The Structural Dimension of Cooperation. Cooperation Networks as Cohesive Small Worlds

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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

Dynamics and collective phenomena of social systems

This thesis focuses on the study of social systems through methods of theoretical physics, in particular proceedings of statistical physics and complex systems, as well as mathematical tools like game theory and complex networks. There already ex- ists predictive and analysis methods to address these problems in sociology, but the contribution of physics provides new perspectives and complementary and powerful tools. This approach is particularly useful in problems involving stochastic aspects and nonlinear dynamics. The contribution of physics to social systems provides not only prediction procedures, but new insights, especially in the study of emergent properties that arise from holistic approaches. We study social systems by introducing different agent-based models (ABM). When possible, the models are analyzed using mathematical methods of physics, in order to achieve analytical solutions. In addition to a theoretical approach, experi- mental treatment is performed via computer simulations both through Monte Carlo methods and deterministic or mixed procedures. This working method has proved very fruitful for the study of several open problems. The book is structured as follows. This introduction presents the mathematical formalisms used in the investigations, which are structured in two parts: in part I we deal with the emergence of cooperation, while in part II we analyze cultural dynamics under the perspective of tolerance

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

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

Тhe benefits of an additional practice in descriptive geometry course: non obligatory workshop at the Faculty of civil engineering in Belgrade

At the Faculty of Civil Engineering in Belgrade, in the Descriptive geometry (DG) course, non-obligatory workshops named “facultative task” are held for the three generations of freshman students with the aim to give students the opportunity to get higher final grade on the exam. The content of this workshop was a creative task, performed by a group of three students, offering free choice of a topic, i.e. the geometric structure associated with some real or imagery architectural/art-work object. After the workshops a questionnaire (composed by the professors at the course) is given to the students, in order to get their response on teaching/learning materials for the DG course and the workshop. During the workshop students performed one of the common tests for testing spatial abilities, named “paper folding". Based on the results of the questionnairethe investigation of the linkages between:students’ final achievements and spatial abilities, as well as students’ expectations of their performance on the exam, and how the students’ capacity to correctly estimate their grades were associated with expected and final grades, is provided. The goal was to give an evidence that a creative work, performed by a small group of students and self-assessment of their performances are a good way of helping students to maintain motivation and to accomplish their achievement. The final conclusion is addressed to the benefits of additional workshops employment in the course, which confirmhigherfinal scores-grades, achievement of creative results (facultative tasks) and confirmation of DG knowledge adaption

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

The contemporary visualization and modelling technologies and techniques for the design of the green roofs

The contemporary design solutions are merging the boundaries between real and virtual world. The Landscape architecture like the other interdisciplinary field stepped in a contemporary technologies area focused on that, beside the good execution of works, designer solutions has to be more realistic and “touchable”. The opportunities provided by Virtual Reality are certainly not negligible, it is common knowledge that the designs in the world are already presented in this way so the Virtual Reality increasingly used. Following the example of the application of virtual reality in landscape architecture, this paper deals with proposals for the use of virtual reality in landscape architecture so that designers, clients and users would have a virtual sense of scope e.g. rooftop garden, urban areas, parks, roads, etc. It is a programming language that creates a series of images creating a whole, so certain parts can be controlled or even modified in VR. Virtual reality today requires a specific gadget, such as Occulus, HTC Vive, Samsung Gear VR and similar. The aim of this paper is to acquire new theoretical and practical knowledge in the interdisciplinary field of virtual reality, the ability to display using virtual reality methods, and to present through a brief overview the plant species used in the design and construction of an intensive roof garden in a Mediterranean climate, the basic characteristics of roofing gardens as well as the benefits they carry. Virtual and augmented reality as technology is a very powerful tool for landscape architects, when modeling roof gardens, parks, and urban areas. One of the most popular technologies used by landscape architects is Google Tilt Brush, which enables fast modeling. The Google Tilt Brush VR app allows modeling in three-dimensional virtual space using a palette to work with the use of a three-dimensional brush. The terms of two "programmed" realities - virtual reality and augmented reality - are often confused. One thing they have in common, though, is VRML - Virtual Reality Modeling Language. In this paper are shown the ways on which this issue can be solved and by the way, get closer the term of Virtual Reality (VR), also all the opportunities which the Virtual reality offered us. As well, in this paper are shown the conditions of Mediterranean climate, the conceptual solution and the plant species which will be used by execution of intensive green roof on the motel “Marković”