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Thèses sur le sujet "Risk and uncertainty theory"

1

Martinez-Correa, Jimmy. "Decisions under Risk, Uncertainty and Ambiguity: Theory and Experiments." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/rmi_diss/29.

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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.
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Walker, Kenneth C. "Rhetorics of Uncertainty: Networked Deliberations in Climate Risk." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556604.

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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.
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PANK, ROULUND Rasmus. "Essays in empirical economics." Doctoral thesis, European University Institute, 2019. http://hdl.handle.net/1814/62944.

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Defence date: 20 May 2019<br>Examining Board: Prof. Jerome Adda (Supervisor); Prof. Piero Gottardi,University of Essex; Prof. Rosemarie Nagel, Universitat Pompeu Fabra; Prof. Glenn W. Harrison, Georgia State University<br>This first chapter is co-authored with Nicolás Aragón and examines how participant and market confidence affect the outcomes in an experimental asset market where the fundamental value is known by all participants. Such a market should, in theory, clear at the expected value in each period. However, the literature has shown that bubbles often occur in these markets. We measure the confidence of each participant by asking them to forecast the one-period-ahead price as a discrete probability mass distribution. We find that confidence not only affects price-formation in markets, but is important in explaining the dynamics of bubbles. Moreover, as traders’ confidence grows, they become increasingly more optimistic, thus increasing the likelihood of price bubbles. The second chapter also deals with expectations and uncertainty, but from a different angle. It asks how increased uncertainty affects economic demand in a particular sector, using a discrete-choice demand framework. To investigate this issue I examine empirically to what extent varying uncertainty affects the consumer demand for flight traffic using us micro demand data. I find that the elasticity of uncertainty on demand is economically and statistically significant. The third chapter presents a more practical side to the issue examined in the first chapter. It describes how to elicit participants’ expectations in an economic experiment. The methodology is based on Harrison et al. (2017). The tool makes it easier for participants in economic experiments to forecast the movements of a key variable as discrete values using a discrete probability mass distribution that can be “drawn” on a virtual canvas using the mouse. The module I wrote is general enough that it can be included in other economic experiments.<br>1. Certainty and Decision-Making in Experimental Asset Markets 1.1. Literature Review 1.2. Hypotheses 1.3. Experimental Design 1.3.1. The asset market 1.3.2. Eliciting traders’ beliefs 1.3.3. Risk, Ambiguity and Hedging 1.4. Overview of experimental data 1.4.1. Summary of the trade data 1.4.2. Expectation data 1.5. Results 1.5.1. Predictions and forecast 1.5.2. Convergence of expectations 1.5.3. Market volatility and initial expectations 1.5.4. Explanatory power of certainty on price formation 1.6. Conclusion 2. The impact of macroeconomic uncertainty on demand: 2.1. Introduction 2.2. Literature review 2.3. A model of demand for flights 2.3.1. Demand 2.3.2. Firms 2.4. Data 2.4.1. The characteristics of the products 2.4.2. Market and macroeconomic characteristics 2.4.3. Instruments 2.4.4. Product shares 2.5. Results 2.6. Conclusion 3. forecast.js: a module for measuring expectation in economic experiments 3.1. Background 3.1.1. Elicitating Expectations in Experimental Finance 3.1.2. Eliciting a Distribution of Beliefs: Theoretical Considerations 3.2. Using the forecast.js module 3.2.1. Calibration 3.2.2. Accessing the forecast data 3.3. The generated data 3.3.1. Example of individual expectations 3.3.2. Timing Considerations 3.3.3. Prediction precision over time 3.4. Conclusion Bibliography A. Appendix to Chapter 1 A.1. Further robustness checks A.1.1. Additional graph for Hypothesis 2 A.1.2. Increased agreement with the Bhattacharyya coefficient A.1.3. Additional robustness checks for Hypothesis 3 A.2. Instructions for experiment A.2.1. General Instructions A.2.2. How to use the computerized market A.3. Questionnaire A.3.1. Before Session A.3.2. After Session B. Appendix to Chapter 3 99 B.1. Robustness check of precision B.2. Using forecast.js in a standalone HTML page B.3. Using forecast.js with oTree B.3.1. Setting up models.py B.3.2. The pages.py file B.3.3. Display forecast modules on the pages
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Li, Kehan. "Stress, uncertainty and multimodality of risk measures." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E068.

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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<br>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
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Raykov, Radoslav S. "Essays in Applied Microeconomic Theory." Thesis, Boston College, 2012. http://hdl.handle.net/2345/bc-ir:104087.

