Learning from Experience, Simply

There is substantial academic interest in modeling consumer experiential learning. However, (approximately) optimal solutions to forward-looking experiential learning problems are complex, limiting their behavioral plausibility and empirical feasibility. We propose that consumers use cognitively simple heuristic strategies. We explore one viable heuristic—index strategies—and demonstrate that they are intuitive, tractable, and plausible. Index strategies are much simpler for consumers to use but provide close-to-optimal utility. They also avoid exponential growth in computational complexity, enabling researchers to study learning models in more complex situations. Well-defined index strategies depend on a structural property called indexability. We prove the indexability of a canonical forward-looking experiential learning model in which consumers learn brand quality while facing random utility shocks. Following an index strategy, consumers develop an index for each brand separately and choose the brand with the highest index. Using synthetic data, we demonstrate that an index strategy achieves nearly optimal utility at substantially lower computational costs. Using IRI data for diapers, we find that an index strategy performs as well as an approximately optimal solution and better than myopic learning. We extend the analysis to incorporate risk aversion, other cognitively simple heuristics, heterogeneous foresight, and an alternative specification of brands

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

Marine Microbial Diversity and its role in Ecosystem Functioning and Environmental Change

Seas and oceans cover more than 70% of the Earth’s surface, host the majority of its biomass, and contribute significantly to all global cycles of matter and energy. All life on Earth most likely originated from microbes in the sea. In today’s marine ecosystems, following billions of years of evolution, microbes such as Bacteria, Archaea, viruses, fungi and protists (including microalgae), dominate the living biomass. Recent rapid developments in molecular ecology, metagenomics and ecological modelling illustrate that microbes represent the most important biological group on Earth in terms of phylogenetic and functional diversity. In addition, interdisciplinary research has uncovered new and unexpected roles of microbes in the biogeochemical cycling of carbon, nitrogen, silica and iron and many other (trace) elements in our seas and oceans. Marine microorganisms produce the organic matter and oxygen required to sustain life and facilitate the storage, transport, and turnover of key biological elements. Thus, microorganisms are the foundation of life and are of critical importance to the habitability and sustainability of our planet. (...)Peer reviewe

Globalance towards a new world order : ethics matters and motivates : handbook with 250 graphs

2nd enlarged edition. Prefaces Mariana Bozesan, Liu Baocheng, Ernst Ulrich von Weizsäcker.First published 2020 under the title: Globalance : ethics handbook for a balanced world post-covid

Globalance : ethics handbook for a balanced world post-covid

Preface Ernst Ulrich von WeizsäckerWithout ethics and trust, our societies are in deep trouble. Corona is a chance to reflect. Stückelberger gives us an overview with a lot of background information. The book helps us to be aware of risks, opportunities and our responsibility. It encourages to more international cooperation. Many thanks! (Doris Leuthard, Former Minister of Environment of the Government of Switzerland, twice its President, Member of the Kofi Annan Foundation). - When the global village is facing disintegration and globalisation is heading towards the cliff, how can the human heart and ethics return to the trust of the global family? Stuckelberger is a profound observer and theorist of global ethics. He ignites the torch of people’s conscience. This book is very enlightening to provide a reference direction. (Cui Wantian, Entrepreneur in e-health, Professor of Economics, China). - An inspiring read, providing thought-provoking opportunities for Africa to re-envision its cumulative position and continental role in the new ‘globalanced’ world order Post-Covid. (Divya Singh, Prof. Dr, Chief Academic Officer, Stadio Holdings, South Africa). - One of the foremost global authorities on ethics, Stueckelberger, presents extremely interesting and new perspectives. This book is very relevant for various stakeholders in the global ecosystem, especially with Covid challenges. (Pavan Duggal, Dr, International Cyber Law Expert, Advocate Supreme Court of India). - Very few books provoke to think and to get insight. This book Globalance does it! (Alex Ageev, Professor of Economic Strategies, Russia

CORPORATE SOCIAL RESPONSIBILITY IN ROMANIA

The purpose of this paper is to identify the main opportunities and limitations of corporate social responsibility (CSR). The survey was defined with the aim to involve the highest possible number of relevant CSR topics and give the issue a more wholesome perspective. It provides a basis for further comprehension and deeper analyses of specific CSR areas. The conditions determining the success of CSR in Romania have been defined in the paper on the basis of the previously cumulative knowledge as well as the results of various researches. This paper provides knowledge which may be useful in the programs promoting CSR.Corporate social responsibility, Supportive policies, Romania