Suchergebnisse

The report discusses the role of impact assessment and evaluation of governmental decisions. The main objective of report is to develop a statistical model for optimisation of the set of policy instruments. The Draft of Rural Development Plan is used as example in order to develop and test the statistical model for optimising the allocation and use of funds. The statistical model is developed like as a small software version for policy‐makers to evaluate critical decisions. The statistical decision making theory is used in seeking solutions to optimise the choice of governmental decisions. The functions of losses or gains are defined on the basis of statistical data in two levels ‐ macroeconomic level and microeconomic level. In the statistical model the principles of the subjective probability are also applied in the case of unreliable and unavailable information. First Published Online: 14 Oct 2010

The report discusses the role of impact assessment and evaluation of governmental decisions. The main objective of report is to develop a statistical model for optimisation of the set of policy instruments. The Draft of Rural Development Plan is used as example in order to develop and test the statistical model for optimising the allocation and use of funds. The statistical model is developed like as a small software version for policy‐makers to evaluate critical decisions. The statistical decision making theory is used in seeking solutions to optimise the choice of governmental decisions. The functions of losses or gains are defined on the basis of statistical data in two levels ‐ macroeconomic level and microeconomic level. In the statistical model the principles of the subjective probability are also applied in the case of unreliable and unavailable information. First Published Online: 14 Oct 2010

Part 7: First Mining Humanistic Data Workshop (MHDW 2012) ; International audience ; Web 2.0 has facilitated interactive information sharing on the WWW, allowing users the opportunity to articulate their opinions on different topics. In this framework, certain practices implement information monitoring systems so as digests, reports on keywords and thematic queries regarding opinions on government decisions to be created. Analysis of rubrics associations, primary semantic and statistical interpretation of the texts is usually carried out. It is, on the other hand, rather difficult to get punctual predicts and estimate sufficiently forum users' opinion strength. In this work we present a methodology which automatically mines and estimates the strength of users' opinions on text forums regarding government decisions. According to our methodology, quantitative features are automatically mined from forum posts and then passed to a Support Vector Machine based classifier where the users' opinion strength is estimated. The proposed methodology has been validated in real data and initial experimental results are presented.

REBRAIN GREECE (RBG), a multidisciplinary and inter-ministerial working group, formulated in April 2019, is perhaps the first Greek governmental collective intelligence ecosystem, reaching out to efficiently handle issues of utmost importance, such as brain drain and digital transformation of the existing human resources to the new digital working society. As an ecosystem, it consists of several, thematic, inter-connected working groups, composed of different ministries' teams of field experts, dealing with concrete but inter-correlated issues, interoperating in a constant way. RBG constitutes a three pillar- interoperability -collective intelligence- ecosystem, encompassed in its functional architecture constant and open source, interoperability synergies between governmental agencies, different field's experts and ministerial executives, between machine learning schemes and data bases and integrated information systems info exchange modules. It uses the data analytics power of the Labour Market Diagnosis Mechanism (LMDM) and already produced data driven policy proposals. Big data applications of LMDM are interpreting and visualising different types of big data bases through cross-checking raw data and thus formulating approaches towards public policies. Through visualization softwares used for interpreting the data into policy perspectives and proposals, that policy makers examine and elaborate on the most suitable choices and activities for challenge-responsive market digital policies.

AbstractFor decades, scholars have discussed how to build greater citizen trust in government. I hypothesize that to increase trust in government, we should consider whether decisions made in bureaucrat–citizen encounters (e.g. applications for welfare benefits) are favorable to citizens. Building on insights from social psychology, I argue that in cases where citizens are presented with unfavorable decisions (e.g. rejection of applications), public employees can mitigate the negative impact on trust in government by appearing warm and friendly in the decision-making process. The argument is tested in a large-scale randomized survey experiment on a representative sample of Danish citizens, where I manipulate decision favorability and warmth. The findings reveal that outcome favorability and warmth strongly influence citizens' trust in government.

Policymakers during 1COVID-19 operate in uncharted 2territory and must make tough decisions. Operational Research - the ubiquitous 'science of better' - plays a vital role in supporting this decision-making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID-19 growth rates with statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown). Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.