Ebook: Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited
Author: Dr. Norman Ehrentreich (auth.)
- Tags: Financial Economics, Artificial Intelligence (incl. Robotics), Operations Research/Decision Theory, Methodology and the History of Economic Thought, Computer Appl. in Social and Behavioral Sciences
- Series: Lecture Notes in Economics and Mathematical Systems 602
- Year: 2008
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
From the reviews:
"The book under review is an important and careful study of some of the issues involved in the workings of the SFI stock market. … In my opinion, Ehrentreich’s book is an excellent reference to both the learning, and empirical literature in finance." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 1141, 2008)
"Norman Ehrentreich was one of the daring few to take on the model, and he has summarized his work and findings in this excellent book. … It is useful primer for anyone interested in getting started in the area of agent-based finance. … It is essential reading for anyone interested in the dynamics of the SFI market in particular, but I also recommend it for others as a useful resource on agent-based financial market design as well." (Blake LeBaron, Journal of Artificial Societies and Social Simulation, Vol. 12 (2), March, 2009)
A hugely important and timely work, this text reconciles the existence of technical trading with the Efficient Market Hypothesis.
By analyzing a well-known agent-based model, the Santa Fe Institute Artificial Stock Market (SFI-ASM), it finds that when selective forces are weak, financial evolution cannot guarantee that only the fittest trading rules will survive.
Its main contribution lies in the application of standard results from population genetics which have widely been neglected in the agent-based community. This has led to various misinterpretations of previous simulation results.
The book is able to finally establish the emergence of technical trading for faster learning speeds in the SFI-ASM beyond a doubt.
In emphasizing the importance of genetic drift as an important evolutionary factor and analyzing its effects on various mutation operators, this book provides agent-based modelers with several tools to design better evolutionary algorithms.
A hugely important and timely work, this text reconciles the existence of technical trading with the Efficient Market Hypothesis.
By analyzing a well-known agent-based model, the Santa Fe Institute Artificial Stock Market (SFI-ASM), it finds that when selective forces are weak, financial evolution cannot guarantee that only the fittest trading rules will survive.
Its main contribution lies in the application of standard results from population genetics which have widely been neglected in the agent-based community. This has led to various misinterpretations of previous simulation results.
The book is able to finally establish the emergence of technical trading for faster learning speeds in the SFI-ASM beyond a doubt.
In emphasizing the importance of genetic drift as an important evolutionary factor and analyzing its effects on various mutation operators, this book provides agent-based modelers with several tools to design better evolutionary algorithms.
Content:
Front Matter....Pages I-XVI
Front Matter....Pages 1-1
Introduction....Pages 3-4
The Rationale for Agent-Based Modeling....Pages 5-18
The Concept of Minimal Rationality....Pages 19-28
Learning in Economics....Pages 29-49
Replicating the Stylized Facts of Financial Markets....Pages 51-88
Front Matter....Pages 89-89
The Original Santa Fe Institute Artificial Stock Market....Pages 91-112
A Suggested Modification to the SFI-ASM....Pages 113-125
An Analysis of Wealth Levels....Pages 127-146
Selection, Genetic Drift, and Technical Trading....Pages 147-179
Summary and Future Research....Pages 181-185
Appendix....Pages 187-193
Back Matter....Pages 195-232
A hugely important and timely work, this text reconciles the existence of technical trading with the Efficient Market Hypothesis.
By analyzing a well-known agent-based model, the Santa Fe Institute Artificial Stock Market (SFI-ASM), it finds that when selective forces are weak, financial evolution cannot guarantee that only the fittest trading rules will survive.
Its main contribution lies in the application of standard results from population genetics which have widely been neglected in the agent-based community. This has led to various misinterpretations of previous simulation results.
The book is able to finally establish the emergence of technical trading for faster learning speeds in the SFI-ASM beyond a doubt.
In emphasizing the importance of genetic drift as an important evolutionary factor and analyzing its effects on various mutation operators, this book provides agent-based modelers with several tools to design better evolutionary algorithms.
Content:
Front Matter....Pages I-XVI
Front Matter....Pages 1-1
Introduction....Pages 3-4
The Rationale for Agent-Based Modeling....Pages 5-18
The Concept of Minimal Rationality....Pages 19-28
Learning in Economics....Pages 29-49
Replicating the Stylized Facts of Financial Markets....Pages 51-88
Front Matter....Pages 89-89
The Original Santa Fe Institute Artificial Stock Market....Pages 91-112
A Suggested Modification to the SFI-ASM....Pages 113-125
An Analysis of Wealth Levels....Pages 127-146
Selection, Genetic Drift, and Technical Trading....Pages 147-179
Summary and Future Research....Pages 181-185
Appendix....Pages 187-193
Back Matter....Pages 195-232
....