Income Taxation and Growth in an OLG Economy: Does Aggregate Uncertainty Play any Role?

We analyze the effects of capital income taxation on long-run growth in a stochastic, two-period overlapping generations economy. Endogenous growth is driven by a positive externality of physical capital in the production sector that makes firms exhibit an aggregate technology in equilibrium. We distinguish between capital income and labor income, and between attitudes towards risk and intertemporal substitution of consumption. We show necessary and sufficient conditions such that i) increments in the capital income taxation lead to higher equilibrium growth rates, and ii) the effect of changes in the capital income tax rate on the equilibrium growth may be of opposite signs in stochastic and in deterministic economies. Such a sign reversal is shown to be more likely depending on i) how the intertemporal elasticity of substitution compares to one, and ii) the size of second- period labor supply. Numerical simulations show that for reasonable values of the intertemporal elasticity of substitution, a sign reversal shows up only for implausibly high values of the second- period’s labor supply. The conclusion is that deterministic OLG economies are a good approximation of the effect of taxes on the equilibrium growth rate as in Smith (1996).

Valuing Expansions of the Electricity Transmission Network under Uncertainty: The Binodal Case

Transmission investments are currently needed to meet an increasing electricity demand, to address security of supply concerns, and to reach carbon-emissions targets. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We propose a valuation model that accommodates both physical and economic uncertainties following the Real Options approach. It combines optimization techniques with Monte Carlo simulation. We illustrate the use of our model in a simplified, two-node grid and assess the decision whether to invest or not in a particular upgrade. The generation mix includes coal-and natural gas-fired stations that operate under carbon constraints. The underlying parameters are estimated from observed market data.

Uncertain Information Structures and Backward Induction

In everyday economic interactions, it is not clear whether sequential choices are visible or not to other participants: agents might be deluded about opponents'capacity to acquire,interpret or keep track of data, or might simply unexpectedly forget what they previously observed (but not chose). Following this idea, this paper drops the assumption that the information structure of extensive-form games is commonly known; that is, it introduces uncertainty into players' capacity to observe each others' past choices. Using this approach, our main result provides the following epistemic characterisation: if players (i) are rational,(ii) have strong belief in both opponents' rationality and opponents' capacity to observe others' choices, and (iii) have common belief in both opponents' future rationality and op-ponents' future capacity to observe others' choices, then the backward induction outcome obtains. Consequently, we do not require perfect information, and players observing each others' choices is often irrelevant from a strategic point of view. The analysis extends {from generic games with perfect information to games with not necessarily perfect information{the work by Battigalli and Siniscalchi (2002) and Perea (2014), who provide different sufficient epistemic conditions for the backward induction outcome.

Probabilistic robust control : theory and applications

In this work, the development of a probabilistic approach to robust control is motivated by structural control applications in civil engineering. Often in civil structural applications, a system's performance is specified in terms of its reliability. In addition, the model and input uncertainty for the system may be described most appropriately using probabilistic or "soft" bounds on the model and input sets. The probabilistic robust control methodology contrasts with existing H∞/μ robust control methodologies that do not use probability information for the model and input uncertainty sets, yielding only the guaranteed (i.e., "worst-case") system performance, and no information about the system's probable performance which would be of interest to civil engineers.

The design objective for the probabilistic robust controller is to maximize the reliability of the uncertain structure/controller system for a probabilistically-described uncertain excitation. The robust performance is computed for a set of possible models by weighting the conditional performance probability for a particular model by the probability of that model, then integrating over the set of possible models. This integration is accomplished efficiently using an asymptotic approximation. The probable performance can be optimized numerically over the class of allowable controllers to find the optimal controller. Also, if structural response data becomes available from a controlled structure, its probable performance can easily be updated using Bayes's Theorem to update the probability distribution over the set of possible models. An updated optimal controller can then be produced, if desired, by following the original procedure. Thus, the probabilistic framework integrates system identification and robust control in a natural manner.

The probabilistic robust control methodology is applied to two systems in this thesis. The first is a high-fidelity computer model of a benchmark structural control laboratory experiment. For this application, uncertainty in the input model only is considered. The probabilistic control design minimizes the failure probability of the benchmark system while remaining robust with respect to the input model uncertainty. The performance of an optimal low-order controller compares favorably with higher-order controllers for the same benchmark system which are based on other approaches. The second application is to the Caltech Flexible Structure, which is a light-weight aluminum truss structure actuated by three voice coil actuators. A controller is designed to minimize the failure probability for a nominal model of this system. Furthermore, the method for updating the model-based performance calculation given new response data from the system is illustrated.

Dynamic response of structures with uncertain parameters

This thesis presents a technique for obtaining the response of linear structural systems with parameter uncertainties subjected to either deterministic or random excitation. The parameter uncertainties are modeled as random variables or random fields, and are assumed to be time-independent. The new method is an extension of the deterministic finite element method to the space of random functions.

First, the general formulation of the method is developed, in the case where the excitation is deterministic in time. Next, the application of this formulation to systems satisfying the one-dimensional wave equation with uncertainty in their physical properties is described. A particular physical conceptualization of this equation is chosen for study, and some engineering applications are discussed in both an earthquake ground motion and a structural context.

