Black Holes: Eliminating Information or Illuminating New Physics?

Black holes, initially thought of as very interesting geometric constructions of nature, over time, have learnt to (often) come up with surprises and challenges. From the era of being described as merely some interesting and exotic solutions of \gr, they have, in modern times, really started to test our confidence in everything else, we thought we know about the nature. They have in this process, also earned a dreadsome reputation in some corners of theoretical physics. The most serious charge on the black holes is that they eat up information, never to release and subsequently erase it. This goes absolutely against the sacred principles of all other branches of fundamental sciences. This realization has shaken the very base of foundational concepts, both in quantum theory and gravity, which we always took for granted. Attempts to exorcise black holes of this charge, have led us to crossroads with concepts, hold dearly in quantum theory. The sphere of black hole's tussle with quantum theory has readily and steadily grown, from the advent of the Hawking radiation some four decades back, into domain of quantum information theory in modern times, most aptly, recently put in the form of the firewall puzzle. Do black holes really indicate something sinister about their existence or do they really take the lid off our comfort with ignoring the fundamental issues, our modern theories are seemingly plagued with? In this review, we focus on issues pertaining to black hole evaporation, the development of the information loss paradox, its recent formulation, the leading debates and promising directions in the community.Comment: Published in Univers

Causal decomposition of complex systems and prediction of chaos using machine learning

We live in a complex system. Therefore, it is essential to possess techniques to analyze and comprehend its intricate dynamics in order to improve decision making. The objective of this dissertation is to contribute to the research that enhances our ability to make these complex systems less intransparent to us. Firstly, we illustrate the impact on practical applications when nonlinearity - an often disregarded factor in causal inference - is taken into account. Therefore, we investigate the causal relationships within these systems, particularly shedding light on the distinction between linear and nonlinear drivers of causality. After developing the necessary methods, we apply them to a real-world use case and demonstrate that making slight adjustments to certain financial market frameworks can result in considerable advantages because of the resolution of the correlation-causation fallacy. Subsequently, once the linear and nonlinear causal connections are understood, we can derive governing equations from the underlying causality structure to enhance the interpretability of models and predictions. By fine-tuning the parameters of these equations through the phenomenon of synchronization of chaos, we can ensure that they optimally represent the data. Nevertheless, not all complex systems can be accurately described by governing equations. Therefore, the implementation of machine learning techniques like reservoir computing in predicting chaotic systems offers significant data-driven advantages. While their architecture is relatively simple, ensuring full interpretability and hardware realizations still relies on increased efficiency and reduced data requirements. This dissertation presents some of the necessary modifications to the traditional reservoir computing architecture to bring physical reservoir computing closer to realization.Wir leben in einem komplexen System. Daher ist es unerlässlich, über Techniken zur Analyse und zum Verständnis seiner verschleierten Dynamik zu verfügen, um die Entscheidungsfindung zu verbessern. Ziel dieser Dissertation ist es, einen Beitrag zur Forschung zu leisten, die unsere Möglichkeiten erweitert, diese komplexen Systeme für uns weniger intransparent zu machen. Zunächst wird aufgezeigt, welche Auswirkungen es auf praktische Anwendungen hat, wenn Nichtlinearität - ein oft vernachlässigter Faktor bei kausaler Inferenz - berücksichtigt wird. Daher untersuchen wir die kausalen Beziehungen innerhalb dieser Systeme und beleuchten insbesondere die Unterscheidung zwischen linearen und nichtlinearen Kausalitätsfaktoren. Nachdem wir die erforderlichen Methoden entwickelt haben, wenden wir sie auf einen realen Anwendungsfall an und zeigen, dass leichte Anpassungen bestimmter Finanzmarktmodelle durch die Auflösung des Korrelations-Kausalitäts-Fehlschlusses zu erheblichen Vorteilen führen können. Sobald die linearen und nichtlinearen Kausalzusammenhänge bekannt sind, können wir aus der zugrunde liegenden Kausalitätsstruktur die Differentialgleichungen ableiten, um die Interpretierbarkeit von Modellierungen und Vorhersagen zu verbessern. Durch die Feinjustierung der Parameter dieser Gleichungen durch das Phänomen der Synchronisierung von Chaos können wir sicherstellen, dass sie die Daten optimal darstellen. Allerdings lassen sich nicht alle komplexen Systeme durch Differentialgleichungen adäquat beschreiben. Daher bietet die Anwendung von Techniken des maschinellen Lernens wie Reservoir Computing bei der Vorhersage chaotischer Systeme erhebliche datenbasierte Vorteile. Obwohl ihre Architektur relativ einfach ist, ist die Gewährleistung einer vollständigen Interpretierbarkeit und Hardware-Realisierung immer noch von einer erhöhten Effizienz und reduzierten Datenanforderungen abhängig. In dieser Dissertation werden einige der notwendigen Änderungen an der traditionellen Architektur vorgestellt, um physikalisches Reservoir Computing näher an die Realisierung zu bringen

Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability

Includes bibliographical references (pages. 379-400).This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity

Causal Decomposition of Complex Systems & Prediction of Chaos using Machine Learning

Wir leben in einem komplexen System. Daher ist es unerlässlich, über Techniken zur Analyse und zum Verständnis seiner verschleierten Dynamik zu verfugen, um die Entscheidungsfindung zu verbessern. Ziel dieser Dissertation ist es, einen Beitrag zur Forschung zu leisten, die unsere Möglichkeiten erweitert, diese komplexen Systeme für uns weniger intransparent zu machen. Zunächst wird aufgezeigt, welche Auswirkungen es auf praktische Anwendungen hat, wenn Nichtlinearität — ein oft vernachlässigter Faktor bei kausaler Inferenz — berücksichtigt wird. Daher ¨ untersuchen wir die kausalen Beziehungen innerhalb dieser Systeme und beleuchten insbesondere die Unterscheidung zwischen linearen und nichtlinearen Kausalitätsfaktoren. Nachdem wir die erforderlichen Methoden entwickelt haben, wenden wir sie auf einen realen Anwendungsfall an und zeigen, dass leichte Anpassungen bestimmter Finanzmarktmodelle durch die Auflösung des Korrelations-Kausalitäts-Fehlschlusses zu erheblichen Vorteilen führen können. Sobald die linearen und nichtlinearen Kausalzusammenhänge bekannt sind, können wir aus der zugrunde liegenden Kausalitätsstruktur die Differentialgleichungen ableiten, um die Interpretierbarkeit von Modellierungen und Vorhersagen zu verbessern. Durch die Feinjustierung der Parameter dieser Gleichungen durch das Phänomen der Synchronisierung von Chaos können wir sicherstellen, dass sie die Daten optimal darstellen. Allerdings lassen sich nicht alle komplexen Systeme durch Differentialgleichungen adäquat beschreiben. Daher bietet die Anwendung von Techniken des maschinellen Lernens wie Reservoir Computing bei der Vorhersage chaotischer Systeme erhebliche datenbasierte Vorteile. Obwohl ihre Architektur relativ einfach ist, ist die Gewährleistung einer vollständigen Interpretierbarkeit und Hardware-Realisierung immer noch von einer erhöhten Effizienz und reduzierten Datenanforderungen abhängig. In dieser Dissertation werden einige der notwendigen Änderungen an der traditionellen Architektur vorgestellt, um physikalisches Reservoir Computing näher an die Realisierung zu bringen

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

An investigation of children\u27s ideas about conservation of energy within a concept-based model.

