotemberg-type and Calvo-type price rigidities. Despite the belief that the two schemes are equivalent, I show that they generate different dynamics in response to uncertainty shocks. In the Rotemberg model, uncertainty shocks decrease output and inflation, in line with the empirical results. By contrast, in the Calvo model, uncertainty shocks decrease output but raise inflation because of firms’ precautionary pricing motive. The second essay, written with Dario Bonciani, shows that uncertainty shocks negatively affect economic activity not only in the short, but also in the long run. We build a New Keynesian model with endogenous growth and Epstein-Zin preferences. A decline in R&D by higher uncertainty determines a fall in productivity, which causes a long-term decrease in the macroeconomic aggregates. This long-term risk affects households’ consumption process, which exacerbates the overall negative effects of uncertainty shocks. The third essay, prepared with Anna Rogantini Picco, illustrates how economic agents’ heterogeneity is crucial for the propagation of uncertainty shocks. We build a heterogeneous agent New Keynesian model with search and matching frictions and Calvo pricing. Unemployment risk for imperfectly insured households amplifies their precautionary savings through increased uncertainty, thus further depressing consumption. Therefore, uncertainty shocks have considerably adverse effects and lead to a decrease in inflation.
-- 1 The Propagation of Uncertainty Shocks: Rotemberg vs. Calvo -- 2 The Long-Run Effects of Uncertainty Shocks -- 3 Macro Uncertainty and Unemployment Risk -- Bibliography -- Appendix A Appendix to Chapter 1 -- Appendix B Appendix to Chapter 2
Chapter 1 'The Propagation of Uncertainty Shocks: Rotemberg vs. Calvo' of the PhD thesis draws upon an earlier version published as EUI ECO WP 2019/01
Chapter 2 'The Long-Run Effects of Uncertainty Shocks' of the PhD thesis draws upon an earlier version published as Bank of England Staff WP 2019/802

Humans are unique in their ability to flexibly and rapidly adapt their behaviour and select courses of action that lead to future reward. Several ‘component processes’ must be implemented by the human brain in order to facilitate this behaviour. This thesis examines two such components; (i) the neural substrates supporting action selection during value- guided choice using magnetoencephalography (MEG), and (ii) learning the value of environmental stimuli and other people’s actions using functional magnetic resonance imaging (fMRI). In both situations, it is helpful to formally model the underlying component process, as this generates predictions of trial-to-trial variability in the signal from a brain region involved in its implementation. In the case of value-guided action selection, a biophysically realistic implementation of a drift diffusion model is used. Using this model, it is predicted that there are specific times and frequency bands at which correlates of value are seen. Firstly, there are correlates of the overall value of the two presented options, and secondly the difference in value between the options. Both correlates should be observed in the local field potential, which is closely related to the signal measured using MEG. Importantly, the content of these predictions is quite distinct from the function of the model circuit, which is to transform inputs relating to the value of each option into a categorical decision. In the case of social learning, the same reinforcement learning model is used to track both the value of two stimuli that the subject can choose between, and the advice of a confederate who is playing alongside them. As the confederate advice is actually delivered by a computer, it is possible to keep prediction error and learning rate terms for stimuli and advice orthogonal to one another, and so look for neural correlates of both social and non-social learning in the same fMRI data. Correlates of intentional inference are found in a network of brain regions previously implicated in social cognition, notably the dorsomedial prefrontal cortex, the right temporoparietal junction, and the anterior cingulate gyrus.

The aim of this thesis is the development of a framework for the systematic appraisal and communication of uncertainty in incinerator and landfill human health risk assessments. This aspiration has been in response to finding a limited and ambiguous use of the term 'uncertainty', a lack of consensus in its definition and nomenclature, and its perfunctory review in risk assessmentsI.n the light of the need for such a framework, implications of its introduction are discussed. Guided by the literature and data collected from the field, the development of the framework progressed in three phases. First, the general concept was analysed - immediate and wider goals were set, the target audience identified, the context of use determined, the elements of the framework defined and the needs and requirements of the target audience considered, giving rise to a set of criteria to guide its development. The second phase included the proposal of an initial framework, which would appraise and communicate uncertainty. In drafting the framework, the thesis explored the fundamental context in which uncertainty is placed, drew on the strengths of past definitions and classification schemes to redefine it and suggested a more comprehensive classification scheme which finds practical application in the proposed framework. A second interaction with the field enabled the refinement of the first version and production of a more robust, second version of the framework. Supported by case studies of both incinerator and landfill risk assessments, the third phase of the research involved the application of the framework as "scenarios of use' and its integrity discussed in terms of the set of heuristics developed in the concept analysis. The intention of the introduction of the proposed framework is to increase the transparency of risk assessments, which in turn could establish their reliability and trustworthiness, aid decision-making and allow for its management and subsequent refinement of the risk assessment practice. Although these are achieved to a certain degree, structural and methodological issues stemming from the complex and multidisciplinary nature of uncertainty, the intricacy of risk assessments and the unfamiliarity of the target audience with the fundamental concepts of uncertainty and the framework itself, resulted in both functionality and usability being compromised to a certain extent. Suggestions for future research are made.

