Decoding Complexity Uncovering Patterns in Economic Networks
James B. Glattfelder
While globalization seems to have been the buzzword since the late twentieth century, scientiﬁc insight into the causes and effects, and the beneﬁts and drawbacks of globalization still needs to be fully developed. For an understanding of how globalization works, massive data analysis of economic agents worldwide is an inevitable step. What does the structure of global economic interactions look like? Does it resemble structures found on smaller economic scales, or are there emerging properties on the global level? Do our ﬁndings conform to the established economic theories, or must these theories be reﬁned and further developed to cope with these ﬁndings? Can we derive models to predict the dynamics in such global networks?
The thesis of James Glattfelder addresses the heart of these scientiﬁc questions. Based on the theory of complex networks, it derives a novel approach to investigate large-scale economic networks based on massive analysis of ownership data. It further links these ﬁndings to relevant economic questions, such as the measurement of control. For this, James proposes novel ways to quantify economic concepts like ‘‘value’’, ‘‘control’’, ‘‘power’’, and provides computational algorithms to extract these from weighted, directed network structures hidden in largescale datasets.
A particular highlight of the thesis is the unfolding of a global ownership network of transnational companies (TNC), including their shareholders and their subsidiaries. This analysis identiﬁed a rather small, but fully connected core of the network which contains less than 1 % of all nodes, but almost 20 % of the operating revenue of all TNC. This ‘‘core’’ is mostly dominated by ﬁnancial intermediaries based in the US and UK. The existence of such a structure is an important clue toward understanding systemic risk in economic systems, in particular if seen together with the very strong concentration of control that James has identiﬁed. The ‘‘emerging picture’’ reveals the role of a ‘‘super-entity’’ in the global network of TNC, a ﬁnding that goes beyond our current knowledge about globalization. The insights obtained in this work even challenge established economic theories, because they are hardly predicted or explained by these.
The content of the thesis is highly original and makes a fundamental contribution to our knowledge of global economic networks. Moreover, it is one of the rare cases where scientiﬁc results have made it to the front page of major newspapers and magazines around the world. It thereby fuelled the discussion on inequality, the role of ﬁnancial institutions, and the control of economic power. I am pleased that the publication of this thesis makes such important ﬁndings available to a larger audience.
WHEN dozens of countries refused to sign a new global treaty on internet governance in late 2012, a wide range of activists rejoiced. They saw the treaty, crafted under the auspices of the International Telecommunication Union (ITU), as giving governments pernicious powers to meddle with and censor the internet. For months groups with names like Access Now and Fight for the Future had campaigned against the treaty. Their lobbying was sometimes hyperbolic. But it was also part of the reason the treaty was rejected by many countries, including America, and thus in effect rendered void.
The success at the ITU conference in Dubai capped a big year for online activists. In January they helped defeat Hollywood-sponsored anti-piracy legislation, best known by the acronym SOPA, in America’s Congress. A month later, in Europe, they took on ACTA, an obscure international treaty which, in seeking to enforce intellectual-property rights, paid little heed to free speech and privacy. In Brazil they got closer than many would have believed possible to securing a ground-breaking internet bill of rights, the “Marco Civil da Internet”. In Pakistan they helped to delay, perhaps permanently, plans for a national firewall, and in the Philippines they campaigned against a cybercrime law the Supreme Court later put on hold.
Each chapter in Managing Complexity Focuses on analyzing real-world complex systems and transfer knowledge ring from the complex-systems sciences to applications in business, industry and society. The interdisciplinary contributions range from markets and production through logistics, traffic control, and critical infrastructures, up to network design, information systems, social conflicts and building consensus. They serve to raise readers' awareness concerning the often counter-intuitive behavior of complex systems and to help them integrate insights gained in complexity research into everyday planning, decision making, strategic optimization, and policy.
Intended for a broad readership, the contributions have been kept Largely non-technical and address a general, scientifically literate audience involved in corporate, academic, and public institutions.
