abstract: This dissertation explores how practices and interactions of actors at different scales structure social networks and lead to the emergence of social complexity in middle range societies. To investigate this process, I apply a complex adaptive systems approach and a methodology that combines network science with analytical tools from economics to the three sub-periods of the Prehistoric Bronze Age (The Philia Phase, PreBA 1 and PreBA 2) on Cyprus, a transformational period marked by social and economic changes evident in the material record. Using proxy data representative of three kinds of social interactions or facets of social complexity, the control of labor, participation in trade networks, and access to resources, at three scales, the community, region and whole island, my analysis demonstrates the variability in and non-linear trajectory for the emergence of social complexity in middle range society. The results of this research indicate that complexity emerges at different scales, and times in different places, and only in some facets of complexity. Cycles of emergence are apparent within the sub-periods of the PreBA, but a linear trajectory of increasing social complexity is not evident through the period. Further, this research challenges the long-held notion that Cyprus' involvement in the international metal trade lead to the emergence of complexity. Instead, I argue based on the results presented here, that the emergence of complexity is heavily influenced by endogenous processes, particularly the social interactions that limited participation in an on-island exchange system that flourished on the island during the Philia Phase, disintegrated along the North Coast during the PreBA 1 and was rebuilt across the island by the end of the period. Thus, the variation seen in the emergence of social complexity on Cyprus during the PreBA occurred as the result of a bottom-up process in which the complex and unequal interactions and relationships between social actors structured and restructured social networks across scales differently over time and space. These results speak more broadly about the variability of middle range societies and the varying conditions under which social complexity can emerge and add to our understanding of this phenomenon. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2017

Income growth and income inequality is an important theme in Economic research. It has been debated for decades whether income inequality hinders or enhances income growth. One of the classic models of this relationship was the Kuzenets curve which shows inequality against income per capita can be defined by an inverted U-shaped curve, over a period of time. The purpose of the paper is to see to see the relationship between income growth and inequality on a municipality level. To do this, four econometric panel data models were constructed with data gathered from Statbank Denmark. Log of income was used as the dependent variable and different measures of inequality were used as independent variables among other variables (public expenditure, education, population density, demographic composition, taxation). Results from these models show how income growth is positively related to income inequality, with vastly higher growth at the top end of the income distribution in Denmark. The implications of these findings can show that a trade-off between income inequality and income growth is not true, and it is possible that both variables work in tandem. Other factors such as education and demographic composition were also positively correlated with income growth, while other factors, such as taxation, were statistically insignificant. Comprehensive research on inequality and income growth at a municipality level is sparse, especially in the case of Denmark. Thus, this study contributes to research in regional economics.

Many factors influence quality of higher education. Current research mostly works with economic factors (GDP, higher education expenditures etc.). However, there are also publications that examine an impact of sociological aspects on quality of higher education. My research examined the impact of income inequality on quality of tertiary education. In the analysis of socioeconomic data of 76 countries I have proven that there is no linear relationship between income inequality and quality of tertiary education. According to my results the size of population, GDP per capita and being English speaking country are main drivers of quality of tertiary education. Modified model without outliers also shows that there is a positive effect of R&D expenditures on quality of tertiary education.

The aim of this work is to analyze and assess the financial situation of households in the Czech Republic in the period 2005 to 2009. The work also focuses on the assessment of the financial situation of households made up of unemployed and incomplete families with children. Additional analyses deal with the age aspect, municipality size and level of education of head of household. Then I will calculate the Gini coefficient and construct the Lorenz curve for the entire period 2005 - 2009 and regionally for year 2009.

The degree of inequality present in the distribution of income may be measured with a gini coefficient. If the distribution is found to empirically fit a particular distribution function, then the gini coefficient may be derived from the mean value of income and the variation from the mean. For the purpose of this study, the Beta II distribution was used as the function which most closely approximates the actual distribution of income. The Beta II function provides the skewness which is normally found in an income distribution as well as fulfilling other required characteristics. The degree of inequality was approximated for the distribution of income from all sources and from ten separate components of income sources in constant (1973) dollars. Next, permanent income from all sources and from the ten component sources was estimated based upon actual income using the double exponential smoothing forecasting technique. The estimations of permanent income, which can be thought of as expected income, were used to derive measures of permanent income inequality. The degree of actual income inequality and the degree of permanent income inequality, both being represented by the hypothetical gini coefficient , were compared and tested for statistical differences. For the entire period under investigation, 1952 to 1979, the net effect was no statistically significant difference between permanent and actual income inequality, as was expected. However, significant differences were found in comparing year by year. Relating permanent income inequality to the underlying, structural inequality present in a given distribution, conclusions were drawn regarding the role of mobility in its ability to alter the actual distribution of income. The impact of business fluctuations on the distribution of permanent income relative to the distribution of actual income was studied in an effort to reach general conclusions. In general, cyclical upswings tend to reduce permanent inequality relative to actual inequality. Thus, despite the empirically supported relationship between income inequality and economic growth, it would appear that unexpected growth tends to favor a more equal distribution of income.

This diploma thesis presents description on several algorithms for decision trees induction and software RapidMiner. The first part of the thesis deals with partition and terminology of decision trees. There’re described all algorithms for decision tree construction in RapidMiner. The second part deals with implementation and comparison of chosen algorithms. The application was developed in C++. Based on the real datesets the comparisson of different algorithms was realized using Rapid Miner 4.0.