Dans cette thèse, nous discutons du stress, de l'incertitude et de la multimodalité des mesures de risque en accordant une attention particulière à deux parties. Les résultats ont une influence directe sur le calcul du capital économique et réglementaire des banques. Tout d'abord, nous fournissons une nouvelle mesure de risque - la VaR du stress du spectre (SSVaR) - pour quantifier et intégrer l'incertitude de la valeur à risque. C'est un modèle de mise en œuvre de la VaR stressée proposée par Bâle III. La SSVaR est basée sur l'intervalle de confiance de la VaR. Nous étudions la distribution asymptotique de la statistique de l'ordre, qui est un estimateur non paramétrique de la VaR, afin de construire l'intervalle de confiance. Deux intervalles de confiance sont obtenus soit par le résultat gaussien asymptotique, soit par l'approche saddlepoint. Nous les comparons avec l'intervalle de confiance en bootstrapping par des simulations, montrant que l'intervalle de confiance construit à partir de l'approche saddlepoint est robuste pour différentes tailles d'échantillons, distributions sous-jacentes et niveaux de confiance. Les applications de test de stress utilisant SSVaR sont effectuées avec des rendements historiques de l'indice boursier lors d'une crise financière, pour identifier les violations potentielles de la VaR pendant les périodes de turbulences sur les marchés financiers. Deuxièmement, nous étudions l'impact de la multimodalité des distributions sur les calculs de la VaR et de l'ES. Les distributions de probabilité unimodales ont été largement utilisées pour le calcul paramétrique de la VaR par les investisseurs, les gestionnaires de risques et les régulateurs. Cependant, les données financières peuvent être caractérisées par des distributions ayant plus d'un mode. Avec ces données nous montrons que les distributions multimodales peuvent surpasser la distribution unimodale au sens de la qualité de l'ajustement. Deux catégories de distributions multimodales sont considérées: la famille de Cobb et la famille Distortion. Nous développons un algorithme d'échantillonnage de rejet adapté, permettant de générer efficacement des échantillons aléatoires à partir de la fonction de densité de probabilité de la famille de Cobb. Pour une étude empirique, deux ensembles de données sont considérés: un ensemble de données quotidiennes concernant le risque opérationnel et un scénario de trois mois de rendement du portefeuille de marché construit avec cinq minutes de données intraday. Avec un éventail complet de niveaux de confiance, la VaR et l'ES à la fois des distributions unimodales et des distributions multimodales sont calculés. Nous analysons les résultats pour voir l'intérêt d'utiliser la distribution multimodale au lieu de la distribution unimodale en pratique
In this thesis, we focus on discussing the stress, uncertainty and multimodality of risk measures with special attention on two parts. The results have direct influence on the computation of bank economic and regulatory capital. First, we provide a novel risk measure - the Spectrum Stress VaR (SSVaR) - to quantify and integrate the uncertainty of the Value-at-Risk. It is an implementation model of stressed VaR proposed in Basel III. The SSVaR is based on the confidence interval of the VaR. We investigate the asymptotic distribution of the order statistic, which is a nonparametric estimator of the VaR, in order to build the confidence interval. Two confidence intervals are derived from either the asymptotic Gaussian result, or the saddlepoint approach. We compare them with the bootstrapping confidence interval by simulations, showing that the confidence interval built from the saddlepoint approach is robust for different sample sizes, underlying distributions and confidence levels. Stress testing applications using SSVaR are performed with historical stock index returns during financial crisis, for identifying potential violations of the VaR during turmoil periods on financial markets. Second, we investigate the impact of multimodality of distributions on VaR and ES calculations. Unimodal probability distributions have been widely used for parametric VaR computation by investors, risk managers and regulators. However, financial data may be characterized by distributions having more than one modes. For these data, we show that multimodal distributions may outperform unimodal distribution in the sense of goodness-of-fit. Two classes of multimodal distributions are considered: Cobb's family and Distortion family. We develop an adapted rejection sampling algorithm, permitting to generate random samples efficiently from the probability density function of Cobb's family. For empirical study, two data sets are considered: a daily data set concerning operational risk and a three month scenario of market portfolio return built with five minutes intraday data. With a complete spectrum of confidence levels, the VaR and the ES from both unimodal distributions and multimodal distributions are calculated. We analyze the results to see the interest of using multimodal distribution instead of unimodal distribution in practice

Acid rock drainage (ARD) is a major environmental problem that causes local and global pollution. ARD occurs when sulfide bearing materials are exposed to oxygen and water during mining activities. This reaction between sulfide and oxygen with the presence of water generates elevated metals and metalloids that may cause potential environmental and human health risks. The remediation costs of potentially acid-generating wastes at abandoned minesites are estimated to be over $20 billion in USA. The major objective of this research is to propose a risk management framework for ARD that can improve the prediction of ARD chemistry, assess and manage environmental and human health risks to guide decision-making under uncertainty. The proposed framework consists of methodologies for filling in missing data, predict ARD chemistry, assess environmental risks, and manage risks of ARD. In the first methodology, missing values of ARD data are filled in using imputation algorithms that reduce loss of information and introduction of biases. After having the complete data, future ARD chemistry is predicted using machine learning techniques. The predictive uncertainty due to data, parameters and model is quantified using a probability bounds approach. Models are integrated using aggregation methods to reduce the uncertainty of the individual model. Case studies in minesites show that the developed methodology improves the prediction of future ARD chemistry under uncertainty. For ecological and human health risks assessment of ARD, two methodologies are developed based on the fugacity and PHREEQC approaches. The fugacity and PHREEQC approaches are applied in minesites with limited and adequate hydrogeological information, respectively. Case studies in minesites show that these methodologies are useful to quantify ecological and human health risks in the mining industry. In addition, they quantify the associated uncertainties in the risk assessments using the probability bounds and fuzzy-probabilistic approaches. For risk management of ARD, a methodology that uses multicriteria decision analysis (MCDA) and probabilistic approaches is developed. The methodology enables decision-makers to evaluate mitigation measures with various criteria, such as risk, cost, and technical feasibility, identifies the optimal mitigation measure, and quantifies the associated uncertainties in decision-making. In general, it is believed that the proposed framework enhances the decision-making ability of the mining industry under uncertainty and reduces environmental risks and remediation costs of managing ARD.
Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate

In the first chapter of this thesis, I use the smooth ambiguity model developed by Klibanoff, Marinacci, and Mukerji (2005) to define the concepts of ambiguity and uncertainty premia in a way analogous to what Pratt (1964) did in the risk theory literature. I show that these concepts may be useful to quantify the effect ambiguity has on the welfare of economic agents. I also define several other concepts such as the unambiguous probability equivalent or the ambiguous utility premium, provide local approximations of these different premia and show the link that exists between them when comparing different degrees of ambiguity aversion not only in the small, but also in the large.

In the second chapter, I analyze the effect of ambiguity on self-insurance and self-protection, that are tools used to deal with the uncertainty of facing a monetary loss when market insurance is not available (in the self-insurance model, the decision maker has the opportunity to furnish an effort to reduce the size of the loss occurring in the bad state of the world, while in the self-protection – or prevention – model, the effort reduces the probability of being in the bad state).

In a short note, in the context of a two-period model I first examine the links between risk-aversion, prudence and self-insurance/self-protection activities under risk. Contrary to the results obtained in the static one-period model, I show that the impacts of prudence and of risk-aversion go in the same direction and generate a higher level of prevention in the more usual situations. I also show that the results concerning self-insurance in a single period framework may be easily extended to a two-period context.

I then consider two-period self-insurance and self-protection models in the presence of ambiguity and analyze the effect of ambiguity aversion. I show that in most common situations, ambiguity prudence is a sufficient condition to observe an increase in the level of effort. I propose an interpretation of the model in the context of climate change, so that self-insurance and self-protection are respectively seen as adaptation and mitigation efforts a policy-maker should provide to deal with an uncertain catastrophic event, and interpret the results obtained as an expression of the Precautionary Principle.

In the third chapter, I introduce the economic theory developed to deal with ambiguity in the context of medical decision-making. I show that, under diagnostic uncertainty, an increase in ambiguity aversion always leads a physician whose goal is to act in the best interest of his patient, to choose a higher level of treatment. In the context of a dichotomic choice (treatment versus no treatment), this result implies that taking into account the attitude agents generally manifest towards ambiguity may induce a physician to change his decision by opting for treatment more often. I further show that under therapeutic uncertainty, the opposite happens, i.e. an ambiguity averse physician may eventually choose not to treat a patient who would have been treated under ambiguity neutrality.

Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge.

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2005.
Includes bibliographical references (p. 66-68).
Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the Risk Achievement Worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ACDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important.
(cont.) The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study. We then look at policy issues surrounding the effects of uncertainty in decision making related to nuclear power generation.
by Joshua M. Reinert.
S.M.

This thesis considers the uncertainty in forecasts of climate-driven disease risk, focusing on seasonal and decadal timescales. An analysis of the skill of decadal climate predictions is carried out, looking at the first multi-model decadal hindcast set produced as part of the ENSEMBLES project. Some skill in the prediction of global average temperature trends over the forthcoming decade is shown, with no skill evident for precipitation. Focusing on smaller areas shows limited skill in predicting temperature trends and no skill for precipitation trends, suggesting that decadal climate models cannot currently make useful predictions of disease risk. Seasonal climate forecasting skill is then considered. Seasonal hindcasts produced by two research projects, DEMETER and ENSEMBLES, are compared with the most recent version of the European Centre for Medium-Range Weather Forecast’s seasonal forecast model, System 4. The models are validated over Africa and the Indian subcontinent, and it is shown that in general System 4 forecasts are an improvement over the DEMETER and ENSEMBLES multimodel ensembles, particularly for West Africa. A more in depth study of System 4 is subsequently carried out, comparing the variation in skill between forecast start dates. Forecast value is demonstrated at multiple lead times, with most skill found for West African regions and Botswana and limited skill for India; indicating when and where forecasts can potentially be issued to users. Forecasting malaria is then studied by using Liverpool Malaria Model (LMM) driven by System 4. Skill is demonstrated over Botswana, particularly for forecasts issued in November, validating against laboratory confirmed cases of malaria. This is an improvement on previous work where the LMM was driven with the DEMETER seasonal hindcasts. Where malaria data is not available, System 4-driven LMM hindcasts are compared to LMM driven by ERA-Interim in a tier-2 validation context. Skill is demonstrated at the epidemic fringe of the Sahel and in north west Malawi, whilst the Gulf of Guinea shows no skill. This is consistent with previous work suggesting the LMM performs better in epidemic than in endemic regions. A method for interpreting hindcast validation results as uncertainty quantification is then presented. Finally, the uncertainty in the relationship between seasonal average climate and malaria risk is analysed, using the LMM driven by the 20th century reanalysis dataset. The relationship parameters describing seasonal average climate and malaria risk is explored and impact surfaces are created, relating seasonal average temperature and precipitation to average seasonal malaria incidence. The robustness of these impact surfaces is investigated by comparing the surfaces associated with different LMM survival schemes. A method of combining impact surfaces based on tercile categories is described and implemented and it is demonstrated how the resulting graphic could be integrated with a seasonal ensemble forecast system. Such a tool is potentially useful for decision-makers, allowing an intuitive visual communication of the quantified uncertainty in predicting climate-driven disease risk at seasonal timescales.

This dissertation applies a mixed-methods model across three cases of climate risk in order to examine the rhetorical dynamics of uncertainties. I argue that a rhetorical approach to uncertainties can effectively scaffold civic agency in risk communication by translating conflicting interests and creating sites of public participation. By tracing the networks of scientists and their artifacts through cases of climate risk, I demonstrate how the performances of scientific ethos and their material-discursive technologies facilitate the personalization of risk as a form of scientific prudence, and thus a channel to feasible political action. I support these claims through a rhetorical model of translation, which hybridizes methods from discourse analysis and Actor-Network Theory (ANT) in order assemble a data-driven and corpus-based approach to rhetorical analysis. From this rhetorical perspective uncertainties expand on our notions of risk because they reveal associations between scientific inquiries, probability assessments, and the facilitation of political dialogues. In each case, the particular insight of the model reveals a range of rhetorical potentials in climate risk that can be confronted through uncertainties.

This thesis adds to the resolution of two problems in finance and economics: i) what is macro-financial uncertainty? : How to measure it? How is it different from risk? How important is it for the financial markets? And ii) what sort of asymmetries underlie financial risk and uncertainty propagation across the global financial markets? That is, how risk and uncertainty change according to factors such as market states or market participants. In Chapter 2, which is entitled “Momentum Uncertainties”, I study the relationship between macroeconomic uncertainty and the abnormal returns of a momentum trading strategy in the stock market. I show that high levels of uncertainty in the economy impact negatively and significantly the returns of a portfolio of stocks that consist of buying past winners and selling past losers. High uncertainty reduces below zero the abnormal returns of momentum, extinguishes the Sharpe ratio of the momentum strategy, while increases the probability of momentum crashes both by increasing the skewness and the kurtosis of the momentum return distribution. Uncertainty acts as an economic regime that underlies abrupt changes over time of the returns generated by momentum strategies. In Chapter 3, “Measuring Uncertainty in the Stock Market”, I propose a new index for measuring stock market uncertainty on a daily basis. The index considers the inherent differentiation between uncertainty and the common variations between the series. The second contribution of chapter 3 is to show how this financial uncertainty index can also serve as an indicator of macroeconomic uncertainty. Finally, I analyze the dynamic relationship between uncertainty and the series of consumption, interest rates, production and stock market prices, among others. In chapter 4: “Uncertainty, Systemic Shocks and the Global Banking Sector: Has the Crisis Modified their Relationship?”, I explore the stability of systemic risk and uncertainty propagation among financial institutions in the global economy, and show that it has remained stable over the last decade. Additionally, I provide a new simple tool for measuring the resilience of financial institutions to these systemic shocks. My contribution to the literature in this essay is mainly the examination of the characteristics and stability of systemic risk and uncertainty, in relation to the dynamics of the banking sector stock returns. This sort of evidence is new to the literature and is supportive of past claims, made in the field of macroeconomics, which hold that during the global financial crisis the financial system may have faced stronger versions of traditional shocks rather than a new type of shock. In chapter 5, “Currency downside risk, liquidity, and financial stability”, I analyze downside risk propagation across global currency markets and the ways in which it is related to liquidity. I make two primary contributions to the literature. First, I estimate tail-spillovers between currencies in the global FX market. This index is easy to build and does not require intraday data, which constitutes an important advantage. Second, I show that turnover is related to risk spillovers in global currency markets. Chapter 6 is entitled “Spillovers from the United States to Latin American and G7 Stock Markets: A VAR-Quantile Analysis”. This essay contributes to the studies of contagion, market integration and cross-border spillovers during both regular and crisis episodes by carrying out a multivariate quantile analysis. I focus the analysis carried out in this chapter on Latin American stock markets, which have been characterized by a highly positive dynamic in recent decades, in terms of market capitalization and liquidity ratios, after a far-reaching process of market liberalization and reforms to pension funds across the continent during the 80s and 90s. I documented smaller dependences between the LA markets and the US market than those between the US and the developed economies, especially in the highest and lowest quantiles.
En esta tesis se exploran formas óptimas de medir la incertidumbre macroeconómica y sus impactos sobre la actividad económica y los mercados financieros; así como la propagación internacional del riesgo en los mercados de acciones y de divisas. En el primer capítulo de la tesis se muestra que los retornos de las estrategias de inversión basadas en extrapolar los ganadores y perdedores recientes en el mercado, con el fin de decidir en que títulos invertir en el futuro (momentum), son susceptibles al nivel de incertidumbre registrado en la economía. Cuando la incertidumbre es alta, este tipo de inversiones se vuelven sumamente riesgosas y poco rentables, y por tanto no son recomendables. En el segundo capítulo de la tesis se propone un índice de incertidumbre construido con retornos diarios del mercados de acciones, el cual presenta mejores propiedades que otras alternativas en la literatura. Se utiliza este índice para mostrar las dinámicas macroeconómicas que siguen a un choque de incertidumbre, las cuales son examinadas a la luz de la literatura teórica al respecto. En el tercer capítulo de la tesis se examinan la propagación de la incertidumbre y el riesgo sistémico a las entidades bancarias globales, se estima un modelo de riesgo sistémico que permite mostrar como la propagación del riesgo ha permanecido estable durante las últimas décadas, y además, permite ofrecer nuevas listas de instituciones financieras vulnerables ante los choques de naturaleza sistémica en el mercado, que complementan las que actualmente existen en la literatura y en la práctica regulatoria. En el cuarto capítulo de la tesis se propone un indicador de estabilidad financiera para el mercado de divisas. Tal indicador se basa en el análisis de los cuantiles de depreciación del mercado de divisas, que por definición son de mayor interés para los reguladores, en cuanto está relacionados con las posibilidades de crisis cambiarias. Las asimetrías en la propagación de choques internaciones que se registran durante las depreciaciones (en comparación con los periodos de apreciación) se analizan a la luz del factor de liquidez en el mercado. En el quinto y último capítulo se analiza el efecto choques provenientes del mercado de acciones de Estados Unidos, sobre 6 mercados maduros y seis mercados emergentes de Latino América. Se muestra que la propagación depende del momento en el que se encuentre el mercado al momento de registrarse el choque (al alza o a la baja) y se proponen estrategias de diversificación internacional de portafolios de activos financieros.