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Thesis advisor: Utku Unver<br>This dissertation consists of three essays in microeconomic theory: two focusing on insurance theory and one on matching theory. The first chapter is concerned with catastrophe insurance. Motivated by the aftermath of hurricane Katrina, it studies a strategic model of catastrophe insurance in which consumers know that they may not get reimbursed if too many other people file claims at the same time. The model predicts that the demand for catastrophe insurance can ``bend backwards'' to zero, resulting in multiple equilibria and especially in market failure, which is always an equilibrium. This shows that a catastrophe market can fail entirely due to demand-driven reasons, a result new to the literature. The model suggests that pricing is key for the credibility of catastrophe insurers: instead of increasing demand, price cuts may backfire and instead cause a ``race to the bottom.'' However, small amounts of extra liquidity can restore the system to stable equilibrium, highlighting the importance of a functioning reinsurance market for large risks. These results remain robust both for expected utility consumer preferences and for expected utility's most popular alternative, rank-dependent expected utility. The second chapter develops a model of quality differentiation in insurance markets, focusing on two of their specific features: the fact that costs are uncertain, and the fact that firms are averse to risk. Cornerstone models of price competition predict that firms specialize in products of different quality (differentiate their products) as a way of softening price competition. However, real-world insurance markets feature very little differentiation. This chapter offers an explanation to this phenomenon by showing that cost uncertainty fundamentally alters the nature of price competition among risk-averse firms by creating a drive against differentiation. This force becomes particularly pronounced when consumers are picky about quality, and is capable of reversing standard results, leading to minimum differentiation instead. The chapter concludes with a study of how the costs of quality affect differentiation by considering two benchmark cases: when quality is costless and when quality costs are convex (quadratic). The third chapter focuses on the theory of two-sided matching. Its main topic are inefficiencies that arise when agent preferences permit indifferences. It is well-known that two-sided matching under weak preferences can result in matchings that are stable, but not Pareto efficient, which creates bad incentives for inefficiently matched agents to stay together. In this chapter I show that in one-to-one matching with weak preferences, the fraction of inefficiently matched agents decreases with market size if agents are sufficiently diverse; in particular, the proportion of agents who can Pareto improve in a randomly chosen stable matching approaches zero when the number of agents goes to infinity. This result shows that the relative degree of the inefficiency vanishes in sufficiently large markets, but this does not provide a "cure-all'' solution in absolute terms, because inefficient individuals remain even when their fraction is vanishing. Agent diversity is represented by the diversity of each person's preferences, which are assumed randomly drawn, i.i.d. from the set of all possible weak preferences. To demonstrate its main result, the chapter relies on the combinatorial properties of random weak preferences<br>Thesis (PhD) — Boston College, 2012<br>Submitted to: Boston College. Graduate School of Arts and Sciences<br>Discipline: Economics
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Kentel, Elçin. "Uncertainty Modeling Health Risk Assessment and Groundwater Resources Management." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11584.

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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.
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Zargar, Yaghoobi Amin H. "Handling uncertainty in hydrologic analysis and drought risk assessment using Dempster-Shafer theory." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43814.

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The aim of this thesis is to enhance some of the hydrologic analyses involved in drought risk assessment (DRA) to uncertainty-driven analyses therefore improving the accuracy and informativeness of DRA. In DRA, risk, or the expected loss from drought hazard is estimated by integrating the magnitude of hazard (i.e., drought severity) with vulnerability (i.e., susceptibility to losses from drought). Most hydrologic analyses including DRA are traditionally performed in a deterministic setting, ignoring data quality and uncertainty issues. Uncertainty can affect the accuracy of modeling results and undermine subsequent decision making. In order to handle uncertainty in DRA, this thesis uses the Dempster-Shafer theory (DST) which provides a unified platform for modeling and propagating uncertainty in the forms of variability, conflict and incompleteness. First, DST is used to model and propagate uncertainty arisen from a high degree of conflict between two datasets of a drought hazard indicator, the snow water equivalent. Four DST combination rules are used for conflict-resolution and results unanimously indicate a high possibility of drought. Second, the Standardized Precipitation Index (SPI) is used as a generic measure of hazard and is linked directly with wildfire risk in current and future climate scenarios. Using DST, modifications are introduced into SPI, enabling the integration of uncertainty analysis with SPI processes. The resulting enhanced SPI can model the effects of long-term shifts in climate normals on drought hazard while simultaneously evaluating the significance of these shifts within the range of surrounding uncertainty. Later, vulnerability to wildfire is simulated using enhanced SPI and two additional variables: evaporation and firefighting capacity. The estimated risk indicates that forests in Okanagan Basin are vulnerable to wildfires during periods of 2040-2069 and 2070-2099 unless the firefighting capacity is enhanced with a presumed rate. Through the successful implementation of DST into DRA processes, this research demonstrates the capability of DST in improving hydrologic analyses and enhancing informativeness in the water resources arena in general.
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Niculescu, Mihai. "Towards a Unified Treatment of Risk and Uncertainty in Choice Research." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1249493228.

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Zhao, Mingjun. "Essays on model uncertainty in macroeconomics." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1153244452.

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Garcia, Thomas. "A behavioral approach of decision making under risk and uncertainty." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132313/1/Thomas%20Jean-Christophe%20Lucien_Garcia_Thesis.pdf.

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This thesis investigates how individuals make decisions under risk and uncertainty. It is composed of four essays that theoretically and experimentally investigate decision-making. First, I study situations where individuals must decide whether an event has occurred using uncertain evidence. I highlight that individuals tend to maximize accuracy instead of maximizing expected payoffs. I find that it is partially due to the existence of a value of being right and a recency bias. Second, I study how ambiguity on the costs or the benefits of a donation affects donation behavior. I show that individuals use ambiguity strategically as an excuse to behave less generously without feeling guilty. Finally, I study the external validity of risk preference measures based on a representative panel of the Dutch population. I find that risk-preference measures are related to behavior in experimental risk tasks, however they are not related to risk-taking in the field.
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