Finally, the formulation of the new method is extended to include cases where the excitation is random in time. Application of this formulation to the random response of a primary-secondary system is described. It is found that parameter uncertainties can have a strong effect on the system response characteristics.

Optimal Investment in Energy Efficiency under Uncertainty

41 p.

Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications.

Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake.

To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.

Avaliações econômicas do uso da vacina contra o Papilomavírus Humano (HPV) em meninas adolescentes: uma revisão sistemática

O câncer de colo do útero persiste como um importante problema de saúde em todo o mundo, em particular nos países em desenvolvimento. Duas vacinas contra o papilomavirus humano (HPV) encontram-se atualmente disponíveis e aprovadas para uso em meninas adolescentes, antes do início da vida sexual: uma bivalente, contra os sorotipos 16 e 18 e outra quadrivalente, contra os sorotipos 6, 11, 16 e 18. Estes imunobiológicos têm por objetivo induzir uma imunidade contra o papilomavírus e, desta forma, atuar na prevenção primária do câncer do colo de útero. As avaliações econômicas podem ser um elemento que auxiliem nos processos de tomada de decisão sobre a incorporação da vacina em programas de imunização nacionais. Estas avaliações foram o objeto central deste trabalho, que teve como objetivo sintetizar as evidências procedentes de uma revisão sistemática da literatura de estudos de avaliação econômica da utilização da vacina contra o HPV em meninas adolescentes e pré-adolescentes. Foi realizada uma busca na literatura nas bases MEDLINE (via Pubmed), LILACS (via Bireme) e National Health Service Economic Evaluation Database (NHS EED) ate junho de 2010. Dois avaliadores, de forma independente, selecionaram estudos de avaliação econômica completa, que tivessem como foco a imunização para HPV em mulheres com as vacinas comercialmente disponíveis direcionada à população adolescente. Após a busca, 188 títulos foram identificados; destes, 39 estudos preencheram os critérios de elegibilidade e foram incluídos na revisão. Por tratar-se de uma revisão de avaliações econômicas, não foi realizada uma medida de síntese dos valores de relação incremental entre custos e efetividade. Os 39 artigos incluídos envolveram 51 avaliações econômicas em 26 países. Predominaram estudos de custo-utilidade (51%). Do ponto de vista da perspectiva da análise, predominou o dos sistemas de saúde (76,4%). A maioria dos trabalhos (94,9%) elegeu meninas, com idade entre 9 e 12 anos, como sua população alvo e desenvolveu simulações considerando imunidade para toda a vida (84,6%). Os modelos utilizados nos estudos foram do tipo Markov em 25 análises, de transmissão dinâmica em 11 e híbridos em 3. As análises de sensibilidade revelaram um conjunto de elementos de incerteza, uma parte significativa dos quais relacionados a aspectos vacinais: custos da vacina, duração da imunidade, necessidade de doses de reforço, eficácia vacinal e cobertura do programa. Estes elementos configuram uma área de especial atenção para futuros modelos que venham a ser desenvolvidos no Brasil para análises econômicas da vacinação contra o HPV.

Vibration and stress analysis in the presence of structural uncertainty

At medium to high frequencies the dynamic response of a built-up engineering system, such as an automobile, can be sensitive to small random manufacturing imperfections. Ideally the statistics of the system response in the presence of these uncertainties should be computed at the design stage, but in practice this is an extremely difficult task. In this paper a brief review of the methods available for the analysis of systems with uncertainty is presented, and attention is then focused on two particular "non- parametric" methods: statistical energy analysis (SEA), and the hybrid method. The main governing equations are presented, and a number of example applications are considered, ranging from academic benchmark studies to industrial design studies. © 2009 IOP Publishing Ltd.

Handling uncertainty in housing stock models

Housing stock models can be useful tools in helping to assess the environmental and socio-economic impacts of retrofits to residential buildings; however, existing housing stock models are not able to quantify the uncertainties that arise in the modelling process from various sources, thus limiting the role that they can play in helping decision makers. This paper examines the different sources of uncertainty involved in housing stock models and proposes a framework for handling these uncertainties. This framework involves integrating probabilistic sensitivity analysis with a Bayesian calibration process in order to quantify uncertain parameters more accurately. The proposed framework is tested on a case study building, and suggestions are made on how to expand the framework for retrofit analysis at an urban-scale. © 2011 Elsevier Ltd.

Modelling the evolution of uncertainty levels during design

Design work involves uncertainty that arises from, and influences, the progressive development of solutions. This paper analyses the influences of evolving uncertainty levels on the design process. We focus on uncertainties associated with choosing the values of design parameters, and do not consider in detail the issues that arise when parameters must first be identified. Aspects of uncertainty and its evolution are discussed, and a new task-based model is introduced to describe process behaviour in terms of changing uncertainty levels. The model is applied to study two process configuration problems based on aircraft wing design: one using an analytical solution and one using Monte-Carlo simulation. The applications show that modelling uncertainty levels during design can help assess management policies, such as how many concepts should be considered during design and to what level of accuracy. © 2011 Springer-Verlag.

