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More About This Textbook
Overview
Econophysics is an emerging interdisciplinary field that takes advantage of the concepts and methods of statistical physics to analyze economic phenomena. This book expands the explanatory scope of econophysics to the real economy by using methods from statistical physics to analyze the success and failure of companies using large data sets of companies and income-earners in Japan and Europe, a distinguished team of researchers show how these methods allow us to analyze companies, from huge corporations to small firms, as heterogeneous agents interacting at multiple layers of complex networks. They then show how successful this approach is in explaining a wide range of recent findings relating to the dynamics of companies. With mathematics kept to a minimum, the book is not only a lively introduction to the field of econophysics but also provides fresh insights into company behaviour.
Product Details
Meet the Author
Hideaki Aoyama is Professor of Physics at Kyoto University, Japan.
Yoshi Fujiwara is Research Fellow at Advanced Telecommunication Research Institute International (ATR).
Yuichi Ikeda is Senior Researcher in Hitachi Ltd, Hitachi Research Laboratory, Japan.
Hiroshi Iyetomi is Professor of Physics at Niigata University, Japan.
Wataru Souma is Associate Professor of Physics at Nihon University, Japan.
Table of Contents
List of figures
List of tables
About the authors
Foreword
Preface
Prologue
1 New insights 1
1.1 A scientific approach 1
1.1.1 Science of complex systems 3
1.1.2 The emergence of econophysics 3
1.2 Distributions and fluctuations 4
1.3 Are networks complex? 6
1.4 Change in the environment surrounding companies 7
1.4.1 Outline of the Japanese electrical and electronics and automobile industries 8
1.4.2 The electrical and electronics industry 9
1.4.3 The automobile industry 10
1.4.4 Industrial structures and business networks 11
2 Size distribution 14
2.1 Preliminaries 14
2.1.1 Flows and stocks 14
2.1.2 Size distribution and Pareto's law 15
2.1.3 Other distributions with a long tail 21
2.2 Distribution of personal income 21
2.2.1 Income distribution and Pareto's law 22
2.3 Distribution of companies 26
2.3.1 Size distribution of companies 26
2.3.2 Size of European companies 28
2.3.3 A caveat: sample and true distributions 30
2.4 Pareto's law 33
2.4.1 Gini and Robin Hood 35
2.4.2 Simulation: the inverse-function method 39
2.4.3 Devil's Staircase 40
2.4.4 Oligopoly and monopoly 42
2.4.5 Pareto's 80-20 rule 46
2.4.6 The fractal dimension 51
2.5 Side story: 'long-tail phenomena' 54
2.6 ? = 1 and phase transition 56
3 Company growth as fluctuations 59
3.1 Gibrat's law and detailed balance 60
3.1.1 Growth-rate and Gibrat's law 61
3.1.2 Data for Japanese companies 64
3.1.3 'Data for European companies 65
3.1.4 Gibrat revisited 67
3.1.5 Detailed balance 69
3.1.6 Relation between Pareto's and Gibrat's laws and the detailed balance 72
3.1.7 Copulas 74
3.2 Digression: personal income fluctuation 78
3.2.1 Gibrat's law and detailed balance 78
3.2.2 Breakdown of the laws 80
3.2.3 Side story: public notice of high-tax payers, and lost data in Japan 81
3.3 Small and medium-sized companies 83
3.3.1 Large-scale data for small and medium-sized enterprises 83
3.3.2 Size dependence of growth 84
3.4 Companies' bankruptcy 87
3.4.1 Companies' activity and bankruptcy 87
3.4.2 Lifetime and debt at bankruptcy 88
3.5 The production function and ridge theory 91
3.5.1 The production function 91
3.5.2 Ridge theory for companies' growth 92
4 Complex business networks 99
4.1 Introduction to network science 99
4.2 1, 2, 3,..., 6 degrees of separation 106
4.3 Networks in the economy 111
4.3.1 The shareholding network 113
4.3.2 The interlocking directors' network 115
4.3.3 The transaction network 116
4.3.4 The innovation network 118
4.4 Network indices 121
4.4.1 Degree centrality 122
4.4.2 Shortest path length 123
4.4.3 Clustering coefficient 123
4.4.4 The betweenness centrality of nodes 125
4.4.5 Cliques 125
4.5 Statistical properties of network indices 126
4.5.1 Comparison of industries by using network indices 126
4.5.2 Degree distribution 128
4.5.3 Correlations related to degree 131
4.5.4 The shareholding network and company size 133
4.6 Dynamics of the company network 136
4.6.1 Change in the shareholding network 136
4.6.2 Change of degree distribution 139
4.6.3 Correlation between companies in networks 143
5 An agent-based model for companies 152
5.1 Gibrat's process 152
5.2 Model of the shareholding network 154
5.2.1 Reproduction of size distribution 155
5.2.2 Reproduction of degree distribution 156
5.2.3 Effects of nodal characteristics 157
5.3 Balance sheet dynamics 158
5.3.1 The basic agent model 159
5.3.2 Representative agents 163
5.3.3 Reduction to a multiplicative process 164
5.3.4 Distribution of company sizes 165
5.3.5 Synchronised bankruptcy 168
5.4 Network effects on wealth distribution 169
5.4.1 Model construction 170
5.4.2 Network effects 170
5.4.3 Clustering of wealth 172
5.5 Modelling the transaction network 175
5.5.1 Autonomous companies 175
5.5.2 Model of bounded rationality 180
6 Perspectives for practical applications 184
6.1 Development of business strategies 184
6.1.1 Valuation of companies 184
6.1.2 Optimum capital structure 188
6.1.3 Decision-making for business entry and exit 190
6.1.4 Decision-making under a given economic trend 193
6.2 Chain bankruptcy and credit risk 196
6.2.1 Transaction network 196
6.2.2 The relationship of debtors and creditors 198
6.2.3 The causes of bankruptcy and the link effect 199
6.2.4 Magnitude of link effect 200
6.2.5 The ripple effect 201
6.2.6 Propagation of credit risk on the transaction network 206
6.3 Business model and business information 209
6.3.1 The industrial group as a business model 209
6.3.2 Robustness of industrial groups 214
6.3.3 Synergy in industrial groups 215
6.3.4 Business information systems 216
Epilogue 221
References 224
Index 230