The overall purpose of the paper is to see how crediting institutions assess risks in emerging market countries. The paper describes prevalent economic and social conditions for each of the selected emerging market countries (Brazil, China, Kazakhstan, India, Russia and Ukraine) as examples of recent attractive investment locations in quest of higher returns.  Second, recognizing the importance of ratings for risk management in credit institutions, the authors show what determines country ratings made by main rating agencies by running a linear regression on several macroeconomic indicators and the country ratings. It is also explained what the most widely-used ratings mean and described the correlation between the ratings as well as between the macroeconomic indicators and the ratings. The authors also describe the characteristic approach of a Scandinavian bank towards dealing with risk factors in emerging market countries. Concluding comments: risks happen to be inbound in the bank interest rates; there is no common pattern for banks to apply to all the emerging market countries and each market should be analyzed separately. Nordic banks have a relatively safe and careful strategy concerning lending in the emerging markets.

 

Real-world problems especially the ones that involve natural systems are complex and they are composed of many non-deterministic components. Uncertainties associated with these non-deterministic components may originate from randomness or from imprecision due to lack of information. Until recently, uncertainty, regardless of its nature or source has been treated by probability concepts. However, uncertainties associated with real-world systems are not limited to randomness. Imprecise, vague or incomplete information may better be represented by other mathematical tools, such as fuzzy set theory, possibility theory, belief functions, etc. New approaches which allow utilization of probability theory in combination with these new mathematical tools found applications in various engineering fields. Uncertainty modeling in human health risk assessment and groundwater resources management areas are investigated in this thesis. In the first part of this thesis two new approaches which utilize both probability theory and fuzzy set theory concepts to treat parameter uncertainties in carcinogenic risk assessment are proposed. As a result of these approaches fuzzy health risks are generated. For the fuzzy risk to be useful for practical purposes its acceptability with respect to compliance guideline has to be evaluated. A new fuzzy measure, the risk tolerance measure, is proposed for this purpose. The risk tolerance measure is a weighed average of the possibility and the necessity measures which are currently used for decision making purposes. In the second part of this thesis two decision making frameworks are proposed to determine the best groundwater resources management strategy in the Savannah region, Georgia. Groundwater resources management problems, especially ones in the coastal areas are complex and require treatment of various uncertain inputs. The first decision making framework proposed in this study is composed of a coupled simulation-optimization model followed by a fuzzy multi-objective decision making approach while the second framework includes a groundwater flow model in which the parameters of the flow equation are characterized by fuzzy numbers and a decision making approach which utilizes the risk tolerance measure proposed in the first part of this thesis.

I combine theory, experiments and econometrics to undertake the task of disentangling the subtleties and implications of the distinction between risk, uncertainty and ambiguity. One general conclusion is that the elements of this methodological trilogy are not equally advanced. For example, new experimental tools must be developed to adequately test the predictions of theory. My dissertation is an example of this dynamic between theoretical and applied economics.

UNIVERSIDADE FEDERAL DE ALAGOAS
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
A análise quantitativa de risco à saúde humana (AqR) devido a uma determinada área contaminada vem se verificando como importante ferramenta na gestão ambiental bem como a concretização de dano ambiental, tanto no Brasil como em outros países. Os procedimentos para AqR consistem em passos seqüenciados de forma orgânica e lógica e englobam características legais, aspectos toxicológicos e mecanismos de transporte. Apesar de não haver uma lei específica que regule a AqR, o Direito Ambiental permite que estas metodologias sejam plenamente aplicadas tanto no âmbito administrativo quanto no âmbito judicial para a caracterização de dano ambiental. As metodologias de AqR se valem de modelos fármaco-cinéticos que relacionam a exposição ao composto químico à possibilidade de causar danos à saúde humana. A Geotecnia Ambiental estuda o transporte e comportamento dos contaminantes nos solos e nas águas subterrâneas. A AqR se mostra um problema complexo e permeado por inúmeras incertezas e variabilidades. Foi proposta a utilização do método do segundo momento de primeira ordem (FOSM) para quantificar as incertezas relacionadas com a estimativa dos parâmetros de transporte a serem usadas em um modelo analítico de transporte de soluto em meios porosos (Domenico). O estudo de caso consiste na aplicação do programa desenvolvido para esta finalidade (SeRis). O método se mostra computacionalmente econômico e o estudo de caso, dentro das idealizações, identificou os parâmetros com maior importância relativa e apresentou uma variância total razoável para o resultado.
The quantitative human health risk assessment (AqR) due a contaminated site has became an important tool at Environmental Managenment and at the identification of environmental harm, at Brazil and other countries. The AqR procedures consists in logical sequence of actions concerned about legal aspects, toxicological matter and transport phenomena. In spite of the absence of a single law that could regulate specifically the AqR, the Environmental Law, as a whole, allows that AqR methodologies to be fully applied at governamental and judicial levels. The AqR procedures are base on pharmaco-kinetics models that quantitatively relates the exposure to the chemicals to human harm potency. The Environmental Geotechnics studies the fate and transport of contaminants at soil and groundwater. AqR is complex and full of uncertainties and variabilities subject. It have been proposed the application of the first order second moment method (FOSM) to quantify the uncertainties related to the estimation of the transport parameters to be used in the analytical transport model of solute in porous media (Domenico). It have been developed a specific software that meets this objective (SeRis). This software proved to be computationally efficient. The case study example indicated the relative importance of the considered parameters and presented a reasonable total system variance.