Table of Contents
Editorial - Managing Complexity: An Introduction - Market Segmentation - The Network Approach - Managing Autonomy and Control in Economic Systems - Complexity and the Enterprise:..... Illusion of Control - Benefits and Drawbacks of Simple Models for Complex Production Systems - . Logistics Networks - Coping With Nonlinearity and Complexity - Repeated Auction Games and Learning Dynamics in Electronic Logistics Marketplaces - Decentralized Approaches to Adaptive Traffic Control - Critical Infrastructures Vulnerability:.. The Highway Networks - Trade Credit Networks and Systemic Risk - A Complex System's.. View of Critical Infrastructures -. Bootstrapping the Long Tail in Peer to Peer Systems -. Coping With Information Overload Through Trust-Based Networks -. Complexity in Human Conflict -. Fostering Consensus in Multidimensional Continuous Opinion Dynamics Under Bounded Confidence -. multi-stakeholder governance - Emergence and transformational potential of a New Political Paradigm -. Evolutionary Engineering of Complex Functional Networks -. Path Length Scaling and Discrete Effects in Complex Networks -. index.
FuturICT is a visionary project that will deliver new science and technology to explore, understand and manage our connected world. This will inspire new information and communication technologies (ICT) that are socially adaptive and socially interactive, supporting collective awareness. Revealing the hidden laws and processes underlying our complex, global, socially interactive systems constitutes one of the most pressing scientific challenges of the 21st Century. Integrating complexity science with ICT and the social sciences, will allow us to design novel robust, trustworthy and adaptive technologies based on socially inspired paradigms. Data from a variety of sources will help us to develop models of techno-socioeconomic systems. In turn, insights from these models will inspire a new generation of socially adaptive, self-organised ICT systems. This will create a paradigm shift and facilitate a symbiotic co-evolution of ICT and society. In response to the European Commission’s call for a ‘Big Science’ project, FuturICT will build a largescale, pan European, integrated programme of research which will extend for 10 years and beyond.
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often Fundamentally entangled.
Recent research has shown that search networks can exhibit a plethora of new phenomena Which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor". # In which at Initially homogenous population of network nodes self-Organizes into functionally distinct classes These are just a few.
This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
Related subjects » Applications - Complexity - Database Management & Information Retrieval
TABLE OF CONTENTS Preface - Adaptive Networks - Part I:... Real-World Examples of Adaptive Networks - Part II: Self-Organization of Adaptive Networks - Part III:.. Contact Processes and Epidemiology on Adaptive Networks - Part IV: Social Games on Adaptive Networks . - Part V: graph rewriting-based approaches.
The present volume collects notes from lectures delivered for the CIME course on Modelling and optimisation of flows on networks, held in Cetraro in the summer of 2009. In recent years modelling of flows on networks has been the subject of many investigations leading to an increasing number of research papers.Moreover, a wide set of possible applications, such as vehicular traffic, blood flow, supply chains and others, has directed the attention of mathematicians towards research domains usually populated by engineers, physicists or researchers with other expertise. The aim of the CIME school was to gather summer courses which could give a wide view of modelling, analysis, numerics and control for dynamic flows on networks. Encompassing all application domains (including irrigation channels, data networks, air traffic management and others) was impossible, thus we focused on mathematical approaches, which are feasible for a number of applications, and a restricted set of specific applications, in particular vehicular traffic and supply chains. The attempt of finding a common ground, for different mathematical techniques to treat flows on networks, was already successful in a number of cases both at the level of research projects (such as the Italian national INDAM project 2005) and editorial initiatives (the foundation in 2006 of a new applied math journal entitled Networks and Heterogeneous Media). The school took place in Cetraro, Italy, on June 15–19 2009. The course subjects were the following: 1. Introduction to conservation laws: Alberto Bressan (PennState) 2. Optimal transportation: Luigi Ambrosio (SNS, Pisa) 3. Pedestrian motions and vehicular traffic: Dirk Helbing (ETH) 4. Control and stabilization of waves on 1-D networks: Enrique Zuazua (BCAM) 5. Modelling and optimization of scalar flows on networks: Axel Klar (Kaiserlautern) 6. Fluid dynamic and kinetic models for supply chains: Christian Ringhofer (Arizona State) vii
Our social and economic worlds have been revolutionised. The Internet has transformed communications and for the first time in human history, more than half of us live in cities. We are increasingly aware of the choices, decisions, behaviours and opinions of other people. Network effects - the fact that a person can and often does decide to change his or her behaviour simply on the basis of copying what others do - pervade the modern world.
The financial crisis has shown us that conventional economics is drastically limited by its failure to comprehend networks. Paul Ormerod, argues that as our societies become ever more dynamic and intertwined, network effects on every level are increasingly profound. 'Nudge theory' is popular, but only part of the answer. To grapple successfully with the current financial crisis, businesses and politicians need to grasp the perils and possibilities of 'positive linking'.
