abstract: What causes social systems to resist change? Studies of the emergence of social complexity in archaeology have focused primarily on drivers of change with much less emphasis on drivers of stability. Social stability, or the persistence of social systems, is an essential feature without which human society is not possible. By combining quantitative modeling (Exponential Random Graph Modeling) and the comparative archaeological record where the social system is represented by networks of relations between settlements, this research tests several hypotheses about social and geographic drivers of social stability with an explicit focus on a better understanding of contexts and processes that resist change. The Valencian Bronze Age in eastern Spain along the Mediterranean, where prior research appears to indicate little, regional social change for 700 years, serves as a case study. The results suggest that social stability depends on a society’s ability to integrate change and promote interdependency. In part, this ability is constrained or promoted by social structure and the different, relationship dependencies among individuals that lead to a particular social structure. Four elements are important to constraining or promoting social stability—structural cohesion, transitivity and social dependency, geographic isolation, and types of exchange. Through the framework provided in this research, an archaeologist can recognize patterns in the archaeological data that reflect and promote social stability, or lead to collapse. Results based on comparisons between the social networks of the Northern and Southern regions of the Valencian Bronze Age show that the Southern Region’s social structure was less stable through time. The Southern Region’s social structure consisted of competing cores of exchange. This type of competition often leads to power imbalances, conflict, and instability. Strong dependencies on the neighboring Argaric during the Early and Middle Bronze Ages and contributed to the Southern Region’s inability to maintain social stability after the Argaric collapsed. Furthermore, the Southern Region participated in the exchange of more complex technology—bronze. Complex technologies produce networks with hub and spoke structures highly vulnerable to collapse after the destruction of a hub. The Northern Region’s social structure remained structurally cohesive through time, promoting social stability. / Dissertation/Thesis / Webpage with data tables and R code / Doctoral Dissertation Anthropology 2020

V této diplomové práci se zaměřujeme na vytvoření plně automatizovaného algoritmu pro předpovědi finančních řad, který se snaží využít kombinační proceduru na dvou úrovních mezi dvěma rodinami předpovědních modelů, Box-Jenkins a Exponenciální stavové modely, které jsou schopny modelovat jak homoskedastické tak heteroskedastické časové řady. Pro tento účel jsme navrhli selekční proceduru v prostředí MATLAB pro modely ARIMA. Výsledný kombinovaný model je pak aplikován několik finančních časových řad a jeho výkonost je diskutována.

MSc (Mathematics) / Department of Mathematics and Applied Mathematics / See the attached abstract below

This thesis deals with the use of statistical state space models of exponential smooth- ing for estimating the conditional probability distribution of future values of time series. This knowledge allows calculation of interval predictions, not only point forecasts. Meth- ods of exponential smoothing are described and set into the context of state space models. Analytical and simulation methods used in the calculation of interval predictions are presented, in particular simulations based on assumption of normality, bootstrap method or estimated parametric model. The methods are applied to simulated as well as real data and their results are compared. 1

The proliferation of Electric Vehicles (EVs) poses new challenges for the Distribution System Operator (DSO). For example, the rectifiers that are used for charging EV batteries could significantly influence Power Quality (PQ), in terms of harmonic distortion. The emissions from individual EV, are well regulated by current harmonic emission standards. But what the aggregation of multiple EV loads will look like is still uncertain and the research being made in this area is still in its early stage.  The DSO responsibilities include ensuring grid code compliance confirmed by PQ metering.  In general, 10 minute RMS values are sufficient. However, the large scale integration of non-linear loads, like EVs, could lead to new dynamic phenomena, possibly lost in the process of time aggregation. In this thesis PQ and, in more detail, the concept of harmonics, and how this is related to EVs, is presented. A current-harmonic load model using power exponential functions and built from actual measurement data during the individual charging of four different fully electric vehicles was constructed. The model was based on individual emitted current harmonics as a function of state of charge (SOC), and was used to deterministically simulate the simultaneous charging of six vehicles fed from the same bus. The aggregation of current harmonics up to the 11th was simulated while randomizing battery SOC, the start of charging, and the kind of vehicle. Additionally, an investigation of the impact on aggregation in time was conducted. The analysis clearly shows the importance of phase angle information, its correlation to SOC, and how the aggregation of EV loads is influenced by these factors. The analysis also shows that 10 minute RMS aggregation could lead to significant deviations from the “actual” (200ms) data.  This indicates that 10 minute value monitoring could lead to information losses.

Background: As more individuals continuously become more conscious of the external influences of their decisions, integrating social and ethical criteria and perceived non-monetary value in their investment decisions, the interest in socially responsible investments (SRI) has escalated in the past decade. Reflecting this shift, the Swedish Pension Agency continuously increases the requirements and sustainability demands for the funds available in the premium pension selection. To investigate the underlying variables affecting the decision to invest socially responsibly, the authors of this thesis studied Swedish pension beneficiaries’ demographics, attitudes and beliefs.  Purpose: The purpose of this thesis is to examine the socio-demographic and psychological determinants of pension beneficiaries' and the influence of these variables on the willingness to pay for socially responsible investments. The study will additionally explore the tendency to underestimate exponential growth in one’s pension savings.  Method: The study is conducted by collecting primary data in the form of quantitative research through an online questionnaire. Based on previous research, six hypotheses are developed. This in order to investigate the relationship between willingness to pay for socially responsible investments and several socio-demographic and psychographic variables. Additionally, to examine Swedish pension beneficiaries’ tendency to underestimate exponential growth. The data collected is analysed through a multiple linear regression model and other descriptive statistics to examine if the hypotheses are rejected or not.  Conclusion: The majority of the subjects in the study are willing to pay for SRI. Gender significantly impacts the willingness to pay for SRI, as men demonstrate a lower willingness to pay than women. Furthermore, altruistic values, concern for one’s pension savings, concern for ESG-related issues (environmental, social and governance) and perceived consumer effectiveness proves to have a significant impact on the willingness to pay for SRI. Further, the sample demonstrated a definite tendency to underestimate exponential growth.

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第19200号 / 人博第741号 / 新制||人||178(附属図書館) / 27||人博||741(吉田南総合図書館) / 32192 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 宇敷 重廣, 教授 森本 芳則, 准教授 木坂 正史 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM

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In this paper we conduct an out-of-sample forecasting exercise for monthly Swedish air traveler volumes. The models considered are multiplicative seasonal ARIMA, Neural network autoregression, Exponential smoothing, the Prophet model and a Random Walk as a benchmark model. We divide the out-of-sample data into three different evaluation periods: Pre-COVID-19, during COVID-19 and Post-COVID-19 for which we calculate the MAE, MAPE and RMSE for each model in each of these evaluation periods. The results show that for the Pre-COVID-19 period all models produce accurate forecasts, in comparison to the Random Walk model. For the period during COVID-19, no model outperforms the Random Walk, with only Exponential smoothing performing as well as the Random Walk. For the period Post-COVID-19, the best performing models are Random Walk, SARIMA and Exponential smoothing, with all aforementioned models having similar performance.

We first consider confidence intervals for a normal percentile, an exponential percentile and a uniform percentile. Then we develop confidence intervals for a difference of percentiles from two independent normal populations, two independent exponential populations and two independent uniform populations. In our study, we mainly focus on the maximum likelihood to develop our confidence intervals. The efficiency of this method is examined via coverage rates obtained in a simulation study done with the statistical software R.