The efficiency of direct payments versus tax reductions under uncertainty

This paper analyzes the optimal behavior of farmers in the presence of direct payments and uncertainty. In an empirical analysis for Switzerland, it confirms previously obtained theoretical results and determines the magnitude of the theoretical predicted effects. The results show that direct payments increase agricultural production between 3.7% to 4.8%. Alternatively to direct payments, the production effect of tax reductions is evaluated in order to determine its magnitude. The empirical analysis corroborates the theoretical results of the literature and demonstrates that tax reductions are also distorting, but to a substantially lesser degree if losses are not offset. However, tax reductions, independently whether losses are offset or not, lead to higher government spending than pure direct payments

Reaction times as a measure of uncertainty. 'Tiempo de reacci??n como una medida de incertidumbre'

Resumen tomado de la publicaci??n

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Fault diagnosis by refining the parameter uncertainty space of nonlinear dynamic systems

This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented

Genotyping Klebsiella pneumoniae isolated from hepatic abscesses in three patients from Bogota, Colombia

Pyogenic liver abscess caused by Klebsiella pneumoniae represents an ever increasing entity which has mainly been described as occurring in Asia, even though, on a smaller scale, cases are being more frequently described from the USA and Europe, 13% overall mortality being reached worldwide. Affected patients are severely sick, suffering from fever, sweating, having increased acute phase reactants and risk factors such as Diabetes Mellitus, alcoholism and the inherent characteristics of the bacteria causing the disease. Objective: in this work we used a Multilocus Sequencing Typing (MLST), a nucleotide sequence-based method in order to characterize the genetic relationships among bacterial isolates. Materials and methods: the report is focused on three cases involving patients suffering from pyogenic liver abscess caused by Klebsiella pneumoniae in two hospitals in Bogota, Colombia, where phenotyping and hypermucoviscosity studies were carried out, as well as the genotyping of cultured Klebsiella isolates. Reults: it was found that the isolated microorganism in cases I and II corresponded to the same K. pneumoniae strain, having 100% sequence identity for the 5 genes being studied while the strain in Case III was genotypically different. Conclusion: it is important to carry out multidisciplinary studies allowing all pyogenic liver abscess cases reported in Colombia to be complied to ascertain the frequency of microorganisms causing this pathology in our country, as well as a genotyping study of different K. pneumoniae strains to compare them and confirm clonal and pathogenicity relationships through housekeeping gene analysis.

Piecemeal Oligopoly, Exchange Rate Uncertainty, and Trade Policy.

Este artículo analiza el efecto sistemático de la volatilidad de la tasa de cambio, cuando un gobierno local debe evaluar políticas comerciales estratégicas lineales y cuadráticas. Este ejercicio se realiza para modelos de mercado Cournot y Bertran. El modelo prueba que tanto el esquema lineal como el cuadrático tienen el mismo efecto sobre el bienestar social de los países, y que la volatilidad de la tasa de cambio domestica lleva a los gobiernos a reducir los subsidios a las exportaciones o bajan los impuestos a las exportaciones, de acuerdo a la variable estratégica elegida por las firmas. La tasa de cambio extranjera tiene diferentes efectos dependiendo de si las firmas producen bajos rendimientos a escalas constantes o decrecientes.

Does uncertainty cause inertia in decision making? An experimental study of the role of regret aversion and indecisiveness

Previous research has shown that often there is clear inertia in individual decision making---that is, a tendency for decision makers to choose a status quo option. I conduct a laboratory experiment to investigate two potential determinants of inertia in uncertain environments: (i) regret aversion and (ii) ambiguity-driven indecisiveness. I use a between-subjects design with varying conditions to identify the effects of these two mechanisms on choice behavior. In each condition, participants choose between two simple real gambles, one of which is the status quo option. I find that inertia is quite large and that both mechanisms are equally important.

Measuring the local economic impact of universities : an approach that considers uncertainty. 'La medición del impacto económico local de las universidades : un enfoque que considera la incertidumbre'.

Resumen tomado de la publicación

Enhancing emotion-based learning in decision-making under uncertainty

Resumen tomado de la publicaci??n

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.

An ensemble approach to uncertainty estimation for satellite-based rainfall estimates

The potential to narrow uncertainty in regional climate predictions

Faced by the realities of a changing climate, decision makers in a wide variety of organisations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organisations that fund climate research, is what is the potential for climate science to deliver improvements - especially reductions in uncertainty - in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty and scenario uncertainty. Using data from a suite of climate models we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare: a) the cost of various degrees of adaptation, given current levels of uncertainty; and b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

Uncertainty in the estimation of potential evapotranspiration under climate change.

21st century climate change is projected to result in an intensification of the global hydrological cycle, but there is substantial uncertainty in how this will impact freshwater availability. A relatively overlooked aspect of this uncertainty pertains to how different methods of estimating potential evapotranspiration (PET) respond to changing climate. Here we investigate the global response of six different PET methods to a 2 °C rise in global mean temperature. All methods suggest an increase in PET associated with a warming climate. However, differences in PET climate change signal of over 100% are found between methods. Analysis of a precipitation/PET aridity index and regional water surplus indicates that for certain regions and GCMs, choice of PET method can actually determine the direction of projections of future water resources. As such, method dependence of the PET climate change signal is an important source of uncertainty in projections of future freshwater availability.