As Ormerod shows, network effects make conventional approaches to policy, whether in the public or corporate sectors, much more likely to fail. But they open up the possibility of truly 'positive linking' - of more subtle, effective and successful policies, ones which harness our knowledge of network effects and how they work in practice.
Understanding Complex Systems Series Editors: Abarbanel, H., Braha, D., Érdi, P., Friston, K., Haken, H., Jirsa, V., Kacprzyk, J., Kaneko, K., Kirkilionis, M., Kurths, J., Nowak, A., Reichl, L., Schuster, P., Schweitzer, F., Sornette, D., Thurner, S. Founded by: Kelso, Scott
Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition (typically many different kinds of components interacting with each other and their environments on multiple levels) and in the rich diversity of behavior of which they are capable. The Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of self-organizing dynamical systems at all levels of description and in all scientific fields, especially newly emerging areas within the Life, Social, Behavioral, Economic, Neuro- and the Cognitive Sciences (and derivatives thereof): Second, to encourage novel applications of these ideas in various fields of Engineering and Computation such as Robotics, Nanotechnology and Informatics: Third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs and selected edited contributions from specialized conferences and workshops aimed at communicating new findings to a large transdisciplinary audience.
The 21st century is currently witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.
Editors-in-Chief Frank Schweitzer, ETH Zürich Alessandro Vespignani, Northeastern University
The power of network science, the beauty of network visualization.
The book is the result of a collaboration between a number of individuals, shaping everything, from content (Laszlo Barabasi) to visualizations and interactive tools (Mauro Martino), simulations and data analysis (Márton Pósfai).
FuturICT: Global Computing for Our Complex World We have built particle accelerators to understand the forces that make up our physical world. Yet, we do not understand the principles underlying our strongly connected, techno-socio-economic systems. We have enabled ubiquitous Internet connectivity and instant, global information access. Yet we do not understand how it impacts our behavior and the evolution of society. To fill the knowledge gaps and keep up with the fast pace at which our world is changing, a Knowledge Accelerator must urgently be created. For this, the FuturICT flagship project will promote an interdisciplinary integration of natural, social, and engineering sciences with novel paradigms of information technology. This will produce the synergy effects required to address many of our 21st century challenges. After the age of physical, biological and technological innovations, FuturICT will lead Europe into the next era – a wave of information-driven social and socio-inspired innovations. Globalisation and technological change have made our world a different place. This has created or intensified a number of serious problems, such as global financial and economic crises, political instabilities and revolutions, the quick spreading of diseases, disruptions of international supply chains, organised crime, international conflict and world-wide terrorism, and increased cyber-risks as well. Although the creation of more and more interconnected systems and the pace of innovation in the area of information and communication technologies (ICT) have contributed to the above problems, future ICT can also be key to the solution. It can create unprecedented benefits for our economy and society, based on a whole range of new methods and innovations. For this, ICT must acquire the ability to flexibly adapt to the needs of society. In this way, it can become a stabilising factor fostering transparency, trust, respect for individual rights, and opportunities for participation in social, economic, and political processes. This requires us to establish a new science of multi-level complex, global systems and a co-evolution of ICT with society, by bringing the best knowledge of experts on information and communication systems, complex systems and the social sciences together. The vision of the FuturICT flagship project is to develop the capacity to explore and manage our future, based on a fundamental understanding of the institutional and interaction-based principles that make connected systems work well. The methods and ‘Big Data’ needed for such a scientific endeavour are now becoming available: it is, therefore, time to make a ‘Big Science’ effort to couple methods and data with theories and models, like in the Human Genome Project. This endeavour should be open, because we need to prevent private monopolies of socio-economic data, and it should be federated, because joint interdisciplinary efforts are the only way to tackle humanity’s global challenges and ensure leadership in socio-inspired ICT innovations. The investments into the FuturICT project can benefit citizens and society in many ways: by promoting collective awareness of the impacts of our decisions and actions, by mitigating global problems and systemic risks, and by creating new possibilities to participate in social, economic and political affairs. In particular, FuturICT will create the basis for new spin-offs, business opportunities and jobs.
