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E-raamat: Complexity and Artificial Markets

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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.
Part I Market Mechanisms
Zero-Intelligence Trading Without Resampling
3(12)
Marco LiCalzi
Paolo Pellizzari
Introduction
3(1)
The Model
4(3)
Results
7(6)
Test 1: Does Resampling Matter?
7(1)
Test 2: Which Protocol Performs Better Under Zero Intelligence?
8(1)
Test 3: Does Learning Make a Difference?
9(4)
Conclusions
13(1)
References
14(1)
Understanding the Price Dynamics of a Real Market Using Simulations: The Dutch Auction of the Pescara Wholesale Fish Market
15(12)
Gianfranco Giulioni
Edgardo Bucciarelli
Introduction
15(1)
Market Description
16(1)
Modeling the Buyers' Bidding Behavior
17(3)
Simulations and Validation
20(3)
Discussion and Conclusions
23(1)
Appendix
24(1)
The Bidding Threshold
24(1)
Simulations Settings
24(1)
References
25(2)
Market Behavior Under Zero-Intelligence Trading and Price Awareness
27(14)
Lucia Milone
Introduction
27(1)
The Model
28(3)
Behavioral Assumptions
29(1)
Open and Closed Book Scenarios
30(1)
Experimental Design
30(1)
Results
31(5)
Outcome Variables
31(1)
Efficiency
32(1)
Volume
33(2)
Transaction Prices
35(1)
Conclusions
36(1)
References
37(4)
Part II Evolution and Decision Making
Evolutionary Switching between Forecasting Heuristics: An Explanation of an Asset-Pricing Experiment
41(14)
Mikhail Anufriev
Cars Hommes
Introduction
41(1)
Laboratory Experiment
42(4)
Findings of the Experiment
44(1)
Discussion
44(2)
Evolutionary Model
46(4)
Forecasting Heuristics
47(1)
Evolutionary Switching
48(1)
Model Initialization
49(1)
Simulations of the Model
50(2)
Conclusion
52(1)
References
52(3)
Prospect Theory Behavioral Assumptions in an Artificial Financial Economy
55(12)
Marco Raberto
Andrea Teglio
Silvano Cincotti
Introduction
56(1)
The Model
57(3)
Results and Discussion
60(5)
Conclusions
65(1)
References
66(1)
Computing the Evolution of Walrasian Behaviour
67(10)
Gonzalo Fernandez-de-Cordoba
Alvaro P. Navas
Introduction
67(2)
The Vega--Redondo Economy Model
69(1)
The Behavioural Rules Set
70(4)
Walrasian Equilibrium Revisited
74(1)
Conclusions
74(2)
References
76(1)
Multidimensional Evolving Opinion for Sustainable Consumption Decision
77(14)
Sabine Garabedian
Introduction
77(1)
Multidimensional Opinion
78(4)
Direct Opinion: An Opinion About the Characteristic
79(1)
Indirect Opinion: An Opinion Resulting from Social Interaction
80(1)
Consumers Classification
81(1)
Computer Simulation and Results
82(4)
Groups' Characteristics
83(1)
Impact of Elasticity Values
84(1)
Impact of Discussion Rate
85(1)
Conclusion
86(1)
References
86(5)
Part III Information Economics
Local Interaction, Incomplete Information and Properties of Asset Prices
91(16)
Richard Hule
Jochen Lawrenz
Introduction
91(3)
The Economy
94(4)
Simulation Results
98(5)
Conclusion
103(1)
References
104(3)
Long-Term Orientation in Trade
107(14)
Gert Jan Hofstede
Catholijn M. Jonker
Tim Verwaart
Introduction
108(1)
Long- vs. Short-Term Orientation
108(2)
The Effect of LTO on Trade Processes
110(2)
Representation in Agents
112(3)
Experimental Verification
115(2)
Conclusion
117(1)
References
118(3)
Agent-Based Experimental Economics in Signaling Games
121(12)
Adolfo Lopez-Paredes
Marta Posada
Cesareo Hernandez
Javier Pajares
Three Approaches to Study Signaling Games
121(2)
Human-Subject Behaviour in a Signaling Game Experiment
123(1)
Modelling Artificial Agents' Behaviour in Signalling Games
124(3)
Parameters and Scenarios of the Simulation
127(1)
Some Simulations Results
127(1)
Conclusions
128(1)
References
129(4)
Part IV Methodological Issues
Why do we need Ontology for Agent-Based Models?
133(14)
Pierre Livet
Denis Phan
Lena Sanders
Introduction
133(1)
From Ontology in Philosophy and Computer Science to Ontological Design for ABM
134(2)
From Individuals to Spatial Entities: What Entities Make Sense from the Ontological Standpoint?
136(3)
Model vs. ``Real'' World and Ontological Test
139(4)
Conclusion
143(1)
References
144(3)
Production and Finance in Eurace
147(12)
Sander van der Hoog
Christophe Deissenberg
Herbert Dawid
Introduction
148(1)
The Eurace Project
148(3)
Flame
149(1)
The Real Sector
150(1)
The Real-Financial Interaction
150(1)
The Financial Management Module
151(7)
General Assumptions
151(1)
The Operating Cycle
152(6)
Conclusion
158(1)
References
158(1)
Serious Games for Economists
159(16)
Wilbert Grevers
Anne van der Veen
Introduction
159(2)
Individual-Based Methods
161(1)
System Theories
162(1)
Mathematical Biology and Game Theory
163(1)
Simulation Methods
164(1)
AI in Computer Games
165(3)
Conclusions
168(1)
References
169(6)
Part V Invited Speakers
Computational Evolution
175(20)
Peter A. Henning
Introduction
175(2)
Catastrophic Events in Macro Evolution
177(4)
Variations of Micro Evolution
181(6)
Evolution Strategy for Throwing
183(3)
Other Examples for Micro Evolution
186(1)
Bottom-Up Evolution by Digital Biochemistry
187(4)
Summary and Outlook
191(1)
References
192(3)
Artificial Markets: Rationality and Organisation
195(38)
Alan Kirman
Introduction
195(2)
Relationships in Markets
197(2)
The Marseille Fish Market (Saumaty)
199(2)
A Simple Market Model
201(1)
Trading Relationships Within the Market
202(1)
A Little Formal Analysis
203(6)
An Artificial Market Based on a Simpler Modelling Approach
209(7)
Other Forms of Market Organisation
216(1)
Meritan a Market Based on Dutch Auctions
217(2)
The Empirical Evidence
219(1)
Price Dynamics
219(2)
Loyalty Again
221(2)
Comparison Between Auctions and the Decentralised Market in an Agent-Based Model
223(1)
Common Features
224(1)
The Auction Market
225(2)
Profit Generated by the Rules
227(1)
Simulations
227(1)
Results with a Large Supply
228(3)
Results with a Limited Supply
231(1)
The Market when Both Sides Learn
231(1)
Conclusion
231(2)
References
233