Significant improvement in American science education is desirable from many perspectives. This need can be encapsulated in the student learning goal of flexible, inquiry-based problem solving. To guide efforts to move towards this goal, an Amalgamated Model of science education is synthesized from six major trends in science education. Explication of this model is focused on the physical sciences, as is the research carried out to test one of the predictions of the Model. One keystone of the Amalgamated Model is the delineation and characterization of concepts according to the three properties of generality, complexity, and abstractness. Using these three properties, a conceptual structure for physical science is constructed. When this structure is compared with conventional physical science curricula, a large discrepancy is in the treatment of the conservation of energy concept. The Model predicts that elementary and middle school age children develop intuitive understandings of the concept, whereas conventionally it is believed to be inherently difficult and comprehensible only to older, high school students. To test this prediction, two sets of tasks and associated clinical interviews are administered to 48 subjects randomly drawn from a K-6 population. The first set of tasks consists of the classical Piagetian conservation instances and the second set investigates understanding of conservation of energy. The quantitative data generated by the two tasks is analyzed to measure the degree of connection within subjects\u27 conceptual structures between their ability to conserve quantity, substance, weight and volume and their ability to conserve energy. The results indicated that understanding of conservation of energy is connected to and apparently develops from the understandings of Piagetian conservation. The experimental and analytical methodologies could be used for further mapping of conceptual structures. It would seem that the prediction of the Amalgamated Model cannot be rejected as false, providing some justification for using the Model as a future framework for research in conceptual structures and for science curriculum development

Subject and Aesthetic Interface - an inquiry into transformed subjectivities

The present PhD-thesis seeks new definitions of human subjectivity in an age of technoscience and a networked, globalized, Information Society. The perspective presented relates to Philosophy of Science, which includes the Human, the Natural, the Social and the Life Sciences. The project is directed at addressing, and aims to participate in, the further development of Philosophy of Science, or rather, the philosophy of knowing, which leaves a perspective broader than that of science. Methodologically, I combine readings of technoetic artworks, which I approach from a hermeneutical-semiotic perspective, with transdisciplinary research into existing theory concerning the human subject. These readings form my case studies. I keep a particular focus on holistic biophysics (Mae Wan Ho, James Oschman, Marko Bischof). Furthermore, Søren Brier's cybersemiotic theory of communication, cognition and consciousness, which combines a cybernetic-autopoietic and a Peircean semiotic perspective, plays a central role in the project. The project has three parts. Part one contextualizes the study within philosophy of science. It discusses relevant epistemologies, and places the case studies in an art categorical context. It further discusses the philosophical problems involved in writing an academic thesis in the form of a linear, argumentative, critical style, and how it affects the process of meaning making in a way that has consequences to my research. The second part consists of four case studies, each under an overall theme, which applies to the question of human subjectivity. Here I build the concept Extended Sentience, and the concept of an Ideal User. The Ideal User functions as a conceptual frame, which allows me to gradually add more elements to a theory of an altered human subject and knower. The third part presents new ontologies under three basic themes: Time and Relativity, The Life Cycles of Metaphors, and Logos Philosophy and Virtual Grids. These ontologies strongly affect ways of interpretation made in part one and two. Part Three allows more space to my subjective thought processes, which will take precedence over the literature applied. Thus, I, as a post-objective subject observer, will become more transparent. Finally, I will seek an overall conclusion to the project, which should clarify areas where it is evident that the human subject must be reconsidered at a pre-scientific level. It is my thesis that the foundation for human knowledge generation is changing drastically today, and that it has become crucial to reconsider a common understanding of what constitutes the human knower

Scientific methods: an online book

BookThis book was originally intended as ˜How to do science™, or ˜How to be a scientist™, providing guidance for the new scientist, as well as some reminders and tips for experienced researchers. Such a book does not need to be written by the most expert or most famous scientist, but by one who likes to see the rules of play laid out concisely. It does need to be written by a working scientist, not by a philosopher of science. The first half of the book, called ˜Scientist's Toolbox", retains this original focus on what Jerome Brumer called the structure of science -- its methodologies and logic. This objective is still present in the second half of the book, ˜Living Science". In researching that section, however, I was fascinated by the perspectives of fellow scientists on ˜What it is like to be a scientist." Encountering their insights into the humanity of science, I found resonance with my already intense enjoyment of the process of science. Gaither and Cavazon-Gaither [2000] provide many additional scientific quotations on the experience of science