Various methods can be used to estimate the human health risk associated with exposure to contaminants at brownfields facilities. The deterministic method has been the standard practice, but the use of probabilistic methods is increasing. Contaminant data for non-carcinogens and carcinogens from 21 brownfields sites in Pennsylvania were collected and compiled. These were used to evaluate the performance of the deterministic and several probabilistic methods for assessing exposure and risk in relation to variability and uncertainty in the data set and input parameters. The probabilistic methods were based (a) entirely on Monte Carlo simulated input parameter distribution functions, (b) on a combination of some of these functions and fixed parameter values, or (c) on a parameter distribution function. These methods were used to generate contaminant intake doses, defined as the 90th, 95th, or 99.9th percentile of their estimated output distribution, for the principal human exposure routes. These values were then compared with the corresponding point values estimated by the deterministic method. For all exposure routes the probabilistic intake dose estimates, taken as the 90th and 95th percentiles of the output distribution, were not markedly different from the deterministic values or from each other. The opposite was generally the case for the estimates at the 99.9th cutoff percentile; especially for the Monte Carlo-based methods. Increasing standard deviation of the input contaminant concentration tended to produce higher intake dose estimates for all estimation methods. In pairwise comparisons with the deterministic estimates, this trend differed significantly only for the probabilistic intake doses estimated as the 99.9th percentile of the output distribution. Taken together, these results did not indicate clear and definitive advantages in using probabilistic methods over the deterministic method for exposure and risk assessment for brownfields redevelopment. They supported using the tired system for environmental risk assessment at any particular brownfields facility.
Master of Science

Flooding in the UK is one of the mostly costly natural hazards. Reliable estimation of flood risk is becoming increasingly relevant to flood management practices as the insurance industry, planning decisions and allocation of flood resources are encouraged to move towards a fully risk-based methodology. This thesis describes the implementation of a flood modelling chain to estimate the flood risk and quantify the associated uncertainty for the city of Carlisle, UK. Observational data from an extreme flood in January, 2005 is analysed to estimate its accuracy, then a method of reducing inconsistencies in the measurements is proposed. The observational dataset is used to condition a hydraulic flood model for the area. The potential benefit of implementing risk-based calibration schemes as an alternative to a global scheme that gives equal weight to all observations is investigated and found to be minimal in this instance. A flood frequency curve for peak flood discharges in Carlisle is derived using a Bayesian statistical model that combines estimates of historic floods with systematic river gauge data. The uncertainty in the resulting flood frequency curve reflects errors in estimates of peak flood discharge, changes in the channel and floodplain as well as the uncertainty arising from the limited length of the gauge data and compares favourably against the current ‘best practice’ methods. The flood frequency curve is used to drive the hydraulic model in a series of Monte Carlo simulations to give probabilistic maps of design floods for Carlisle. Spatial dependence between river flows and variability in flood hydrographs are incorporated in the Monte Carlos simulations. The uncertain consequences of the floods are examined in terms of financial risk, and risk to population and property. A social research project using semi-structured interviews attempts to establish the relevance of the results to urban planning, the insurance industry and flood management resource allocation. Discussion of uncertainty in flood risk in a broad context suggests a high level of awareness, but not prioritisation with no accepted standard of communication.

In this dissertation, we study the effects of emerging biomass markets on land use changes between alternatives of agricultural production, conventional timber production, and forest woody biomass production for energy use. Along with the uncertainty associated with woody biomass prices and rents, transaction costs incurred to land use play an important role in land allocation decisions and make this study distinct from other work. In Chapter 1, we introduce the background and objectives of our study. In Chapter 2, we analyze the behavior of a risk-neutral private landowner and social planner under uncertainty of woody biomass prices, assuming that there is a market emergence at some unknown time point in the future. Market emergence is characterized by a price jump and a certain timing of the price jump. Six different price jumps and five different timings of bioenergy market emergence are adopted to study their collective effects on land use change between agriculture and forestry. Chapter 3 studies this problem for a risk-averse private landowner. Two measures of relative risk aversion are used to examine how a landownerâ s preference may affect his or her land use decision. In Chapter 2, we find that, for three different quality categories of land, land rents from forestry increase significantly for higher price jumps and decreases in the length of time until bioenergy market emergence. One of the most important results is concerned with the presence of transaction costs. Here, we find that these costs may require unrealistic market emergence scenarios to lead to bioenergy adoption on any large scale. This result is even more likely with nonlinear transaction costs. Land allocation decisions in Chapter 3 are distinctly different from those in Chapter 2, due to the introduction of landowner risk aversion. In certain market emergence cases, some land units retain in agriculture entirely when the landowner is risk averse . The Chapter 4 studies a stochastic optimization problem of land use, assuming that woody biomass rents follow a stochastic diffusion called geometric Brownian motion that is later discretized by a binomial option pricing approach. The problems in Chapters 2 and 3 assume that the landowner must make all decisions at the beginning of his or her time horizon. This assumption is relaxed in Chapter 4. Now, the landowner is allowed to revise his or her land allocation decision among three alternatives over time as information about market emergence is collected. We observe that the different forms of transaction costs are not as significant as in Chapters 2 and 3. However, different values of volatility of forest biomass rents give rise to different land allocation decisions, especially for the land of high quality.
Ph. D.

