Schredelseker / Hauser Complexity and Artificial Markets

2008
ISBN: 978-3-540-70556-7
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 614, 234 Seiten

Reihe: Lecture Notes in Economics and Mathematical Systems

In recent years, agent-based simulation has become a widely accepted tool when dealing with complexity in economics and other social sciences. The contributions presented in this book apply agent-based methods to derive results from complex models related to market mechanisms, evolution, decision making, and information economics. In addition, the applicability of agent-based methods to complex problems in economics is discussed from a methodological perspective. The papers presented in this collection combine approaches from economics, finance, computer science, natural sciences, philosophy, and cognitive sciences.

Autoren/Hrsg.

Schredelseker, Klaus

Hauser, Florian

Inhaltsverzeichnis

1;Preface;5
2;Acknowledgements;8
3;Contents;9
4;Contributors;14
5;Market Mechanisms;17
5.1;Zero-Intelligence TradingWithout Resampling;18
5.1.1;1.1 Introduction;18
5.1.2;1.2 The Model;19
5.1.3;1.3 Results;22
5.1.4;1.4 Conclusions;28
5.1.5;References;29
5.2;Understanding the Price Dynamics of a Real Market Using Simulations: The Dutch Auction of the Pescara Wholesale Fish Market;30
5.2.1;2.1 Introduction;30
5.2.2;2.2 Market Description;31
5.2.3;2.3 Modeling the Buyers’ Bidding Behavior;32
5.2.4;2.4 Simulations and Validation;35
5.2.5;2.5 Discussion and Conclusions;38
5.2.6;2.6 Appendix;39
5.2.7;References;40
5.3;Market Behavior Under Zero-Intelligence Trading and Price Awareness;41
5.3.1;3.1 Introduction;41
5.3.2;3.2 The Model;42
5.3.3;3.3 Results;45
5.3.4;3.4 Conclusions;50
5.3.5;References;51
6;Evolution and Decision Making;52
6.1;Evolutionary Switching between Forecasting Heuristics: An Explanation of an Asset- Pricing Experiment;53
6.1.1;4.1 Introduction;53
6.1.2;4.2 Laboratory Experiment;54
6.1.3;4.3 Evolutionary Model;58
6.1.4;4.4 Simulations of the Model;62
6.1.5;4.5 Conclusion;64
6.1.6;References;64
6.2;Prospect Theory Behavioral Assumptions in an Artificial Financial Economy;66
6.2.1;5.1 Introduction;67
6.2.2;5.2 The Model;68
6.2.3;5.3 Results and Discussion;71
6.2.4;5.4 Conclusions;76
6.2.5;References;77
6.3;Computing the Evolution of Walrasian Behaviour;78
6.3.1;6.1 Introduction;78
6.3.2;6.2 The Vega–Redondo Economy Model;80
6.3.3;6.3 The Behavioural Rules Set;81
6.3.4;6.4 Walrasian Equilibrium Revisited;85
6.3.5;6.5 Conclusions;85
6.3.6;References;87
6.4;Multidimensional Evolving Opinion for Sustainable Consumption Decision;88
6.4.1;7.1 Introduction;88
6.4.2;7.2 Multidimensional Opinion;89
6.4.3;7.3 Computer Simulation and Results;93
6.4.4;7.4 Conclusion;97
6.4.5;References;97
7;Information Economics;99
7.1;Local Interaction, Incomplete Information and Properties of Asset Prices;100
7.1.1;8.1 Introduction;100
7.1.2;8.2 The Economy;103
7.1.3;8.3 Simulation Results;107
7.1.4;8.4 Conclusion;112
7.1.5;References;113
7.2;Long-Term Orientation in Trade;115
7.2.1;9.1 Introduction;116
7.2.2;9.2 Long- vs. Short-Term Orientation;116
7.2.3;9.3 The Effect of LTO on Trade Processes;118
7.2.4;9.4 Representation in Agents;120
7.2.5;9.5 Experimental Verification;123
7.2.6;9.6 Conclusion;125
7.2.7;References;126
7.3;Agent-Based Experimental Economics in Signaling Games;128
7.3.1;10.1 Three Approaches to Study Signaling Games;128
7.3.2;10.2 Human-Subject Behaviour in a Signaling Game Experiment;130
7.3.3;10.3 Modelling Artificial Agents’ Behaviour in Signalling Games;131
7.3.4;10.4 Parameters and Scenarios of the Simulation;134
7.3.5;10.5 Some Simulations Results;134
7.3.6;10.6 Conclusions;135
7.3.7;References;136
8;Methodological Issues;137
8.1;Why do we need Ontology for Agent-Based Models?;138
8.1.1;11.1 Introduction;138
8.1.2;11.2 From Ontology in Philosophy and Computer Science to Ontological Design for ABM;139
8.1.3;11.3 From Individuals to Spatial Entities: What Entities Make Sense from the Ontological Standpoint?;141
8.1.4;11.4 Model vs. "Real” World and Ontological Test;144
8.1.5;11.5 Conclusion;148
8.1.6;References;149
8.2;Production and Finance in EURACE;151
8.2.1;12.1 Introduction;152
8.2.2;12.2 The EURACE Project;152
8.2.3;12.3 The Financial Management Module;155
8.2.4;12.4 Conclusion;162
8.2.5;References;162
8.3;Serious Games for Economists;163
8.3.1;13.1 Introduction;163
8.3.2;13.2 Individual-Based Methods;165
8.3.3;13.3 System Theories;166
8.3.4;13.4 Mathematical Biology and Game Theory;167
8.3.5;13.5 Simulation Methods;168
8.3.6;13.6 AI in Computer Games;169
8.3.7;13.7 Conclusions;172
8.3.8;References;173
9;Invited Speakers;176
9.1;Computational Evolution;177
9.1.1;14.1 Introduction;177
9.1.2;14.2 Catastrophic Events in Macro Evolution;179
9.1.3;14.3 Variations of Micro Evolution;183
9.1.4;14.4 Bottom-Up Evolution by Digital Biochemistry;189
9.1.5;14.5 Summary and Outlook;193
9.1.6;References;194
9.2;Artificial Markets: Rationality and Organisation;196
9.2.1;15.1 Introduction;196
9.2.2;15.2 Relationships in Markets;198
9.2.3;15.3 The Marseille Fish Market (Saumaty);200
9.2.4;15.4 A Simple Market Model;202
9.2.5;15.5 Trading Relationships Within the Market;203
9.2.6;15.6 A Little Formal Analysis;204
9.2.7;15.7 An Artificial Market Based on a Simpler Modelling Approach;210
9.2.8;15.8 Other Forms of Market Organisation;217
9.2.9;15.9 MERITAN a Market Based on Dutch Auctions;218
9.2.10;15.10 The Empirical Evidence;220
9.2.11;15.11 Price Dynamics;220
9.2.12;15.12 Loyalty Again;222
9.2.13;15.13 Comparison Between Auctions and the Decentralised Market in an Agent- Based Model;224
9.2.14;15.14 Common Features;225
9.2.15;15.15 The Auction Market;226
9.2.16;15.16 Profit Generated by the Rules;228
9.2.17;15.17 Simulations;228
9.2.18;15.18 Results with a Large Supply;229
9.2.19;15.19 Results with a Limited Supply;232
9.2.20;15.20 The Market when Both Sides Learn;232
9.2.21;15.21 Conclusion;232
9.2.22;References;234