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Artificial Economics: The Generative Method in Economics 2009 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 268 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 70 Illustrations, black and white; XXIV, 268 p. 70 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 631
  • Ilmumisaeg: 26-Aug-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642029558
  • ISBN-13: 9783642029554
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Artificial Economics: The Generative Method in Economics 2009 ed.Suurem pilt
  • Formaat: Paperback / softback, 268 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 70 Illustrations, black and white; XXIV, 268 p. 70 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 631
  • Ilmumisaeg: 26-Aug-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642029558
  • ISBN-13: 9783642029554
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642029554.html
Märksõnad:
Simulation is used in economics to solve large econometric models, for large-scale micro simulations, and to obtain numerical solutions for policy design in top-down established models. But these applications fail to take advantage of the methods offered by artificial economics (AE) through artificial intelligence and distributed computing. AE is a bottom-up and generative approach of agent-based modelling developed to get a deeper insight into the complexity of economics. AE can be viewed as a very elegant and general class of modelling techniques that generalize numerical economics, mathematical programming and micro simulation approaches. The papers presented in this book address methodological questions and applications of AE to macroeconomics, industrial organization, information and learning, market dynamics, finance and financial markets.

The papers presented in this book address methodological questions and applications of artificial economics (AE) to macroeconomics, industrial organization, information and learning, market dynamics, finance and financial markets.

Arvustused

From the reviews:

Artificial Economics: The Generative Method in Economics represents the proceedings of the Artificial Economics Conference . Given the breadth of topics covered from Auction Theory to labor markets certainly something to be found for everybody with an interest in AgentBased Modeling in economics. I have enjoyed reading the contributions to this volume. This volume is an impressive snapshot of the current state of the art in AgentBased Computational Economics and host to a lot of fresh ideas . (Wolfgang Radax, Journal of Artificial Societies and Social Simulation, Vol. 13 (1), 2010)

