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

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

Constraint satisfaction techniques under uncertain conditions for fault diagnosis in nonlinear dynamic systems

The speed of fault isolation is crucial for the design and reconfiguration of fault tolerant control (FTC). In this paper the fault isolation problem is stated as a constraint satisfaction problem (CSP) and solved using constraint propagation techniques. The proposed method is based on constraint satisfaction techniques and uncertainty space refining of interval parameters. In comparison with other approaches based on adaptive observers, the major advantage of the presented method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements and model errors and without the monotonicity assumption. In order to illustrate the proposed approach, a case study of a nonlinear dynamic system is presented

Gender differences in premorbid, entry, treatment, and outcome characteristics in a treated epidemiological sample of 661 patients with first episode psychosis.

OBJECTIVES: Gender differences in psychotic disorder have been observed in terms of illness onset and course; however, past research has been limited by inconsistencies between studies and the lack of epidemiological representative of samples assessed. Thus, the aim of this study was to elucidate gender differences in a treated epidemiological sample of patients with first episode psychosis (FEP). METHODS: A medical file audit was used to collect data on premorbid, entry, treatment and 18-month outcome characteristics of 661 FEP consecutive patients treated at the Early Psychosis Prevention and Intervention Centre (EPPIC), Melbourne, Australia. RESULTS: Prior to onset of psychosis, females were more likely to have a history of suicide attempts (p=.011) and depression (p=.001). At service entry, females were more likely to have depressive symptoms (p=.007). Conversely, males had marked substance use problems that were evident prior to admission (p<.001) and persisted through treatment (p<.001). At service entry, males also experienced more severe psychopathology (p<.001) and lower levels of functioning (GAF, p=.008; unemployment/not studying p=.004; living with family, p=.003). Treatment non-compliance (p<.001) and frequent hospitalisations (p=.047) were also common for males with FEP. At service discharge males had significantly lower levels of functioning (GAF, p=.008; unemployment/not studying p=.040; living with family, p=.001) compared to females with FEP. CONCLUSIONS: Gender differences are evident in illness course of patients with FEP, particularly with respect to past history of psychopathology and functioning at presentation and at service discharge. Strategies to deal with these gender differences need to be considered in early intervention programs.

Doing Intersectionality: Repertoires of Feminist Practices in France and Canada

Intersectionality has been adopted as the preferred term to refer to and to analyze multiple axes of oppression in feminist theory. However, less research examines if this term, and the political analyses it carries, has been adopted by women's rights organizations in various contexts and to what effect. Drawing on interviews with activists working in a variety of women's rights organizations in France and Canada, I show that intersectionality is only one of the repertoires that a women's rights organization might use to analyze the social experience and the political interests of women situated at the intersection of several axes of domination. I propose a typology of four repertoires that activists use to reflect on intersectionality and inclusiveness. Drawing on a quantitative and qualitative analysis of the interview data, I show that hegemonic repertoires about racial or religious identity in one national context shape the way activists and organizations understand intersectionality and its challenges. The identity of organizations, as well as their main function (advocacy or providing service), also shape their understanding of intersectional issues.

Student modeling based on fuzzy inference mechanisms

The paper presents a competence-based instructional design system and a way to provide a personalization of navigation in the course content. The navigation aid tool builds on the competence graph and the student model, which includes the elements of uncertainty in the assessment of students. An individualized navigation graph is constructed for each student, suggesting the competences the student is more prepared to study. We use fuzzy set theory for dealing with uncertainty. The marks of the assessment tests are transformed into linguistic terms and used for assigning values to linguistic variables. For each competence, the level of difficulty and the level of knowing its prerequisites are calculated based on the assessment marks. Using these linguistic variables and approximate reasoning (fuzzy IF-THEN rules), a crisp category is assigned to each competence regarding its level of recommendation.

An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Aim  Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location  World-wide.Methods  Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results  Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions  By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

Survival and Sensemaking: Organizational Resilience in a Wall Street Trading Room After 9/11

How do organizations cope with extreme uncertainty? The existing literature is divided on this issue: some argue that organizations deal best with uncertainty in the environment by reproducing it in the organization, whereas others contend that the orga nization should be protected from the environment. In this paper we study the case of a Wall Street investment bank that lost its entire office and trading technology in the terrorist attack of September 11 th. The traders survived, but were forced to relocate to a makeshift trading room in New Jersey. During the six months the traders spent outside New York City, they had to deal with fears and insecurities inside the company as well as outside it: anxiety about additional attacks, questions of professional identity, doubts about the future of the firm, and ambiguities about the future re-location of the trading room. The firm overcame these uncertainties by protecting the traders' identities and their ability to engage in sensemaking. The organization held together through a leadership style that managed ambiguities and created the conditions for new solutions to emerge.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

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.

Construction and validation of nursing diagnoses for individuals with diabetes in specialized care

OBJECTIVE To construct statements of nursing diagnoses related to nursing practice for individuals with diabetes in Specialized Care, on the basis of the Database of Nursing Practice Terms related to diabetes, in the International Classification for Nursing Practice (ICNP®) and in the Theory of Basic Human Needs and to validate them with specialist nurses in the area. METHOD Methodological research, structured into sequential stages of construction, cross-mapping, validation and categorization of nursing diagnoses. RESULTS A list was indicated of 115 statements of diagnostic, including positive, negative and improvement statements; 59 nursing diagnoses present in and 56 nursing diagnoses absent from the ICNP® Version 2011. 66 diagnoses with CVI ≥ 0.50 were validated, being categorized on the basis of human needs. CONCLUSION It was observed that the use of the ICNP® 2011 favored the specifications of the concepts of professional practice in care with individuals with diabetes.

Testing Spatial Autocorrelation in Weighted Networks : the Modes Permutation Test

In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.

A dynamic choice model of hybrid behavior in the attribute-space

This paper presents a dynamic choice model in the attributespace considering rational consumers that discount the future. In lightof the evidence of several state-dependence patterns, the model isfurther extended by considering a utility function that allows for thedifferent types of behavior described in the literature: pure inertia,pure variety seeking and hybrid. The model presents a stationaryconsumption pattern that can be inertial, where the consumer only buysone product, or a variety-seeking one, where the consumer buys severalproducts simultane-ously. Under the inverted-U marginal utilityassumption, the consumer behaves inertial among the existing brands forseveral periods, and eventually, once the stationary levels areapproached, the consumer turns to a variety-seeking behavior. An empiricalanalysis is run using a scanner database for fabric softener andsignificant evidence of hybrid behavior for most attributes is found,which supports the functional form considered in the theory.

Testing the descriptive performance of the rank-dependent utility in the domain of health profiles

Expected utility theory (EUT) has been challenged as a descriptive theoryin many contexts. The medical decision analysis context is not an exception.Several researchers have suggested that rank dependent utility theory (RDUT)may accurately describe how people evaluate alternative medical treatments.Recent research in this domain has addressed a relevant feature of RDU models-probability weighting-but to date no direct test of this theoryhas been made. This paper provides a test of the main axiomatic differencebetween EUT and RDUT when health profiles are used as outcomes of riskytreatments. Overall, EU best described the data. However, evidence on theediting and cancellation operation hypothesized in Prospect Theory andCumulative Prospect Theory was apparent in our study. we found that RDUoutperformed EU in the presentation of the risky treatment pairs in whichthe common outcome was not obvious. The influence of framing effects onthe performance of RDU and their importance as a topic for future researchis discussed.