We know that it is difficult to forecast the future, and the same applies to project management. There are a lot of occurrences that are both predictable and not possible throughout the course of a project. The management of uncertainty is a management method that helps to forecast and adjust to future occurrences. In this context, frameworks and tools have been created to help project managers genuinely understand a project's potential.  The study was conducted as a case study at Rimaster Development in Söderhamn and aimed at discovering sources of uncertainty within their project development that affect project performance. The study is built on both quantitative and qualitative data gathering from a workshop, survey, meetings, and interviews from ten different projects of Rimaster. The study had five respondents who contributed with two projects, each of which they had been a part of previously, one classified as uncertain and one as more certain.  The data were analyzed with the statistics tool SPSS, where variables were computed based on the empirical findings in the uncertainty survey. The result was then presented through uncertainty attribute matrices to identify how each project performed in relation to customer satisfaction as well as time plan and budget. Here, dependent variables were tested, such as if the uncertainty score (the higher value, the more uncertain is the project) influenced project performance. From the data, two categories of projects could be developed: Higher performing projects and lower performing projects. To this, significant analysis of the performance groups was performed to find parameters that were significantly different between the categories. To capture more data to the thesis, each project was then evaluated through an individual project analysis which consisted of analyzing the data from the survey more closely for each project. The findings from this were then supported with qualitative interviews.  The findings in the study suggest that there is a relationship between project uncertainty and lower-performing projects in relationship to time plan and budget. Further, sixteen uncertainty parameters showed to have a significant value in the comparison between higher and lower performing projects. Based upon the data and the analysis of the study, managerial recommendations have been provided to Rimaster on tools and development processes that may assist the organization's uncertainty management and increase their project performance.
Det är allmänt känt att det är omöjligt att tydligt förutsäga framtiden och detsamma gäller projektledning. Det finns många händelser som är både oförutsägbara och riskfyllda under ett projektets gång, där osäkerhetshanteringen är en hanteringsmetod som hjälper till att prognostisera och anpassa sig till framtida händelser. Tidigare forskning inom osäkerhetshantering har utvecklat ramar och verktyg för att hjälpa projektledare att verkligen förstå ett projekts potential.  Studien genomfördes som en fallstudie vid Rimaster Development i Söderhamn och syftade till att upptäcka källor till osäkerhet inom deras utvecklingsavdelning som påverkar projektets prestanda. Studien bygger på både kvantitativ och kvalitativ datainsamling från en workshop, enkätundersökning, möten och intervjuer från tio olika projekt från Rimaster. Studien enkätundersökning hade fem svarande som bidrog med två projekt som de tidigare hade varit en del av, ett klassificerat som osäkert och ett som mer säkert.  Data erhållen from enkäten analyserades med statistikverktyget SPSS där variabler beräknades baserat på empiriska resultat i osäkerhetsundersökningen. Resultatet presenterades sedan genom osäkerhetsattributmatriser för att identifiera hur varje projekt presterade i förhållande till kundnöjdhet samt tidsplan och budget. Här testades beroende variabler som om den osäkerhetspoängen varde projekt erhöll (ju högre värde, desto mer osäkert är projektet) hade en effekt på projektets prestanda. Från analysen kunde två kategorier av projekt utvecklas: projekt med högre prestanda och projekt med lägre prestanda. Utöver detta genomfördes en signifikansanalys av prestationsgrupperna för att hitta parametrar som var signifikanta olika mellan kategorierna. För att samla ytterliga data till avhandlingen utvärderades varje projekt genom en individuell projektanalys som bestod av att analysera data från undersökningen närmare för varje projekt. Resultaten från detta styrktes sedan med kvalitativa intervjuer.  Resultaten i studien antyder att det finns ett samband mellan projektosäkerhet och projekt med lägre resultat i förhållande till tidsplan och budget. Vidare visade sig sexton osäkerhetsparametrar ha ett signifikant värde i jämförelsen mellan projekt med högre och lägre resultat. Baserat på analyseringen av den data studien samlat in så har rekommendationer överlämnats till Rimaster om verktyg och utvecklingsprocesser som kan hjälpa organisationens osäkerhetshantering och därmed öka deras projektprestanda.

This study contributes to a growing body of literature on uncertainty communication. It uses quantitative and qualitative methods to explore a user-centred framework for uncertainty communication design for the non-expert audience, and a user study investigating how uncertainty representation methods influence behaviour of non-expert audiences in an online game.

This thesis explores fifteen women’s accounts of pregnancy over the course of gestation. It highlights the fluidity and dynamism of these women’s experiences, placing these in the context of the breadth of medical interventions they engaged with. Much existing literature concerning pregnancy focuses on specific instances of contact with medical professionals or technological interventions. This study explores the mundane and routine elements of the everyday practice of pregnancy, including during the first trimester. This is a period rarely addressed in academic literature. The thesis draws on data from in-depth interviews with women in Scotland, experiencing a continuing pregnancy for the first time. These were conducted at three points over the course of gestation. Interviews aimed to explore women’s interactions with medical interventions, their conceptualisations of the foetus, and changing experiences of embodiment. Analysis took place in several stages, incorporating three ‘readings’ of interviews and the development of a case study for each participant. This was inspired by the voice centred relational method of analysis. Themes were then identified and developed within, and between, individual women’s accounts. Participants’ narratives, particularly in early pregnancy, resonated with Rothman’s (1988) concept of the ‘tentative pregnancy’, originally developed to describe pregnancy in the wake of amniocentesis. Tentativeness emerged as a key theme characterising women’s experiences. Tentativeness was especially evident during the first trimester, largely due to women’s understanding that the risk of miscarriage was at its highest during this period. Women described managing their emotions at this time, in order to balance excitement about their wanted pregnancy with the possibility that it may end in a pregnancy loss. One aspect of this emotion work, explored in this thesis, was the effort made by women to keep their pregnancy a secret from wider family and friends for the first twelve weeks of gestation. Medical intervention and its associated technologies played a key role in both constructing pregnancy as tentative, but paradoxically, also provided a means to resolve this through reassurance. Women engaged with these interventions flexibly. In contrast to much existing literature, this thesis highlights that while contact with prenatal technologies cemented the reality of the pregnancy for some, they also had the power to add to the ambiguity of participants’ status as a ‘pregnant woman’. In later pregnancy, women’s shifting embodied experiences contributed to a reduction in tentativeness. The ability to feel definite foetal movements, coupled with medical and popular discourses of foetal viability, allowed women to feel less anxious about the safety of the pregnancy and the foetus. As a result, women reported changed interactions with health professionals and advice during the final trimester of pregnancy. This thesis, engaging with literature from sociology, science and technology studies (STS) and anthropology, makes theoretical contributions in three areas. First, its consideration of gestation over time nuances discussions of pregnancy in terms of risk. Second, this research further contributes to literature regarding pregnant embodiment, and conceptualisations of the foetus. Third, the thesis demonstrates that relationships between forms of knowledge mobilised by participants during pregnancy were complex, shifting over the course of gestation, and reflective of women’s experiences of pregnancy as tentative.