Part I Macroeconomics
A Potential Disadvantage of a Low Interest Rate Policy: the Instability of Banks Liquidity
3(12)
Gianfranco Giulioni
Introduction
3(2)
The Bank
5(4)
Profitability and Liquidity
6(1)
An Idealization of the Bank Activity
6(1)
The Lending Activity
7(2)
Simulations
9(2)
Comparative Static, Dynamics and Credit Rationing
11(1)
Conclusions
12(3)
References
13(2)
Keynes in the Computer Laboratory. An Agent-Based Model with MEC, MPC, LP
15(14)
Giulia Canzian
Edoardo Gaffeo
Roberto Tamborini
Introduction
15(2)
The Model
17(6)
Methodological Premises
17(1)
Modelling the Market Sentiment
18(1)
The Marginal Efficiency of Capital
19(1)
The Marginal Propensity to Consume
20(1)
The Liquidity Preference
21(1)
Aggregate Supply
22(1)
Simulations Results
23(3)
GDP Series
23(2)
GDP and its Components
25(1)
Conclusions
26(3)
References
27(2)
Pride and Prejudice on a Centralized Academic Labor Market
29(14)
Philippe Caillou
Michele Sebag
Introduction
29(1)
Related Work
30(1)
The French Academic Labor Market
31(1)
The Hiring Process
31(1)
Empirical Evidence
31(1)
Academic Labor Market Modelling
32(2)
Agent Preferences
32(1)
Multi-Agent Based Model
32(2)
Simulation Results
34(5)
Methodology and Experimental Settings
34(1)
No Learning Setting
35(1)
Learning Universities Setting
36(3)
Conclusion
39(4)
References
39(4)
Part II Industrial Organization
U. S. Defense Market Concentration: An Analysis of the Period 1996-2006
43(10)
Wayne Zandbergen
Introduction
43(1)
Analysis of U. S. Defense Market Structure 1996-2006
44(5)
Data Sources
44(1)
Market Description & Unique Factors
44(2)
Results and Findings
46(3)
Two Simple Computational Models
49(2)
Conclusion and Further Research
51(2)
References
52(1)
Operator's Bidding Strategies in the Liberalized Italian Power Market
53(14)
Eric Guerci
Mohammad Ali Rastegar
Silvano Cincotti
Introduction
53(2)
ACE Model
55(5)
Market Model
55(2)
Grid Model
57(1)
Agent Model
57(2)
Learning Model
59(1)
Results
60(4)
Conclusions
64(3)
References
65(2)
Selection Processes in a Monopolistic Competition Market
67(14)
Jose I. Santos
Ricardo del Olmo
Javier Pajares
Motivation
67(1)
A Formal Model of a Differentiated Industry
68(3)
Consumer Behavior
68(1)
Evolutionary Firm Behavior
69(2)
Selection and Monopolistic Competition
71(6)
Homogeneous Product Competition
71(1)
Differentiated Product Competition
72(2)
Heterogeneity and Other Model Parameters
74(3)
Conclusions
77(4)
References
77(4)
Part III Market Dynamics and Auctions
Symmetric Equilibria in Double Auctions with Markdown Buyers and Markup Sellers
81(12)
Roberto Cervone
Stefano Galavotti
Marco Licalzi
Introduction
81(1)
The Model
82(1)
The Environment
83(1)
Call Market
83(3)
General Markup and Markdown Coefficients
84(1)
Ex Ante Equilibria
85(1)
Bilateral Trading
86(1)
Continuous Double Auction
87(5)
Conclusions
92(1)
References
92(1)
Multi-Unit Auction Analysis by Means of Agent-Based Computational Economics
93(10)
Asuncion Mochon
Yago Saez
David Quintana
Pedro Isasi
Introduction
93(2)
The Ausubel Auction
95(1)
The Agent-Based Model
96(1)
The Experimental Results
97(3)
Decreasing Marginal Values
97(1)
Increasing Marginal Values
98(2)
Conclusions
100(3)
References
101(2)
Social Learning and Pricing Obfuscation
103(14)
Maciej Latek
Bogumil Kaminski
Introduction
103(2)
Model Architecture
105(3)
Obfuscation Game and Dimensions of Intervention
105(1)
Recursive Companies
106(1)
Adaptive Customers
107(1)
Experiments
108(4)
Baseline Behaviors
109(1)
Efficiency of Market Intervention
110(2)
Conclusions
112(5)
References
113(4)
Part IV Finance
Mutual Funds Flows and the ``Sheriff of Nottingham'' Effect
117(12)
Lucia Milone
Paolo Pellizzari
Introduction
117(2)
A Simple Example and One Analytical Result
119(3)
A Computational Model
122(5)
Results
123(4)
Conclusion
127(2)
References
128(1)
Foundations for a Framework for Multiagent-Based Simulation of Macrohistorical Episodes in Financial Markets
129(16)
Barbara Llacay
Gilbert Peffer
Introduction
129(1)
What is Wrong with MABS?
130(5)
Barriers to MABS for Macrohistorial Research in Finance
131(4)
Proposal for a MABS Framework
135(7)