Over the past two decades there has been a significant increase in the support for radical-right populist parties in Europe. Simultaneously the income inequality has been rising. The aim of this thesis is to examine how income inequality affects the support for populist radical-right parties. To achieve this, we study the support for the Sweden Democrats, a radical-right populist party, and income inequality measured as the Gini coefficient. Using Swedish municipality level panel data of the election years from 2002 to 2014, a fixed effects-method is employed to examine the relationship between the Gini coefficient and support for the Sweden Democrats. Interestingly, the results show a robust statistically significant negative relationship between income inequality and support for the Sweden Democrats. Thus, our findings indicate that increased inequality decreases the support for the Sweden Democrats.

This paper investigates the effect of financial development on income inequality in 20 African countries. Theory and a growing number of empirical studies suggest that the former exerts a negative impact on the latter by enabling low-income holders to undertake income-enhancing education and business investments, thereby promoting a tighter income distribution. However, using the share of GDP constituted by domestic credit to the private sector and broad money respectively as proxies for financial development, the results of this study fail to give significant evidence of this income-equalising effect. Given the heterogeneous nature of the economies studied here and the fact that data quality and quantity improve over time, it is believed that country-specific studies and future research can offer more conclusive results on how financial development influences income distribution in the African context. This would also provide a stronger foundation for policy recommendations in the continent’s plight to address the persistent high levels of income inequality.

The management of missing data is a major concern in classification model generation in all fields but poses a particular challenge in situations where there is only a small quantity of sparse data available. In the field of medicine, this is not an uncommon problem. While widely subscribed methodologies like logistic regression can, with minor modifications and potentially much labor, provide reasonable insights from the larger and less sparse datasets that are anticipated when analyzing diagnosis of common conditions, there are a multitude of rare conditions of interest. Primary angiitis of the central nervous system (PACNS) is a rare but devastating entity that given its range of presenting symptoms can be suspected in a variety of circumstances. It unfortunately continues to be a diagnosis that is hard to make. Aside from some general frameworks, there isn’t a rigorously defined diagnostic approach as is the case in other more common neuroinflammatory conditions like multiple sclerosis. Instead, clinicians currently rely on experience and clinical judgement to guide the reasonable exclusion of potential inciting entities and mimickers. In effect this results in a smaller quantity of heterogenous that may not optimally suited for more traditional classification methodology (e.g., logistic regression) without substantial contemplation and justification of appropriate data cleaning / preprocessing. It is therefore challenging to make and analyze systematic approaches that could direct clinicians in a way that standardizes patient care. In this thesis, a machine learning approach was presented to derive quantitatively justified insights into the factors that are most important to consider during the diagnostic process to identify conditions like PACNS. Modern categorization techniques (i.e., random forest and support vector machines) were used to generate diagnostic models identifying cases of PACNS from which key elements of diagnostic importance could be identified. A novel variant of a random forest (RF) approach was also demonstrated as a means of managing missing data in a small sample, a significant problem encountered when exploring data on rare conditions without clear diagnostic frameworks. A reduced need to hypothesize the reasons for missingness when generating and applying the novel variant was discussed. The application of such tools to diagnostic model generation of PACNS and other rare and / or emerging diseases and provide objective feedback was explored. This primarily centered around a structured assessment on how to prioritize testing to rapidly rule out conditions that require alternative management and could be used to support future guidelines to optimize the care of these patients. The material presented herein had three components. The first centered around the example of PACNS. It described, in detail, an example of a relevant medical condition and explores why the data is both rare and sparse. Furthermore, the reasons for the sparsity are heterogeneous or non-monotonic (i.e., not conducive to modelling with a singular model). This component concludes with a search for candidate variables to diagnose the condition by means of scoping review for subsequent comparative demonstration of the novel variant of random forest construction that was proposed. The second component discussed machine learning model development and simulates data with varying degrees and patterns of missingness to demonstrate how the models could be applied to data with properties like what would be expected of PACNS related data. Finally, described techniques were applied to separate a subset of patients with suspected PACNS from those with diagnosed PACNS using institutional data and proposes future study to expand upon and ultimately verify these insights. Further development of the novel random forest approach is also discussed.

Transportation is one of the largest sectors contributing to CO2-emissions, and has doubled its emissions in 30 years. Despite this, studies of the environmental Kuznets curve (”EKC”) often focus on stationary industry emissions. Studies of the EKC have detected an N-shape, rather than an inverted U-curve, indicating that rich nation’s emissions, in fact, increase again after the downturn. Possibly, this could be explained by a trend for inhabitants of wealthy countries with high equality to purchase local products and potentially reverse a trend of dirty-industry emigration. Local production and movement of intermediate goods demand domestic goods transportation. To my knowledge, no previous research has studied how changes in GDP/capita, trade intensity and GINI-index are related to CO2-emissions from domestic goods transportation in wealthy countries with high equality. To study the relationship, mathematical tests using Panel data with Fixed Effects Regression were used. Five countries qualified for the tests, having both high equality (lowest GINI-index) and high GDP/capita, and were included in the study for the year interval 2000-2020. Test results showed a significant correlation between the following: (1) wealth coincides positively with CO2-emissions, (2) trade intensity coincides negatively with CO2-emissions and (3) GINI-index coincides positively with CO2-emissions. Methodologically, this study contributes with the estimator GDP/GINI-index, rather than GDP solely, which could be a better estimator for the richness of a country’s population. The mathematical test results indicate that domestic goods transportation could be a reason for the increased CO2-emissions from developed wealthy countries. This could be a development of the environmental Kuznets curve.