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A game theoretical study of generalised trust and reciprocation in Poland: II. A description of the study group

100%
MARKOWSKA-PRZYBYŁA U., RAMSEY D.
Operations Research and Decisions
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2015
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vol. 25
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issue 2
51-73
EN
The first article describing this project presented the three games that the participants played: the Ultimatum Game, the Trust Game and the Public Goods Game. This article describes the study group on the basis of a questionnaire regarding where they study and come from, their social contacts, interest in current issues, views on inequality and outlook on life. A description of the migratory decisions of students is given. In particular, two exploratory methods are used to investigate the data’s structure: Bayesian networks and principal component analysis. Bayesian networks are used to illustrate the associations between categorical variables. Principal component analysis is designed to describe latent variables which reflect the associations between numerical variables. We present the results of this analysis and discuss the advantages and disadvantages of these two methods.
2
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Decisions involving databases, fuzzy databases and codatabases

75%
SCHUMANN A., WOLEŃSKI J.
Operations Research and Decisions
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2015
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vol. 25
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issue 3
59-72
EN
The authors consider the following three ways of decision making: (i) decisions involving databases by means of standard tools of sequential logic and universal algebra; (ii) decisions involving fuzzy databases by means of fuzzy logic; (iii) decision involving continuously growing databases (codatabases) using the tools of Bayesian networks.
3
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The impact of regional diversification in the size of the general government sector on the economies of EU countries

51%
Mroczek T., Skica T., Rodzinka J.
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
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2019
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issue vol. 63, nr 2
65-80
EN
The main objective of this article is proving how the regional diversification in the size of general government sector influences the economies of EU countries. To achieve this, presenting both the size of the general government sector and of the economy, using variables which enable comparison in time and space, is essential. Bearing this in mind, the general government sector has been depicted by nine variables and the economy has been described by thirteen explanatory variables. The complexity of the phenomenon imposes the implementation of an unconventional approach in this field of exploration. Our approach is based on Intelligent Data Analysis (IDA) - a methodology that includes a set of techniques that can be applied for extracting useful knowledge from large amounts of data. In order to indicate the impact of regional diversification in the size of the general government sector on the EU countries’ economies, probabilistic techniques were applied – Bayesian Networks. Analysis made in the study showed that the largest impact of the GGS size on the economy was identified in Portugal and Slovakia. The results of the studies show that the most "responsive" to the size of the GGS variable describing the economy was gross domestic product per inhabitant. The research proved that the economies of some countries showed similarities in the effect of the size of the general government sector on the parameters of the economy. We have identified five groups of such countries.
PL
Głównym celem artykułu jest ukazanie wpływu regionalnego zróżnicowania rozmiaru sektora finansów publicznych na gospodarki państw Unii Europejskiej. Do osiągnięcia tego celu konieczne jest zobrazowanie rozmiaru zarówno sektora finansów publicznych, jak i gospodarki – za pośrednictwem zmiennych umożliwiających ich porównanie w czasie i w przestrzeni. Mając to na uwadze, sektor finansów publicznych zobrazowano za pośrednictwem dziewięciu zmiennych, a gospodarka została opisana za pomocą trzynastu zmiennych wyjaśniających. Złożoność tego zjawiska wymaga realizacji niekonwencjonalnego podejścia w tej dziedzinie badań. Podejście autorów opiera się na inteligentnej analizie danych (IDA) – metodologii obejmującej zestaw technik, które można zastosować do wydobywania użytecznej wiedzy z dużej ilości danych. W celu wskazania opisanych związków w artykule zastosowało techniki probabilistyczne – sieci Bayesa.
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