Agent-Based Modeling [electronic resource] :The Santa Fe Institute Artificial Stock Market Model Revisited / by Norman Ehrentreich.
by Ehrentreich, Norman [author.]; SpringerLink (Online service).
Material type:
BookSeries: Lecture Notes in Economics and Mathematical Systems: 602Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: online resource.ISBN: 9783540738794.Subject(s): Economics | Artificial intelligence | Social sciences -- Data processing | Economics -- Methodology | Finance | Economics/Management Science | Financial Economics | Artificial Intelligence (incl. Robotics) | Operations Research/Decision Theory | Methodology and the History of Economic Thought | Computer Appl. in Social and Behavioral SciencesDDC classification: 332 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| MAIN LIBRARY | HG1-9999 (Browse shelf) | Available |
Agent-Based Modeling in Economics -- The Rationale for Agent-Based Modeling -- The Concept of Minimal Rationality -- Learning in Economics -- Replicating the Stylized Facts of Financial Markets -- The Santa Fe Institute Artificial Stock Market Model Revisited -- The Original Santa Fe Institute Artificial Stock Market -- A Suggested Modification to the SFI-ASM -- An Analysis of Wealth Levels -- Selection, Genetic Drift, and Technical Trading -- Summary and Future Research.
This book 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.
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