Real-world problems, especially those that involve natural systems, are complex and composed of many nondeterministic components having non-linear coupling. It turns out that in dealing with such systems, one has to face a high degree of uncertainty and tolerate imprecision. Classical system models based on numerical analysis, crisp logic or binary logic have characteristics of precision and categoricity and classified as hard computing approach. In contrast soft computing approaches like probabilistic reasoning, fuzzy logic, artificial neural nets etc have characteristics of approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost of computation, effective communication and high Machine Intelligence Quotient (MIQ). Proposed thesis has tried to explore use of different soft computing approaches to handle uncertainty in environmental risk management. The work has been divided into three parts consisting five papers.
In the first part of this thesis different uncertainty propagation methods have been investigated. The first methodology is generalized fuzzy α-cut based on the concept of transformation method. A case study of uncertainty analysis of pollutant transport in the subsurface has been used to show the utility of this approach. This approach shows superiority over conventional methods of uncertainty modelling. A Second method is proposed to manage uncertainty and variability together in risk models. The new hybrid approach combining probabilistic and fuzzy set theory is called Fuzzy Latin Hypercube Sampling (FLHS). An important property of this method is its ability to separate randomness and imprecision to increase the quality of information. A fuzzified statistical summary of the model results gives indices of sensitivity and uncertainty that relate the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method is validated to analyze total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in Basque Country, Spain.
The second part of this thesis deals with the use of artificial intelligence technique for generating environmental indices. The first paper focused on the development of a Hazzard Index (HI) using persistence, bioaccumulation and toxicity properties of a large number of organic and inorganic pollutants. For deriving this index, Self-Organizing Maps (SOM) has been used which provided a hazard ranking for each compound. Subsequently, an Integral Risk Index was developed taking into account the HI and the concentrations of all pollutants in soil samples collected in the target area. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a Geographic Information System (GIS). The second paper is an improvement of the first work. New approach called Neuro-Probabilistic HI was developed by combining SOM and Monte-Carlo analysis. It considers uncertainty associated with contaminants characteristic values. This new index seems to be an adequate tool to be taken into account in risk assessment processes. In both study, the methods have been validated through its implementation in the industrial chemical / petrochemical area of Tarragona.
The third part of this thesis deals with decision-making framework for environmental risk management. In this study, an integrated fuzzy relation analysis (IFRA) model is proposed for risk assessment involving multiple criteria. The fuzzy risk-analysis model is proposed to comprehensively evaluate all risks associated with contaminated systems resulting from more than one toxic chemical. The model is an integrated view on uncertainty techniques based on multi-valued mappings, fuzzy relations and fuzzy analytical hierarchical process. Integration of system simulation and risk analysis using fuzzy approach allowed to incorporate system modelling uncertainty and subjective risk criteria. In this study, it has been shown that a broad integration of fuzzy system simulation and fuzzy risk analysis is possible.
In conclusion, this study has broadly demonstrated the usefulness of soft computing approaches in environmental risk analysis. The proposed methods could significantly advance practice of risk analysis by effectively addressing critical issues of uncertainty propagation problem.
Los problemas del mundo real, especialmente aquellos que implican sistemas naturales, son complejos y se componen de muchos componentes indeterminados, que muestran en muchos casos una relación no lineal. Los modelos convencionales basados en técnicas analíticas que se utilizan actualmente para conocer y predecir el comportamiento de dichos sistemas pueden ser muy complicados e inflexibles cuando se quiere hacer frente a la imprecisión y la complejidad del sistema en un mundo real. El tratamiento de dichos sistemas, supone el enfrentarse a un elevado nivel de incertidumbre así como considerar la imprecisión. Los modelos clásicos basados en análisis numéricos, lógica de valores exactos o binarios, se caracterizan por su precisión y categorización y son clasificados como una aproximación al hard computing. Por el contrario, el soft computing tal como la lógica de razonamiento probabilístico, las redes neuronales artificiales, etc., tienen la característica de aproximación y disponibilidad. Aunque en la hard computing, la imprecisión y la incertidumbre son propiedades no deseadas, en el soft computing la tolerancia en la imprecisión y la incerteza se aprovechan para alcanzar tratabilidad, bajos costes de computación, una comunicación efectiva y un elevado Machine Intelligence Quotient (MIQ). La tesis propuesta intenta explorar el uso de las diferentes aproximaciones en la informática blanda para manipular la incertidumbre en la gestión del riesgo medioambiental. El trabajo se ha dividido en tres secciones que forman parte de cinco artículos.
En la primera parte de esta tesis, se han investigado diferentes métodos de propagación de la incertidumbre. El primer método es el generalizado fuzzy α-cut, el cual está basada en el método de transformación. Para demostrar la utilidad de esta aproximación, se ha utilizado un caso de estudio de análisis de incertidumbre en el transporte de la contaminación en suelo. Esta aproximación muestra una superioridad frente a los métodos convencionales de modelación de la incertidumbre. La segunda metodología propuesta trabaja conjuntamente la variabilidad y la incertidumbre en los modelos de evaluación de riesgo. Para ello, se ha elaborado una nueva aproximación híbrida denominada Fuzzy Latin Hypercube Sampling (FLHS), que combina los conjuntos de la teoría de probabilidad con la teoría de los conjuntos difusos. Una propiedad importante de esta teoría es su capacidad para separarse los aleatoriedad y imprecisión, lo que supone la obtención de una mayor calidad de la información. El resumen estadístico fuzzificado de los resultados del modelo generan índices de sensitividad e incertidumbre que relacionan los efectos de la variabilidad e incertidumbre de los parámetros de modelo con las predicciones de los modelos. La viabilidad del método se llevó a cabo mediante la aplicación de un caso a estudio donde se analizó la varianza total en la cálculo del incremento del riesgo sobre el tiempo de vida de los habitantes que habitan en los alrededores de una incineradora de residuos sólidos urbanos en Tarragona, España, debido a las emisiones de dioxinas y furanos (PCDD/Fs).
La segunda parte de la tesis consistió en la utilización de las técnicas de la inteligencia artificial para la generación de índices medioambientales. En el primer artículo se desarrolló un Índice de Peligrosidad a partir de los valores de persistencia, bioacumulación y toxicidad de un elevado número de contaminantes orgánicos e inorgánicos. Para su elaboración, se utilizaron los Mapas de Auto-Organizativos (SOM), que proporcionaron un ranking de peligrosidad para cada compuesto. A continuación, se elaboró un Índice de Riesgo Integral teniendo en cuenta el Índice de peligrosidad y las concentraciones de cada uno de los contaminantes en las muestras de suelo recogidas en la zona de estudio. Finalmente, se elaboró un mapa de la distribución espacial del Índice de Riesgo Integral mediante la representación en un Sistema de Información Geográfico (SIG). El segundo artículo es un mejoramiento del primer trabajo. En este estudio, se creó un método híbrido de los Mapas Auto-organizativos con los métodos probabilísticos, obteniéndose de esta forma un Índice de Riesgo Integrado. Mediante la combinación de SOM y el análisis de Monte-Carlo se desarrolló una nueva aproximación llamada Índice de Peligrosidad Neuro-Probabilística. Este nuevo índice es una herramienta adecuada para ser utilizada en los procesos de análisis. En ambos artículos, la viabilidad de los métodos han sido validados a través de su aplicación en el área de la industria química y petroquímica de Tarragona (Cataluña, España).
El tercer apartado de esta tesis está enfocado en la elaboración de una estructura metodológica de un sistema de ayuda en la toma de decisiones para la gestión del riesgo medioambiental. En este estudio, se presenta un modelo integrado de análisis de fuzzy (IFRA) para la evaluación del riesgo cuyo resultado depende de múltiples criterios. El modelo es una visión integrada de las técnicas de incertidumbre basadas en diseños de valoraciones múltiples, relaciones fuzzy y procesos analíticos jerárquicos inciertos. La integración de la simulación del sistema y el análisis del riesgo utilizando aproximaciones inciertas permitieron incorporar la incertidumbre procedente del modelo junto con la incertidumbre procedente de la subjetividad de los criterios. En este estudio, se ha demostrado que es posible crear una amplia integración entre la simulación de un sistema incierto y de un análisis de riesgo incierto.
En conclusión, este trabajo demuestra ampliamente la utilidad de aproximación Soft Computing en el análisis de riesgos ambientales. Los métodos propuestos podría avanzar significativamente la práctica de análisis de riesgos de abordar eficazmente el problema de propagación de incertidumbre.