Strength mobilization in clays and silts

A large database of 115 triaxial, direct simple shear, and cyclic tests on 19 clays and silts is presented and analysed to develop an empirical framework for the prediction of the mobilization of the undrained shear strength, cu, of natural clays tested from an initially isotropic state of stress. The strain at half the peak undrained strength (γM=2) is used to normalize the shear strain data between mobilized strengths of 0.2cu and 0.8cu. A power law with an exponent of 0.6 is found to describe all the normalized data within a strain factor of 1.75 when a representative sample provides a value for γM=2. Multi-linear regression analysis shows that γM=2 is a function of cu, plasticity index Ip, and initial mean effective stress p′0. Of the 97 stress-strain curves for which cu, Ip, and p′0 were available, the observed values of γM=2 fell within a factor of three of the regression; this additional uncertainty should be acknowledged if a designer wished to limit immediate foundation settlements on the basis of an undrained strength profile and the plasticity index of the clay. The influence of stress history is also discussed. The application of these stress-strain relations to serviceability design calculations is portrayed through a worked example. The implications for geotechnical decision-making and codes of practice are considered.

Modulation of pain ratings by expectation and uncertainty: Behavioral characteristics and anticipatory neural correlates.

Expectations about the magnitude of impending pain exert a substantial effect on subsequent perception. However, the neural mechanisms that underlie the predictive processes that modulate pain are poorly understood. In a combined behavioral and high-density electrophysiological study we measured anticipatory neural responses to heat stimuli to determine how predictions of pain intensity, and certainty about those predictions, modulate brain activity and subjective pain ratings. Prior to receiving randomized laser heat stimuli at different intensities (low, medium or high) subjects (n=15) viewed cues that either accurately informed them of forthcoming intensity (certain expectation) or not (uncertain expectation). Pain ratings were biased towards prior expectations of either high or low intensity. Anticipatory neural responses increased with expectations of painful vs. non-painful heat intensity, suggesting the presence of neural responses that represent predicted heat stimulus intensity. These anticipatory responses also correlated with the amplitude of the Laser-Evoked Potential (LEP) response to painful stimuli when the intensity was predictable. Source analysis (LORETA) revealed that uncertainty about expected heat intensity involves an anticipatory cortical network commonly associated with attention (left dorsolateral prefrontal, posterior cingulate and bilateral inferior parietal cortices). Relative certainty, however, involves cortical areas previously associated with semantic and prospective memory (left inferior frontal and inferior temporal cortex, and right anterior prefrontal cortex). This suggests that biasing of pain reports and LEPs by expectation involves temporally precise activity in specific cortical networks.

Coupling model uncertainty for coupled rainfall/runoff and surface water quality models in river problems

Coupled hydrology and water quality models are an important tool today, used in the understanding and management of surface water and watershed areas. Such problems are generally subject to substantial uncertainty in parameters, process understanding, and data. Component models, drawing on different data, concepts, and structures, are affected differently by each of these uncertain elements. This paper proposes a framework wherein the response of component models to their respective uncertain elements can be quantified and assessed, using a hydrological model and water quality model as two exemplars. The resulting assessments can be used to identify model coupling strategies that permit more appropriate use and calibration of individual models, and a better overall coupled model response. One key finding was that an approximate balance of water quality and hydrological model responses can be obtained using both the QUAL2E and Mike11 water quality models. The balance point, however, does not support a particularly narrow surface response (or stringent calibration criteria) with respect to the water quality calibration data, at least in the case examined here. Additionally, it is clear from the results presented that the structural source of uncertainty is at least as significant as parameter-based uncertainties in areal models. © 2012 John Wiley & Sons, Ltd.

Efficient parametric uncertainty analysis within the hybrid Finite Element/Statistical Energy Analysis method

This paper is concerned with the development of efficient algorithms for propagating parametric uncertainty within the context of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) approach to the analysis of complex vibro-acoustic systems. This approach models the system as a combination of SEA subsystems and FE components; it is assumed that the FE components have fully deterministic properties, while the SEA subsystems have a high degree of randomness. The method has been recently generalised by allowing the FE components to possess parametric uncertainty, leading to two ensembles of uncertainty: a non-parametric one (SEA subsystems) and a parametric one (FE components). The SEA subsystems ensemble is dealt with analytically, while the effect of the additional FE components ensemble can be dealt with by Monte Carlo Simulations. However, this approach can be computationally intensive when applied to complex engineering systems having many uncertain parameters. Two different strategies are proposed: (i) the combination of the hybrid FE/SEA method with the First Order Reliability Method which allows the probability of the non-parametric ensemble average of a response variable exceeding a barrier to be calculated and (ii) the combination of the hybrid FE/SEA method with Laplace's method which allows the evaluation of the probability of a response variable exceeding a limit value. The proposed approaches are illustrated using two built-up plate systems with uncertain properties and the results are validated against direct integration, Monte Carlo simulations of the FE and of the hybrid FE/SEA models. © 2013 Elsevier Ltd.