816 research outputs found

    Food security and Brexit: how the CAP began

    Get PDF
    In a new briefing paper, ‘Food, the UK and the EU: Brexit or Bremain?‘, Tim Lang and Victoria Schoen argue that post-Brexit the food world “will be characterised by volatility, disruption and uncertainty”, as the cost of imports will rise if sterling falls. They also discuss the urgent need for continued reform of the Common Agricultural Policy. In this extract from the paper, they explain how food and agriculture were central to the founding mission of the EU

    Serious Gaming for the Evaluation of Market Mechanisms

    Get PDF
    Design science consists of two major design processes: building and evaluation. A wellexecuted evaluation of design artifacts is crucial to their success. Traditional evaluation tools have certain weaknesses because design artifacts include “wicked” problems. Serious Gaming can help to overcome these problems. To this end an online based cloud resource managing game is developed which simulates the implementation of a market mechanism and represents a new design artifact. This mechanism is a heuristic solution consisting of dynamic pricing and a priority policy. The aim of this research is to show that Serious Gaming complements traditional evaluation tools and improves the evaluation of market mechanisms. Therefore, a general guideline for designing Serious Games for evaluation is developed and a classification of Serious Gaming is established. After having collected sufficient data, future work will be to analyze players’ behavior and finally evaluate the market mechanism

    Can training bar staff in responsible serving practices reduce alcohol-related harm?

    Get PDF
    A responsible service training programme aimed at reducing alcohol-related harm was implemented in a popular entertainment area over several months in 1992-93. Another popular entertainment area provided a control site. A number of evaluation measures were used: breath tests on 872 patrons from selected venues; drink driving data; risk assessments; the use of 'pseudo patrons'; and knowledge and attitude changes among trained bar staff (n = 88). Compared to control sites the intervention sites showed an immediate pre- to post-test reduction in patrons rated by researchers as extremely drunk and an eventual reduction from pre-test to follow-up in patrons with blood alcohol levels > = 0.08. There was also a small but significant increase in knowledge among bar staff. There was no significant reduction in patrons with blood alcohol levels > = 0.15 or in the number of drink driving offences from intervention sites during the study period. Pseudo drunk patrons were rarely refused service, identification was rarely checked and non-photographic identification was accepted on most occasions. The less than satisfactory outcome is attributed to poor implementation of the training and a lack of support among managers. The positive results from one venue, whose manager embraced the programme, served to highlight the importance of management support. It is suggested that mandatory training and routine enforcement of licensing laws are essential if the goals of responsible serving are to be met

    Solving the "many variables" problem in MICE with principal component regression

    Full text link
    Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the "many variables" problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy by means of a Monte Carlo simulation study and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures

    High-dimensional Imputation for the Social Sciences: a Comparison of State-of-the-art Methods

    Full text link
    Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidencevalid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results
    • 

    corecore