An increasing number of companies are focusing their efforts on project management. Project management is frequently used as an enabler for meeting an uncertain and turbulent environment. Consequently, the overall effectiveness of the project management process is essential for long-term profitability. The aim and final effects of project management are to predict the outcome, i.e. cost, time and quality. However, uncertainty is inherent in the objectives of the project itself, as we use assumptions and expectations in defining and realizing the outcome of the project. A project’s ability to identify and react to uncertainty will influence the outcome of the project. Presently, risk management processes exist in several forms and are often used to manage uncertainty. However, it is frequently argued in academia as well as for the practitioner that risk management does not live up to expected results.

The overall objective of this research is to improve the process for managing risks and opportunities within a project organization. The research starts from the single project view, followed by the strategic link to business strategy by including the project portfolio management perspective. Finally, the research focuses on opportunities and the ability of a project to realize them. Thus, the research questions addressed concern how risk is conceived in a theoretical global context and how this would assist in developing a methodology for risk management in an international project organization. They also involve how risk management within a project portfolio could be conducted and its effectiveness measured. Finally, the research questions also address how the management of opportunities could be improved.

This research includes the development of four methodologies, based on industrial need. A holistic approach with a systems perspective has been used in order to handle the complexity of the research task. Both empirical and theoretical material has been used for developing the proposed methodologies. The developed methodologies for project risk management and the measures of its effectiveness have been tested and improved over a five-year period within the complete case company. Subsequently, two of them were implemented.

The developed methodologies show that the risk management process in a single project does not foster learning and is not directly applicable within a portfolio of projects. Furthermore, the risk management process is not able to address all types of uncertainty. The project manager is a major factor in an effective management of uncertainty. When identifying and managing opportunity, having the ability to create a holistic view, to oversee both customer expectations, and to communicate project related information are important factors. Furthermore, the implementation also showed that it is actually possible, through the consistent use of a risk management process, to develop a cultural behavior within an organization that is much more preventive and proactive than before.

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This thesis analyzes the impact of risk and uncertainty on the Defense (DoD) Acquisitions System and the decision making process of modern Program Managers. A number of risks and uncertainties will be identified and a determination will be made if Service Oriented Architecture (SOA) and Open Architecture (OA) decreases or increases risks and uncertainty. In addition, it explores whether SOA and OA has achieved projected significant cost savings.

A large number of concepts related to carbon offset trading policy are currently being discussed such as baseline, leakage, permanence, monitoring, verification, enforcement, financial feasibility, and third party verification. Cutting across these concepts are a variety of risks and uncertainties. These risks play a major role in developing effective market designs that achieve aggregate emission caps while encouraging market participation and investment in carbon reduction activities. What are the risks associated with carbon offset policy and how do such risks affect incentives for investing in carbon offsets? A literature review of carbon trading risks is developed. Risks associated with carbon offsets policy can be classified into three major categories: institutional/policy, project level and measurement risks. Institutional/policy risks are related to uncertainties surrounding the future policy decisions and the institutional arrangements established to define baselines, stipulate monitoring/enforcement requirements, and define and estimate leakage. Baseline estimates are necessary to calculate the net carbon reduction of a program or project. Monitoring/enforcement risk is associated with the regulators' ability to detect whether the promised carbon sequestration activities are undertaken. Leakage occurs when carbon sequestration at one site encourages increase in carbon emissions on some other site. Project risk refers to non-performance of a carbon sequestration project in terms of not achieving the requisite target of carbon sequestration. Project risk includes physical risk and financial risk. Physical risks are associated with unexpected carbon emissions due to natural hazards or events such as fire, or hurricanes or changes in the rate of sequestration, which depend on weather and pests. Landowners will not participate in carbon sequestration programs if they expect to incur financial losses by participating. Measurement risk arises because it is difficult to measure actual rates of carbon sequestered due to spatial and temporal heterogeneity of carbon present in agricultural and forest production. Forests are a principal carbon "sink" for sequestering carbon from the atmosphere. The provision of trading emission rights under the Kyoto Protocol will provide forest landowners the opportunity to reap financial gains from sequestering carbon and trading rights to emit carbon in carbon permit markets. However, landowners may be liable for repaying all or some of the proceeds received for sequestering carbon if stored carbon is released during the contract period. Hurricane damage to forests may cause extensive mortality and subsequent emission of carbon dioxide from decomposing biomass. Such liabilities may reduce landowners' incentives to sequester carbon. This research evaluates incentives of an individual forest landowner for sequestering and trading carbon, given the risk of carbon loss from hurricanes. Results of our simulation model reveal that the effect of hurricane risk on landowners' behavior depends on the variability of returns from carbon and timber and the ability of landowners to mitigate risk by diversifying forest holdings across regions with different sequestration rates and different hurricane strike probabilities. Some risk mitigation strategy might be required to create the necessary incentives for landowner participation especially in hurricane prone regions. We evaluate incentives of forest landowners for sequestering and trading carbon, given the risk of carbon loss from hurricanes, and an opportunity to insure their losses. Results of simulation model reveal that the effect of hurricane risk depends on the variability of returns from carbon and timber and landowners' ability to mitigate risk by diversifying forest holdings across regions or transferring risk by purchasing insurance. Although, landowner can spread the risk of carbon loss by diversifying into different regions, insurance has a role to play over and above diversification by reducing landowners' risk (variance) from forestry investments for sequestration and timber purposes, even when timber losses are not insured.
Ph. D.