If the Mind then Behavior and ¬ Behavior

The book is a stroll through those subject matters I have considered worth of research. The targets vary but the focus is on the processes of the mind, and behaviors that join with it. There are some attempts of conceptualizations, and methodical tryouts to find probable causes assumed to exist in the mindamic or in the mind dynamic. However, to figure out of behavioral facts is not so rewarding a thing as one might expect at the first sight. As anybody wrestling with behavioral research, knows. Therefore, there lives hope to knockout some problems, at least. Inventions are rare, and discoveries several, probably, because a human being has been about the same during the last 120000 years or so but a sparkle smolders

Evaluating the impact of social-media on sales forecasting: a quantitative study of worlds biggest brands using Twitter, Facebook and Google Trends

In the world of digital communication, data from online sources such as social networks might provide additional information about changing consumer interest and significantly improve the accuracy of forecasting models. In this thesis I investigate whether information from Twitter, Facebook and Google Trends have the ability to improve daily sales forecasts for companies with respect to the forecasts from transactional sales data only. My original contribution to this domain, exposed in the present thesis, consists in the following main steps: 1. Data collection. I collected Twitter, Facebook and Google Trends data for the period May 2013 May 2015 for 75 brands. Historical transactional sales data was supplied by Certona Corporation. 2. Sentiment analysis. I introduced a new sentiment classification approach based on combining the two standard techniques (lexicon-based and machine learning based). The proposed method outperforms the state-of-the-art approach by 7% in F-score. 3. Identification and classification of events. I proposed a framework for events detection and a robust method for clustering Twitter events into different types based on the shape of the Twitter volume and sentiment peaks. This approach allows to capture the varying dynamics of information propagation through the social network. I provide empirical evidence that it is possible to identify types of Twitter events that have significant power to predict spikes in sales. 4. Forecasting next day sales. I explored linear, non-linear and cointegrating relationships between sales and social-media variables for 18 brands and showed that social-media variables can improve daily sales forecasts for the majority of brands by capturing factors, such as consumer sentiment and brand perception. Moreover, I identified that social-media data without sales information, can be used to predict sales direction with the accuracy of 63%. The experts from the industry consider the results obtained in this thesis to be valuable and useful for decision making and for making strategic planning for the future

Soundscape and the Experience of Positive Silence

This body of work employs a practice-based research methodology to explore the experience of silence as positive, of benefit to the individual and, by extension, wider society. The research is positioned within the related fields of Sound Art and Sound Studies with the practice component including soundwalks, sound installation, exhibition and phenomenological enquiry initiated through listenings and reflections. Current research in this area has explored the value of silence through quiet space studies, acoustics and psychoacoustics as well as research in the field of psychology around the human experience of solitude, mindful awareness and distraction. This doctoral research draws upon the insights of these disciplines to inform both the artworks and thinking that cohered into the themes explored in this commentary. Solitary and shared silences characterised by thresholds, masking, sounds of nature, simplicity, familiarity, safety and quality of attention are explored. In so doing, psychological theories of extended mind, construal level and psychological distance are considered in relation to the web of interactions between individual and soundscape. In all, these investigations revealed auditory distraction as a feature of the soundscape that consistently undermined the experience of silence as positive. Acknowledging the growing influence of the ‘attention economy,’ the work explores the psychoacoustic basis for auditory attention and concludes by forwarding practical strategies for working with distraction that have been developed and refined through listening exercises and participatory arts practice

