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© 2013

Decoding Complexity

Uncovering Patterns in Economic Networks

Book

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. James B. Glattfelder
    Pages 1-21
  3. James B. Glattfelder
    Pages 95-119
  4. James B. Glattfelder
    Pages 121-148
  5. James B. Glattfelder
    Pages 149-166
  6. Back Matter
    Pages 167-225

About this book

Introduction

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.

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

Authors and affiliations

  1. 1.Olsen Ltd.Swiss Federal Institute of TechnologyZurichSwitzerland

Bibliographic information

Reviews

From the reviews:

“Grattfelder’s book develops a set of analytical and numerical tools which investigate the economic system as a large scale network. … The reader searching for a methodological reference and new ideas on how to study economic networks will find in this book an invaluable source of information. … of paramount interest for anyone concerned with social applications of complex networks and, in particular, for social simulators who want to base their models on new network measures and new stylized facts to better understand socioeconomic phenomena.” (Simone Righi, Journal of Artificial Societies and Social Simulation, 2012)