Agent-Based Methods in Economics and Finance: Simulations in Swarm

323 Pages·2002·10.653 MB·English

by  Charlotte Bruun (auth.), Francesco Luna, Alessandro Perrone (eds.)

AGENT -BASED METHODS IN
ECONOMICS AND FINANCE:
Simulations in Swarm
Advances in Computational Economics
VOLUME 17
SERIES EDITORS
Hans Amman, Eindhoven University of Technology, Amsterdam, TheNetherlands
Anna Nagumey, University ofM assachusetts at Amherst, USA
EDITORIAL BOARD
Anantha K. Duraiappah, European University Institute
John Geweke, University ofM innesota
Manfred Gilli, University of Geneva
Kenneth L. Judd, Stanford University
David Kendrick, University of Texas at Austin
Daniel McFadden, University ofC alifornia at Berkeley
Ellen McGrattan, Duke University
Reinhard Neck, University ofK lagenfurt
Adrian R. Pagan, Australian National University
John Rust, University of Wisconsin
Berc Rustem, University ofL ondon
Hal R. Varian, University ofM ichigan
The titles published in this series are listed at the end oft his volume.
Agent-Based Methods in
Economics and Finance:
Simulations in Swarm
edited by
Francesco Luna
International Monetary Fund
EUl Eastern Division
and
Alessandro Perrone
Universita di Venzia Ca'Foscari
Venice, Italy
...., ..
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data
Agent-based methods in econom ies and finance: simulations in Swarm / edited by
Francesco Luna and Alessandro Perrone.
p.cm--(Advances in computational economics; v. 17)
IncIudes bibliographical references and index.
ISBN 978-1-4613-5238-9 ISBN 978-1-4615-0785-7 (eBook)
DOI 10.1007/978-1-4615-0785-7
1. Economics--Computer simulation. 2. Economics--Mathematical models. 1. Luna,
Francesco, 1963-II. Perrone, Alessandro. III. Ser ies
HBI43.5.A442001
330' .01' 13--dc21 2001038822
Copyright <r> 2002 by Springer Science+Business Media New York
Origina11y published by Kluwer Academic Publishers in 2002
Softcover reprint ofthe hardcover Ist edition 2002
AII rights reserved. No part of this publication may be reproduced, stored in a retrieval
system OI' transmitted in any form OI' by any means, mechanical, photo-copymg, recording,
or otherwise, without the prior written permission of the publisher, Springer Science+
Business Media, LLC.
Printed an acid-Iree paper.
Contents
List of Figures IX
Preface XVll
Contributing Authors XXI
Introduction XXVll
The Editors
1.1 Evolution and continuity in a competitive environment xxvii
1.2 Acknowledgments XXXI
Part I a modest proposal ...
1
Prospect for an Economics Framework for Swarm 3
Charlotte Bruun
1.1 Introduction 3
1.2 Swarm and the Tower of Babel 4
1.3 What's in a Framework? 12
1.4 Collecting Building Blocks for a Framework for Economics 15
1.5 An Economics Framework 23
1.6 Testing our virtual framework 26
1.7 Conclusion 31
Part II new tools
2
Automated Trading Experiments with MAML 39
Laszl6 Gulyas and Tibor Vincze
2.1 Introduction 39
2.2 Agents that Negotiate 40
2.3 The Multi-Agent Modeling Language 44
2.4 Preliminary Results 55
2.5 Summary 58
VI ECONOMIC AND FINANCIAL SIMULATIONS IN SWARM
3
VSB -Visual Swarm Builder -A Visual tool for Swarm Agent Based 63
Environment
Alessandro Perrone and Marco Tenuti
3.1 Introduction 64
3.2 The Visual Swarm Builder - A Short Overview 66
3.3 Working with VSB 67
3.4 Tutorial - Examples with VSB 76
3.5 The goal of using a tool like VSB 84
3.6 Future Works 84
4
SWIEE - a Swarm Web Interface for Experimental Economics 87
Riccardo Boero
4.1 Introduction 88
4.2 SWIEE Basics 89
4.3 The Prisoner's Dilemma 95
4.4 Making Experiments on the Internet 103
4.5 Future Developments 105
Part III financial applications
5
Contagion of Financial Crises un- der Local and Global Networks 111
Alessandra Cassar and Nigel Duffy
5.1 Introduction 112
5.2 The Bank Avalanche Model 114
5.3 Financial Crises on Networks 118
5.4 Model Implementation in Swarm 121
5.5 Results 122
5.6 Conclusions 129
6
Simulating Fractal Financial Markets 133
Marco CORAZZA and Alessandro PERRONE
6.1 Introduction and motivations 134
6.2 Fractal probability laws for financial asset returns 137
6.3 Basics for a possible fractal financial economics 140
6.4 The market dynamical model 143
6.5 The agent-based approach 146
6.6 Concluding remarks and open questions 152
7
Growing Theories from the "Bottom Up". A Simple Entry-Exit 157
Model
Domenico Delli Gatti, Mauro Gallegati and Roberto Leombruni
7.1 Introduction 157
7.2 The base model 159
7.3 Simulations 170
7.4 Conclusions 183
8
Cognitive Agents Behaving in a Simple Stock Market Structure 187
Contents Vll
Pietro Terna
8.1 Introduction 188
8.2 "Minded" or "no minded" agents in our models 190