Developing a Predictive Model of Depression and Suicidal Tendencies in Pilots

The mental health of commercial airline pilots is as important as their physical health because of their immense responsibility for the safety of their passengers and crews. Pilot suicides that end in fatal aircraft crashes result in many injuries and deaths. Although depression and suicidal tendencies are common across all genders, ages, ethnicities, and backgrounds, the mental health of aviation pilots has been challenging to evaluate and quantify through routine flight medical exams. The purpose of this study was to determine predictive factors of depression and suicidal tendencies among commercial airline pilots. Previous research has determined predictive factors of depression and suicidal tendencies in both the general population and specific subgroups, such as police and military personal. There has been little research focused on predicting depression and suicidal tendencies in 14 C.F.R. Part 121 airline pilots; thus, exposing a significant gap in the related aviation literature. This study employed a non-experimental correlational research design to investigate depression and suicidal tendencies among commercial airline pilots. From 728 responses to the online questionnaire website, 570 cases qualified for analysis. Thirteen exogenous variables were assessed through structural equation modeling (SEM) to identify predictors of Depression and Suicidal Tendencies, the endogenous variables. The results indicated support for three hypotheses, and two other significant relationships were discovered. Level of Stress and Job Satisfaction had significant influence on Depression, while Self-Reported Childhood Trauma, Level of Stress, and Depression had significant influence on Suicidal Tendencies. Level of Stress was positively related to Depression, suggesting Depression scores increase with Stress scores. Job Satisfaction was negatively related to Depression, suggesting Depression scores decrease with increases in Job Satisfaction scores. Stress, Childhood Trauma, and Depression were positively related to Suicidal Tendencies, suggesting the score for Suicidal Tendencies increases with an increase in Stress, Childhood Trauma, or Depression. The results also indicated a significant relationship between the COVID-19 control variable and Depression, but not with Suicidal Tendencies. There was no change in model fit when the control variable was removed, suggesting minimal effects of a perceived threat from COVID-19 on both Depression and Suicidal Tendencies. The initial SEM model explained 7.3% of the variance in Depression and 2.1% of the variance in Suicidal Tendencies. The final SEM model showed improvement in the amount of variance explained in both Depression and Suicidal Tendencies, explaining 8.1% of the variance in Depression and 27.1% of the variance in Suicidal Tendencies. The findings from this research provide insights into predicting the mental health of airline pilots. This information can benefit regulating agencies and airline hiring operations to ensure the safety of commercial air travel

Place Over Politics: Power, Strategy, Terrain, And Regime Type In Interstate War Outcomes, 1816-2003

While the study of war occurrence is among the primary considerations of the field of international relations, only recently has attention turned towards the study of war outcomes. This attention is best represented by the democratic victory proposition, which suggests that democracies win the majority of their wars by virtue of being democratic. However, elements of this study are currently incipient. In turn, this dissertation generates a novel set of variables to measure the impact of terrain on war outcomes, including measures of spatial extent, topographic heterogeneity, and land cover heterogeneity. These metrics are generated for all 94 interstate wars in the correlates of war population between 1816-2003, as well as disaggregated forms of WWI, WWII, and Vietnam – bringing the total to 105 wars. These data are then used to analyze war outcomes using multinomial logistic regression. The results suggest that, at present, the democratic victory proposition is incomplete. Further research is needed to explore the complex relationship between state capabilities, strategy, regime type, and terrain

Place Over Politics: Power, Strategy, Terrain, And Regime Type In Interstate War Outcomes, 1816-2003

While the study of war occurrence is among the primary considerations of the field of international relations, only recently has attention turned towards the study of war outcomes. This attention is best represented by the democratic victory proposition, which suggests that democracies win the majority of their wars by virtue of being democratic. However, elements of this study are currently incipient. In turn, this dissertation generates a novel set of variables to measure the impact of terrain on war outcomes, including measures of spatial extent, topographic heterogeneity, and land cover heterogeneity. These metrics are generated for all 94 interstate wars in the correlates of war population between 1816-2003, as well as disaggregated forms of WWI, WWII, and Vietnam – bringing the total to 105 wars. These data are then used to analyze war outcomes using multinomial logistic regression. The results suggest that, at present, the democratic victory proposition is incomplete. Further research is needed to explore the complex relationship between state capabilities, strategy, regime type, and terrain

Forecast reconciliation : methods, structures, criteria, and a new approach with spatial data

This PhD dissertation is a collection of four papers that aim to explore, in the marketing field, the research on hierarchical and grouped time-series reconciliation approaches. Those approaches are necessary when different departments of an organization have different needs regarding forecast aggregations. This work focuses, besides reconciliation approaches, on time-series forecasting methods, and on the importance of geographical information to better forecast and plan marketing strategies. The first paper is theoretical and argues on the importance to marketing of having accurate forecasts. It explores the current state of marketing research on modelling in general, and on forecast specifically. It covers the classifications of methods, datasets explored on current research, the basic model studied, and existing gaps. The paper concludes that marketing focuses on explanation, living a gap on accuracy evidence and on the applicability of the models proposed. The second paper explores those gaps by applying two current topics of discussion on forecasting time-series literature: machine learning techniques and ensemble models. These methods are easy to implement and are reported in the literature to improve accuracy. The paper proposes an adaptation to portfolio optimization to calculate the weights of an ensemble based on each base model's accuracy and the covariance matrix of such accuracies. The proposed approach outperforms all 15 base models and the equal weights benchmark. The paper also provides evidence that, if single models are compared, statistical methods have better accuracy than the machine learning methods applied. The third paper uses a statistical method to forecast time-series (i.e. sales) combined with different structure and criteria of aggregation. The aim of the paper is to compare different criteria based on marketing mix variables. The empirical application presented in the paper indicates whether product category, channel type or region (geographic location) works best alone or combined. It also gives evidence of the importance of geographical considerations to improve forecast accuracy. The last paper furthers explore this finding by proposing a new reconciliation approach that distributes an aggregate forecast to lower levels of disaggregation using a gravitational model. This paper also contributes to the literature by comparing statistical, machine learning and deep learning methods (LSTM). All papers presented in this dissertation use open-source tools, combining proprietary data that is natural to the process of every organization and publicly available data. The focus is on methods and tools that are generalizable to all types of goods, can be easily applied by any organization, with relatively low investment. The contributions of the PhD dissertation are (1) to compare statistical, machine learning and deep learning methods to forecast sales on single and ensemble models; (2) to provide evidence on the criteria and structure of aggregation that improves forecast accuracy the most; and (3) to offer a new approach to distribute an aggregate forecast to new geographical regions when no historical data is available.A presente tese de doutorado é uma coleção de quatro artigos científicos desenvolvidos com o objetivo de explorar, dentro da área de marketing, a pesquisa sobre reconciliação de previsão de séries temporais com estrutura hierárquica ou agrupada. Reconciliação de previsões é necessária quando diferentes áreas de uma organização necessitam de previsões em diferentes níveis de agregação. O presente conjunto de estudos foca, além da reconciliação de previsões, em métodos de previsão de series temporais e na importância de informações geográficas para melhor prever e planejar estratégias de marketing. O primeiro artigo apresentado é uma revisão da literatura atual em modelagem de marketing, focando nos estudos sobre previsão. O artigo argumenta sobre a importância para o marketing em ter previsões, nas diferentes classificações dos métodos, nos tipos de dados usados, no modelo básico estudado e nos potenciais para estudos futuros. O artigo conclui que marketing precisa de estudos que evidenciem acurácia e sejam fáceis de implementar na prática. O segundo artigo procurar preencher essas lacunas aplicando técnicas de machine learning e ensemble. Essas técnicas são discutidas atualmente na teoria sobre previsão de séries temporais por prometerem facilidade de aplicação e melhoria em acurácia. O artigo propõe uma adaptação da otimização de portfólio como estratégia para calcular os pesos dos diferentes modelos que compõe um ensemble. A proposta do artigo tem melhor acurácia, no teste realizado, que os 15 modelos únicos (estatísticos e de machine learning) e o ensemble usando pesos iguais para todos os modelos. O artigo contribui também para a discussão sobre machine learning para previsão de séries temporais, demonstrando, nesse caso, a superioridade dos modelos estatísticos. O terceiro artigo usa um método estatístico combinado com diferentes estruturas e critérios de agregação para prever séries temporais (vendas). O objetivo do artigo é comparar diferentes critérios baseados em variáveis de marketing. A aplicação empírica dá indícios de que informações sobre a localização das vendas aumenta a acurácia das previsões. O último artigo explora esse achado ao propor uma estratégia alternativa de reconciliação de previsões. Essa estratégia distribui uma previsão feita em um nível agregado para níveis desagregados usando um modelo gravitacional. O artigo também contribui para a literatura ao comparar métodos estatísticos e de machine learning com long short-term memory (LSTM), um método de deep learning. Todos os artigos usam ferramentas open-source e combinam dados públicos com dados proprietários que resultam naturalmente dos processos de qualquer organização. O foco dos estudos são métodos e ferramentas generalizáveis para todos os segmentos que possam ser facilmente implementados por qualquer empresa, com relativamente baixos investimentos. As contribuições dessa tese de doutorado são (1) comparar métodos estatísticos, de machine learning e deep learning para prever vendas em modelos únicos e combinados (ensemble); (2) prover evidências sobre os critérios e estruturas de agregação que melhoram a acurácia das previsões; e (3) oferecer uma nova estratégia para distribuir uma previsão agregada em novas regiões geográficas quando dados históricos não estão disponíveis