Wissen über das Raumschiff Erde : eine soziologische Perspektive auf die Kapazitätsentwicklung

Jappe-Heinze LA. Knowledge about the spaceship Earth : a sociological perspective on capacity development. Bielefeld (Germany): Bielefeld University; 2007.Die vorliegende Arbeit leistet einen Beitrag zu Umweltsoziologie und Wissenschaftssoziologie. Der erste Teil präsentiert einen neuen Forschungsansatz, um die Rolle von Forschung, Technologie und Innovation (engl. STI) für eine nachhaltige Entwicklung empirisch zu untersuchen. Im Zentrum dieser Analyse steht der Begriff der STI-Kapazität. Der zweite Teil untersucht internationale Kooperation in Umweltwissenschaften. Einen Schwerpunkt bildet der Vergleich des Internationalen Geosphären-Biosphären Programms (IGBP) und des Internationalen Hydrologischen Programms (IHP) der Unesco. Diese Fälle stehen exemplarisch für neue Organisationsformen zum Aufbau globalen Umweltwissens - im doppelten Sinn globaler Umweltbeobachtung und einer weltweiten Wissensverbreitung. Derartige Kooperationsprogramme sind aus institutionalistischer Sicht jedoch bislang kaum erforscht. Der Begriff der STI-Kapazität bezieht sich auf das Umweltpolitik-Modell von M. Jänicke. Diesem Modell zufolge umfasst umweltpolitische Kapazität die längerfristigen, strukturellen Bedingungen politischer Handlungsfähigkeit in Abgrenzung von kurzfristigen, situativen Handlungsbedingungen der Tagespolitik. Jänicke geht davon aus, dass moderne Gesellschaften ein großes Potenzial zur Steigerung ihrer umweltbezogenen Problemlösefähigkeit besitzen, auch wenn diese Fähigkeiten bislang nicht ausreichen, um zentrale Umweltprobleme dauerhaft zu lösen. Die Kapazitätsentwicklung stellt somit eine strategische Aufgabe der Umweltpolitik dar. Während Jänicke den Kapazitätsbegriff auf das politische System anwendet, geht es in der vorliegenden Arbeit um das Wissenschaftssystem und um die Innovationsfähigkeit in Wirtschaft und Gesellschaft. STI-Kapazität ist die Fähigkeit einer Gesellschaft, Wissen und technologische Neuerungen zu schaffen und anzuwenden. Der Kapazitätsaufbau ist Teil eines umfassenden gesellschaftlichen Wandels angesichts begrenzter ökologischer Lebensgrundlagen. Im ersten Kapitel wird der Begriff der STI-Kapazität inhaltlich präzisiert. Dabei wird die These vertreten, dass sich die langfristigen Herausforderungen für Forschung und Innovation in vier große Handlungsfelder einteilen lassen: (1.) die ökologische Modernisierung und Transformation von Industrie- und Dienstleistungssektoren, (2.) das Management von Ökosystemen und Ökosystemdiensten, (3.) die Analyse und Bewertung von Umweltrisiken, und (4.) die Anpassung der Gesellschaft an irreversible Umweltveränderungen. Diese Taxonomie dient als Gliederung für einen Literaturüberblick, der bislang getrennte Forschungsansätze verbindet und dadurch aufzeigt, wie das Forschungsgebiet einer umweltsoziologischen STI-Forschung aussehen könnte. Im Anschluss daran untersucht das zweite Kapitel die methodische Operationalisierung des Kapazitätsbegriffs. Im zweiten Teil untersuchen wir internationale Kooperation, ein wichtiges Teilgebiet der wissenschaftlichen Kapazitätsentwicklung. Denn erstens konzentriert sich die Forschungskapazität bislang in führenden Industrieländern. Dadurch ist es - von der satellitengestützten Beobachtung abgesehen - nur begrenzt möglich, Umweltveränderungen weltweit zu erforschen. Zweitens spielen internationale Programme in der Klimaforschung eine wichtige Rolle. Die Klimaforschung ist ein beeindruckendes Beispiel für den Aufbau wissenschaftlicher Kapazität seit den 1970er Jahren. Daher dienten ihre Kooperationsprogramme in der Praxis zum Teil als Modelle für andere Umweltwissenschaften. Das dritte Kapitel untersucht den Internationalisierungsgrad umweltwissenschaftlicher Forschung zunächst auf der Feldebene, gemessen in Kopublikationen im Science Citation Index (SCI). Zentral ist hierbei eine neue theoretische Erklärung für Unterschiede im Internationalisierungsgrad, die sich auf R. Whitleys Theorie der wissenschaftlichen Arbeitsorganisation bezieht. Anhand von vier Feldern belegen wir, dass der Internationalisierungsgrad mit inhaltlichen Merkmalen des Forschungsgegenstandes zusammenhängt. Als Merkmale der kognitiven Struktur unterscheiden wir systemisch-globale von kumulativ-globalen Umweltveränderungen. Das vierte Kapitel vergleicht die Kooperationsprogramme IGBP und Unesco-IHP aus institutionalistischer Sicht. Das IGBP ist ein disziplinübergreifendes Netzwerk der Erdsystemforschung, während das IHP als Teil der Unesco zwischen Wissenschaft und internationaler Politik angesiedelt ist. Verglichen werden Ziele, Organisationsstrukturen, internationale Beteiligung und Entwicklungsdynamik im Zeitverlauf. Die Analyse basiert auf der Auswertung von Literatur und Dokumenten, Interviews und teilnehmender Beobachtung im Rahmen eines Forschungsaufenthalts beim IHP-Sekretariat. Aus dem Fallvergleich werden allgemeine institutionelle Faktoren abgeleitet, die den Erfolg umweltwissenschaftlicher Kooperationsprogramme