Generally, spontaneous pattern formation phenomena are random and repetitive, Whereas elaborate devices are the deterministic product of human design. Yet, biological organisms and collective insect constructions are exceptional examples of complex systems that are both self-organized and architectural. This book is the first initiative of its kind toward Establishing a new field of research, Morphogenetic Engineering, to explore the modeling and implementation of "self-architecturing" system. Particular emphasis is placed on the programmability and computational abilities of self-organization, properties that are often underappreciated in complex systems science, while conversely, the benefits of self-organization are often underappreciated in engineering methodologies. Altogether, the aim of this work is to Ooops a framework for and examples of a larger class of "self-architecturing" system, while addressing fundamental questions examined as > How do biological organisms carry out morphogenetic tasks Sun reliably? > Can we extrapolate their self-formation capabilities to engineered systems? > Can physical systems be endowed with information (or informational system be embedded in physics) so as to create autonomous Morphologies and functions? > What are the core principles and best practices for the design and engineering of search morphogenetic system?
Combines an overview of swarm intelligence with an up-to-date treatment of the advances in the field
Of interest to novices and experienced researchers in the field The laws that govern the collective behavior of social insects, flocks of birds, or fish schools continue to mesmerize researchers. While individuals are rather unsophisticated, in cooperation they can solve complex tasks, a prime example being the ability of ant colonies to find shortest paths between their nests and food sources. Task-solving results from self-organization, which often evolves from simple means of communication, either directly or indirectly via changing the environment, the latter referred to as stigmergy. Scientists have applied these principles in new approaches, for example to optimization and the control of robots. Characteristics of the resulting systems include robustness and flexibility. This field of research is now referred to as swarm intelligence.
The contributing authors are among the top researchers in their domain. The book is intended to provide an overview of swarm intelligence to novices, and to offer researchers in the field an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research such as the evolution of robot behavior, the use of particle swarms for dynamic optimization, and organic computing.
Content Level » Research
Keywords » Artificial intelligence - Distributed systems - Operations research - Optimization - Robotics
Related subjects » Applications - Artificial Intelligence - Robotics - Signals & Communication - Theoretical Computer Science
Encounters Between Complexity Theory and Information Sciences Series: Understanding Complex Systems
Scharnhorst, Andrea; Börner, Katy; Besselaar, Peter van den (Eds.)
Models of science dynamics aim to capture the structure and evolution of science. They are developed in an emerging research area in which scholars, scientific institutions and scientific communications become themselves basic objects of research. In order to understand phenomena as diverse as the structure of evolving co-authorship networks or citation diffusion patterns, different models have been developed. They include conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, and computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive research agenda.
This book aims to fill this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented here cover stochastic and statistical models, game-theoretic approaches, agent-based simulations, population-dynamics models, and complex network models. The book starts with a foundational chapter that defines and operationalizes terminology used in the study of science, and a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of future challenges for science modeling and discusses their relevance for science policy and science policy studies. Content Level » Research
Agent-based modeling is a vital technique for studying Complex Adaptive Systems (CAS) as evidenced by the growing body of literature spanning disciplines ranging from Biology to the Social Sciences to Computer Science. This inaugural special issue of Springer Complex Adaptive Systems Modeling (CASM) will publish key papers documenting multidisciplinary methods and applications for agent-based modeling of CAS. Agent-based modeling has become essential for the study of CAS. Many documented techniques and proven applications have emerged. This inaugural Special Issue will help consolidate these diverse ideas in a single volume and also help to introduce CASM to relevant communities.
Nominated as an outstanding contribution by the ETH Zurich Presents powerful new methods for understanding economic and corporate networks Written in a lucid and accessible style Will appeal to readers from many disciplines that involve complex networks Today it appears that we understand more about the universe than about our interconnected socio-economic world. In order to uncover organizational structures and novel features in these systems, we present the first comprehensive complex systems analysis of real-world ownership networks. This effort lies at the interface between the realms of economics and the emerging field loosely referred to as complexity science. The structure of global economic power is reflected in the network of ownership ties of companies and the analysis of such ownership networks has possible implications for market competition and financial stability. Thus this work presents powerful new tools for the study of economic and corporate networks that are only just beginning to attract the attention of scholars.
Content Level » Research
Keywords » Complex Networks of Ownership - Complex Ownership Networks - Complex Systems in Economics - Corporate Control - Corporate Ownership - Economic Networks -Empirical Network Analysis - Financial Stability Analysis - Ownership and Control
Related subjects » Applications - Complexity - Economic Theory
TABLE OF CONTENTS
The Main Methodology: Computing Control in Ownership Networks.- Backbone of Complex Networks of Corporations: The Flow of Control.- The Network of Global Corporate Control.- The Bow-Tie Model of Ownership Networks.