A Bird's Eye View of the Framework
136(3)
The Modelling Process
139(3)
Conclusions
142(3)
References
143(2)
Explaining Equity Excess Return by Means of an Agent-Based Financial Market
145(14)
Andrea Teglio
Marco Raberto
Silvano Cincotti
Introduction
146(1)
The Model
147(4)
Firms
147(1)
Households
148(2)
The Banking Sector
150(1)
The Government
151(1)
Simulation Results
151(3)
Conclusions
154(5)
References
156(3)
Part V Financial Markets
Bubble and Crash in the Artificial Financial Market
159(12)
Yuji Karino
Toshiji Kawagoe
Introduction
159(1)
An Artificial Financial Market
160(9)
Market Settings
160(2)
Trading Agents
162(2)
Transaction System
164(1)
Results
165(1)
Simulation Results
165(1)
Definition of Price Bubble
166(3)
Sensibility Analysis
169(1)
Conclusion
169(2)
References
170(1)
Computation of the Ex-Post Optimal Strategy for the Trading of a Single Financial Asset
171(14)
Olivier Brandouy
Philippe Mathieu
Iryna Veryzhenko
Introduction
171(2)
Elements of the Game and Formalizations
173(8)
Initial Simplification
174(2)
A Linear Programming Method For the Identification of S
176(1)
Embedding the Identification of S* in a Graph Structure
177(2)
The S * ---determination Algorithm
179(2)
Numerical Illustrations and Conclusive Remarks
181(4)
References
184(1)
A Generative Approach on the Relationship between Trading Volume, Prices, Returns and Volatility of Financial Assets
185(14)
Jose Antonio Pascual
Javier Pajares
Introduction
185(1)
Historical Precedents and Motivation
186(1)
Methodology
187(2)
The ISS-ASM Model
188(1)
Dataset
189(1)
Cross-Correlations and Causal Relation
189(1)
Conclusions - Results
189(6)
Price-Volume Relationship
190(1)
Return-Volume Relationship
191(1)
Volatility-Volume Relationship
192(1)
Causal Relationship
193(2)
Summary
195(4)
References
195(4)
Part VI Information and Learning
Comparing Laboratory Experiments and Agent-Based Simulations: The Value of Information and Market Efficiency in a Market with Asymmetric Information
199(12)
Florian Hauser
Jurgen Huber
Michael Kirchler
Introduction
199(1)
Market Model
200(1)
Experimental Implementation and Simulation
201(2)
Results
203(6)
Distribution of Returns
203(4)
Market Efficiency
207(2)
Conclusion
209(2)
References
209(2)
Asset Return Dynamics under Alternative Learning Schemes
211(12)
Elena Catanese
Andrea Consiglio
Valerio Lacagnina
Annalisa Russino
Introduction
211(2)
The Model
213(4)
The Market Setting
213(1)
The Portfolio Model
214(1)
The Learning Process
214(2)
Statistical Measures of Population Heterogeneity
216(1)
Calibration and Results
217(6)
Simulation Parameters
217(1)
Comparison between the Learning Models
218(4)
References
222(1)
An Attempt to Integrate Path-Dependency in a Learning Model
223(14)
Narine Udumyan
Juliette Rouchier
Dominique Ami
Introduction
223(2)
Study of CPR Based on Information Issue
225(2)
Lack of Information and Over-Exploitation
225(1)
Dealing with Scarce Information in ABM
225(2)
The Model
227(2)
Main Assumptions and General Framework
227(1)
Resource Dynamics and Probabilistic Choice of Effort
227(2)
Simulations
229(1)
Results
230(1)
Discussions
231(1)
Conclusion
232(5)
References
233(4)
Part VII Methodological Issues
A Model-to-Model Analysis of the Repeated Prisoners' Dilemma: Genetic Algorithms vs. Evolutionary Dynamics
237(8)
Xavier Vila
Introduction
237(4)
The Analytical Model
238(1)
The Replicator Dynamics Analysis
239(2)
The Computational Model
241(2)
Conclusions
243(2)
References
244(1)
Impact of Tag Recognition in Economic Decisions
245(12)
David Poza
Felix Villafanez
Javier Pajares
Introduction
245(1)
Cognitive Foundations
246(1)
The Model
247(1)
The Model with One Agent Type
248(6)
Replication
248(3)
Introduction of a New Decision Rule
251(1)
Introduction of a Variable Payoff Matrix
251(3)
The Model with Two Agent Types (the ``Tag'' Model)
254(1)
Conclusions
255(2)
References
255(2)
Simulation of Effects of Culture on Trade Partner Selection
257
Gert Jan Hofstede
Catholijn M. Jonker
Tim Verwaart
Introduction
257
Hofstede's Dimensions and Trade Partner Selection
259
Representation in Agents
260
Simulation Results
263
Conclusion
265
References
267