beeinflussen. Außerdem werden Empfehlungen zur Weiterentwicklung des IHP formuliert und wichtige Fragen für die weitere Forschung aufgeworfen.The present work makes a contribution to environmental sociology and the sociology of science. The first part presents a new approach to the empirical investigation of the role of research, technology and innovation (STI) for sustainable development. The term STI capacity forms the core of this analysis. The second part examines international cooperation in environmental sciences. The comparative analysis of the International Geosphere-Biosphere Programme (IGBP) and the International Hydrological Programme (IHP) of Unesco is a main focus of this part. These cases exemplify new organisational structures to build up global environmental knowledge - in the double sense of global environmental observation and a diffusion of environment-related knowledge worldwide. Yet such collaboration programmes have hardly been researched until now from an institutionalist perspective. Our understanding of "capacity" refers to the model of environmental policy performance of M. Jänicke. According to this model, capacity describes the longer-term structural conditions for environmental policy actions, as opposed to situative opportunities and obstacles that are part of daily politics. Jänicke assumes that modern societies possess a great potential to increase their problem solving capabilities with regard to the environment, even if these capabilities are as yet inadequate to solve central environmental problems permanently. The development of capacity thus represents a strategic task of environmental policy. While Jänicke investigates capacity with regard to the political system, the present dissertation applies the concept to the science system and the innovative ability of industry and society. STI capacity is the ability of a society to create and apply knowledge and technological innovations. Capacity building is part of a comprehensive social transition process in view of limited planetary life support systems. In the first chapter, the content of STI capacity is clarified. Our thesis is that the long-term challenges for research and innovation can be classified in four major task domains: (1.) the ecological modernisation and transformation of industrial and service sectors, (2.) the management of ecosystems and ecosystem services, (3.) the assessment of environmental risks, and (4.) the adaptation of society to irreversible, anthropogenic environmental changes. The taxonomy of the four task domains serves as the structure for a literature review. Linking research approaches which were separate until now, we show how STI research as a field of environmental sociology could look. Following this, the second chapter investigates the methodological operationalisation of the term capacity. In the second part, we examine international cooperation in environmental sciences. International cooperation is an important issue for scientific capacity development. Firstly, research capabilities are as yet concentrated in the leading industrialised countries. Beyond satellite-based observation, this uneven distribution of capacity makes it difficult to track environmental changes worldwide. Secondly, international programmes play a significant role in climate research. Climate research is an impressive example for the development of research capacity since the 1970s. In view of this success story, efforts have been made to implement similar programme structures in other environmental science fields also. The third chapter examines the frequency of international cooperation in environmental research at the field level, measured in co-publications in the Science Citation Index (SCI). We offer a new theoretical explanation for differences in the degree of internationalisation across fields with reference to Richard Whitley's theory of scientific work organisation. Based on four environmental science fields, we demonstrate that the degree of internationalisation is linked with characteristics of the research object. As characteristics of the cognitive structure we differentiate globally systemic from cumulatively global environment changes. The fourth chapter compares the cooperation programmes IGBP and Unesco-IHP from an institutionalist perspective. IGBP is a transdisciplinary network in earth system science, while IHP as a part of Unesco is positioned between the science system and international politics. The comparison includes programme targets, organisational structures, international participation and programme evolution over time. The analysis is based on sources in the literature, internal documents, interviews and participant observation during a research stay in the IHP secretariat. Institutional factors are derived which have significance for the success of multilateral cooperation programmes in general. In addition, we formulate policy recommendations for the further development of IHP and identify relevant questions for further research