8.3 The artificial experiment environment 191
8.4 Basic runs: random agents only 195
8.5 Introducing other types of agents 198
8.6 Agents applying ANN estimates of future prices 203
8.7 A mixed basic case, with stop loss agents and agents applying
ANN estimates 207
8.8 The structure of the CT cognitive agents 207
8.9 The behavior of the CT cognitive agents 219
8.10 Conclusions 224
Part IV other contributions
9
Production Partnerships Formation with Heterogeneous Agents: a 231
Simulation in SWARM
Davide Fiaschi, Nicolas Garrido and Pier Mario Pacini
9.1 Introduction 231
9.2 The model 234
9.3 Simulation framework 238
9.4 SWARM implementation 240
9.5 Numerical experiments 243
9.6 Conclusions 256
Appendix: Genetic Algorithms 258
A.1 Encoding 259
A.2 Selection 259
A.3 Cross-over 259
A.4 Mutation 259
A.5 Setting of the parameters in the simulations 260
10
CasinoWorld:An Agent-based Model with Heterogeneous Risk 263
Preferences and Adaptive Behavior
Michael Harrington and Darold Higa
10.1 Introduction 264
10.2 Standard Economic Models of Human Behavior 265
10.3 A More Refined Model of Human Behavior 268
10.4 Casino World - The Basic Model 271
10.5 The Play 272
10.6 Adaptive Strategies 273
10.7 Results 274
10.8 Future Developments 277
11
Search in Artificial Labour Markets: a Simulation Study 283
Massimo Daniele Sapienza and Magda Fontana
11.1 Introduction 283
11.2 An overview of the literature: building blocks 285
11.3 An artificial labour market: Labour Sim 286
11.4 The model 286
11.5 The Benchmark 288
viii ECONOMIC AND FINANCIAL SIMULATIONS IN SWARM
11.6 The Labour Net as an artificial labour market 292
11.7 Simulations 294
11.8 Learning and skill mismatch effects 301
11.9 Concluding Remarks 303
Index 305
List of Figures
1.1 Component reuse and structural reuse: (a) compo-
nent reuse, (b) intentional structural reuse, and (c)
extensional structural reuse. This figure is from Er-
dogmus and Tanir(1999). 13
1.2 Elements of Unified Modeling Language (UML). 15
1.3 The structure of ASM 18
1.4 A possible implementation of the ERA scheme 20
1.5 Suggestion for an economics framework 24
2.1 The outlook of a MAML simulation is the same as
ordinary Swarm models 56
2.2 Convergence to the equilibrium. 57
2.3 Shift in the equilibrium price. 57
2.4 Soft time pressure 58
2.5 Hard time pressure 58
2.6 The effect of newcomers 59
3.1 First Overview of VSB Project 68
3.2 Initial screen of VSB 69
3.3 The "About VSB" dialog. 70
3.4 File Menu Items 70
3.5 Preferences Dialog. 71
3.6 Edit Menu Items 71
3.7 Object Menu Items 72
3.8 Project Menu Items 73
3.9 "Generating Code" Dialog. 73
3.10 Window Menu Items 74
3.11 Help Menu Items 74
3.12 An example of help dialog 76
3.13 The widgets supported in the palette Window 77
3.14 Output of a model 77
3.15 First experiment 78
x ECONOMIC AND FINANCIAL SIMULATIONS IN SWARM
3.16 The output of the simple model 79
3.17 Result of the first experiment 80
3.18 Output of the Second experiment 82
3.19 Output of "Financial Simulation" (all graphs) 82
3.20 "Add Bar Chart" dialog 85
4.1 The simple SWIEE framework. 90
4.2 The complex SWIEE framework. 91
4.3 RMI - separation of interface and implementation. 92
4.4 RMI - the interface proxy. 92
4.5 RMI - communication layers. 93
4.6 The RMI scheme. 94
4.7 The prisoner's dilemma - simple structure. 98
4.8 The prisoner's dilemma - setting game parameters. 100
4.9 The prisoner's dilemma - player's applet. 101
4.10 The prisoner's dilemma - game instructions window. 102
4.11 The prisoner's dilemma - payoffs of a couple of players. 103
4.12 Simulative Experiments and software tools. 105
5.1 Illiquidity crises on local networks vs no interac. 124
5.2 Illiquidity crises on global networks vs no interac. 125
5.3 Comparison of illiquidity crises between local net-
work and global network 126
5.4 Insolvency crises on local network vs no interac. 127
5.5 Insolvency crises on global network vs no interac. 128
5.6 Comparison of insolvency crises between local and
global network 128
6.1 first investigation (first graph) 148
6.2 first investigation (second graph) 148
6.3 first investigation (third graph) 149
6.4 first investigation (fourth graph) 149
6.5 second investigation (all graphs) 150
6.6 third investigation (all graphs) 150
6.7 fourth investigation (all graphs) 151
6.8 fifth investigation (all graphs) 151
7.1 Qualitative dynamic toward equilibrium map. 164
7.2 Trajectories with j3 > 1 166
7.3 Discrete trajectories with j3 = 1 167
7.4 Distribution of firms' equity bases. 170
7.5 Path of Equity base towards equilibrium with no
heterogeneity, no noise, initial N=400 and A=20 173