Uncertainty, electoral Incentives and political myopia

In this paper, we study the determinants of political myopia in a rational model of electoral accountability where the key elements are informational frictions and uncertainty. We build a framework where political ability is ex-ante unknown and policy choices are not perfectly observable. On the one hand, elections improve accountability and allow to keep well-performing incumbents. On the other, politicians invest too little in costly policies with future returns in an attempt to signal high ability and increase their reelection probability. Contrary to the conventional wisdom, uncertainty reduces political myopia and may, under some conditions, increase social welfare. We use the model to study how political rewards can be set so as to maximise social welfare and the desirability of imposing a one-term limit to governments. The predictions of our theory are consistent with a number of stylised facts and with a new empirical observation documented in this paper: aggregate uncertainty, measured by economic volatility, is associated to better ...scal discipline in a panel of 20 OECD countries.

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

Quality uncertainty and allocation of decision rights in the European protected designation of origin

The paper considers some issue in the governance of the European Protected Designation of Origin (PDO). The PDO systems are the outcomes of both farmers and consumers expectations and connect the valorisation of the agricultural and rural resources of given territories to the quality of typical products. A critical point in the governance of the PDO systems is represented by the connection between the quality strategies and the uncertainty. The paper argues that the PDO systems can be thought of as strictly coordinated subsystems in which the ex post governance play a critical role in coping with quality uncertainty. The study suggests that the society's inducements given raise to complex organizational systems in which the allocation of decision rights to PDO collective organizations play a major role. The empirical analysis is carried out by examining ten Italian PDO systems in order to identify the decision rights allocated.

Entrepreneurial decision-making using the knightian uncertainty approach

The article discusses the behavioral aspects that affect the entrepreneurs' decision making under the Knightian uncertainty approach. Since the profit arising from entrepreneurial activity represents the reward of an immeasurable and subjective risk, it has been hypothesized that innovative entrepreneurs have excessive optimism and confidence, which leads them to invest in high-risk activities. A behavioral model of decision making under uncertainty is used to test the hypothesis of overconfidence. This model is based on Bayesian inference, which allows us to model the assumption that these entrepreneurs are overconfident. We conclude that, under the hypothesis of overconfidence, these entrepreneurs decide to invest, despite the fact that the expected utility model indicates the contrary. This theoretical finding could explain why there are a large number of business failures in the first years of activity.

A Tool for optimum design of Waste Water Treatment Plants under uncertainty

L’objectiu principal és presentar un nou prototipus d’eina per al disseny de les plantes de tractament d’aigües residuals utilitzant models mecànics dinàmics quantificant la incertesa

Traiter l'hypertension artérielle: l'incertitude des définitions et des cibles [Managing hypertension: the uncertainty of definitions and targets].

In clinical practice, physicians are confronted with a multitude of definitions and treatment goals for arterial hypertension, depending of the diagnostic method used (e.g. office, home and ambulatory blood pressure measurement) and the underlying disease. The historical background and evidence of these different blood pressure thresholds are discussed in this article, as well as some recent treatment guidelines. Besides, the debate of the "J curve", namely the possible risks associated with an excessive blood pressure reduction, is discussed.

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

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

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Stochastic dominance and absolute risk aversion

In this paper we proose the infimum of the Arrow-Pratt index of absoluterisk aversion as a measure of global risk aversion of a utility function.We then show that, for any given arbitrary pair of distributions, thereexists a threshold level of global risk aversion such that all increasingconcave utility functions with at least as much global risk aversion wouldrank the two distributions in the same way. Furthermore, this thresholdlevel is sharp in the sense that, for any lower level of global riskaversion, we can find two utility functions in this class yielding oppositepreference relations for the two distributions.

The effects of employment uncertainty and wealth shocks on the labor supply and claiming behavior of older American workers

Unemployment rates in developed countries have recently reached levels not seenin a generation, and workers of all ages are facing increasing probabilities of losingtheir jobs and considerable losses in accumulated assets. These events likely increasethe reliance that most older workers will have on public social insurance programs,exactly at a time that public finances are suffering from a large drop in contributions.Our paper explicitly accounts for employment uncertainty and unexpectedwealth shocks, something that has been relatively overlooked in the literature, butthat has grown in importance in recent years. Using administrative and householdlevel data we empirically characterize a life-cycle model of retirement and claimingdecisions in terms of the employment, wage, health, and mortality uncertainty facedby individuals. Our benchmark model explains with great accuracy the strikinglyhigh proportion of individuals who claim benefits exactly at the Early RetirementAge, while still explaining the increased claiming hazard at the Normal RetirementAge. We also discuss some policy experiments and their interplay with employmentuncertainty. Additionally, we analyze the effects of negative wealth shocks on thelabor supply and claiming decisions of older Americans. Our results can explainwhy early claiming has remained very high in the last years even as the early retirementpenalties have increased substantially compared with previous periods, andwhy labor force participation has remained quite high for older workers even in themidst of the worse employment crisis in decades.