Predicting Forex Currency Fluctuations Using a Novel Bio-inspired Modular Neural Network

This thesis explores the intricate interplay of rational choice theory (RCT), brain modularity, and artificial neural networks (ANNs) for modelling and forecasting hourly rate fluctuations in the foreign exchange (Forex) market. While RCT traditionally models human decision-making by emphasising self-interest and rational choices, this study extends its scope to encompass emotions, recognising their significant impact on investor decisions. Recent advances in neuro- science, particularly in understanding the cognitive and emotional processes associated with decision-making, have inspired computational methods to emulate these processes. ANNs, in particular, have shown promise in simulating neuroscience findings and translating them into effective models for financial market dynamics. However, their monolithic architectures of ANNs, characterised by fixed struc- tures, pose challenges in adaptability and flexibility when faced with data perturbations, limiting overall performance. To address these limitations, this thesis proposes a Modular Convolutional orthogonal Recurrent Neural Net- work with Monte Carlo dropout-ANN (MCoRNNMCD-ANN) inspired by recent neuroscience findings. A comprehensive literature review contextualises the challenges associated with monolithic architectures, leading to the identification of neural network structures that could enhance predictions of Forex price fluctuations, such as in the most prominently traded currencies, the EUR/GBP pairing. The proposed MCoRNNMCD-ANN is thoroughly evaluated through a detailed comparative analysis against state-of-the-art techniques, such as BiCuDNNL- STM, CNN–LSTM, LSTM–GRU, CLSTM, and ensemble modelling and single- monolithic CNN and RNN models. Results indicate that the MCoRNNMCD- ANN outperforms competitors. For instance, reducing prediction errors in test sets from 19.70% to an impressive 195.51%, measured by objective evaluation metrics like a mean square error. This innovative neurobiologically-inspired model not only capitalises on modularity but also integrates partial transfer learning to improve forecasting ac- curacy in anticipating Forex price fluctuations when less data occurs in the EUR/USD currency pair. The proposed bio-inspired modular approach, incorporating transfer learning in a similar task, brings advantages such as robust forecasts and enhanced generalisation performance, especially valuable in domains where prior knowledge guides modular learning processes. The proposed model presents a promising avenue for advancing predictive modelling in Forex predictions by incorporating transfer learning principles

Processing social media text for the quantamental analyses of cryptoasset time series

This thesis analyses social media text to identify which events and concerns are associated with changes between phases of rising and falling cryptoasset prices. A new cryptoasset classification system, based on token functionality, highlights Bitcoin as the largest example of a 'crypto-transaction' system and Ethereum as the largest example of a 'crypto-fuel' system. The price of ether is only weakly correlated with that of bitcoin (Spearman's rho 0.3849). Both bitcoin and ether show distinct phases of rising or falling prices and have a large, dedicated social media forum on Reddit. A process is developed to extract events and concerns discussed on social media associated with these different phases of price movement. This innovative data-driven approach circumvents the need to pre-judge social media metrics. First, a new, non-parametric Data-Driven Phasic Word Identification methodology is developed to find words associated with the phase of declining bitcoin prices in 2017-18. This approach is further developed to find the context of these words, from which topics are inferred. Then, neural networks (word2vec) are applied to evolve analysis from extracting words to extracting topics. Finally, this work enables the development of a framework for identifying which events and concerns are plausible causes of changes between different phases in the ether and bitcoin price series. Consistent with Bitcoin providing a form of money and Ethereum providing a platform for developing applications, these results show the one-off effect of regulatory bans on bitcoin, and the recurring effects of rival innovations on ether price. The results also suggest the influence of technical traders, captured through market price discourse, on both cryptoassets. This thesis demonstrates the value of a quantamental approach to the analysis of cryptoasset prices

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

[EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques