One of the major problems of macroeconomic theory is the way in which the people exchange goods in decentralized market economies. There are major disagreements among macroeconomists regarding tools to influence required outcomes. Since the mainstream efficient market theory fails to provide an internal coherent framework, there is a need for an alternative theory. The book provides an innovative approach for the analysis of agent based models, populated by the heterogeneous and interacting agents in the field of financial fragility. The text is divided in two parts; the first presents analytical developments of stochastic aggregation and macro-dynamics inference methods. The second part introduces macroeconomic models of financial fragility for complex systems populated by heterogeneous and interacting agents. The concepts of financial fragility and macroeconomic dynamics are explained in detail in separate chapters. The statistical physics approach is applied to explain theories of macroeconomic modelling and inference.
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This book describes the analysis of macroeconomic agent based models using the tools of statistical mechanics.
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xiii | |
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xv | |
| Preface |
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xvii | |
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1 | (6) |
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1.2 Aggregation and Interaction |
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7 | (5) |
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12 | (9) |
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1.4 Structure of the Book |
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21 | (8) |
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1.4.1 Three possible reading paths |
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22 | (7) |
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Part I Methodological Notes and Tools |
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29 | (2) |
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2.2 The State Space Notion |
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31 | (15) |
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46 | (3) |
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3.2 The Master Equation: A General Introduction |
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49 | (6) |
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3.2.1 The mechanics inside |
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49 | (4) |
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3.2.2 On the meaning of the ME |
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53 | (2) |
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3.3 The Markov Hypothesis |
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55 | (7) |
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55 | (2) |
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57 | (3) |
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3.3.3 Stationary solution |
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60 | (2) |
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62 | (5) |
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62 | (2) |
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3.4.2 Exact dynamic estimators |
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64 | (2) |
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3.4.3 Mean-field dynamic estimators |
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66 | (1) |
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67 | (4) |
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Part II Applications to HI A Based Models |
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A Premise Before Applications |
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71 | (2) |
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4 Financial Fragility and Macroeconomic Dynamics I: Heterogeneity and Interaction |
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73 | (2) |
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4.2 A Financial Fragility ABM |
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75 | (22) |
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76 | (2) |
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78 | (2) |
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4.2.3 States of financial soundness |
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80 | (1) |
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4.2.4 The microeconomic behaviuor |
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81 | (3) |
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4.2.5 The optimal programing rule |
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84 | (4) |
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4.2.6 Macroeconomic effects and parametrization |
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88 | (9) |
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4.3 Macroeconomic Inference of Stochastic Dynamics |
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97 | (11) |
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4.3.1 The ME applied to the ABM |
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97 | (4) |
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4.3.2 The ME transition rates from the ABM |
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101 | (4) |
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4.3.3 The ME solution to the ABM |
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105 | (3) |
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4.4 Results of Monte Carlo Simulations |
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108 | (23) |
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4.4.1 The simulation procedure |
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108 | (3) |
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4.4.2 Economic scenarios and inference |
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111 | (20) |
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131 | (2) |
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5 Financial Fragility and Macroeconomic Dynamics II: Learning |
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133 | (3) |
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5.2 A Financial Fragility ABM |
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136 | (15) |
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137 | (2) |
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139 | (3) |
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5.2.3 The microeconomic behaviour |
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142 | (9) |
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5.3 Macroeconomic Inference of Stochastic Dynamics |
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151 | (10) |
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5.3.1 The ME applied to the ABM |
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153 | (2) |
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5.3.2 The ME transition rates from the ABM |
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155 | (6) |
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5.3.3 The ME solution to the ABM |
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161 | (1) |
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5.4 Monte Carlo Scenarios and Simulation Results |
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161 | (29) |
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5.4.1 The simulation procedure |
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161 | (2) |
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5.4.2 Economic scenarios and inference |
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163 | (27) |
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190 | (5) |
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6.1 The Relevance of this Book |
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195 | (4) |
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6.2 Current Work and Possible Future Developments |
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199 | (6) |
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200 | (1) |
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6.2.2 Towards a comprehensive representation of the economy |
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201 | (4) |
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Part IV Appendices and Complements |
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Appendix A Complements to
Chapter 3 |
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205 | (6) |
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Appendix B Solving the ME to Solve the ABM |
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211 | (31) |
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Appendix C Specifying Transition Rates |
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242 | (31) |
| References |
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273 | (10) |
| Index |
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283 | |
Corrado Di Guilmi earned his Ph.D. in Economics from Università Politecnica delle Marche, Ancona, Italy in 2008. He is currently working as Senior Lecturer in the Economics Discipline Group of the University of Technology, Sydney. He is associate editor of the Review of Keynesian Economics and of Complexity Economics. He was visiting fellow at the Department of Economics of the University of Cambridge, the Department of Applied Mathematics of the Australia National University, the New School for Social Research in New York. His research interests include business cycle, agent-based modelling, nonlinear modelling applications in economics of complex system theory and Post-Keynesian economics. Mauro Gallegati is Professor of Advanced Macroeconomics at the Università Politecnica delle Marche, Italy. He obtained his Ph.D. in Economics from the Università degli Studi di Ancona, Italy in 1989. He has been visiting professor in several universities including Stanford University, Columbia University and Massachusetts Institute of Technology. His research includes business fluctuations, econophysics, nonlinear dynamics, models of financial fragility and heterogeneous interacting agents. He published papers in economic, history of economy and history of economic analysis, nonlinear math, applied economics and econophysics. Simone Landini earned his Doctoral Degree in Mathematics from the University of Milano Bicocca, Italy in 2006. He is a researcher at the Istituto di Ricerche Economico Sociali del Piemonte (IRES Piemonte), Turin, Italy. He has been visiting Fellow at the Faculty of Business at the University of Technology, Sydney. His research interests include macroeconomics, dynamics of industrial and banking sectors, the financial fragility of economic systems, income and wealth distribution, financial markets analysis and microstructure, agent based modelling and their analytical solution, statistical physics and econometric applications to social and regional sciences.
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