Agent-Based Methods in Economics and Finance

Agent-Based Methods in Economics and Finance : Simulations in Swarm

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This second book on financial and economic simulations in Swarm marks the continued progress by a group of researchers to incorporate agent-based computer models as an important tool within their disci- pline. It is encouraging to see such a clear example of Swarm helping to foster a community of users who rely on the Swarm framework for their own analyses. Swarm aims at legitimizing agent-based computer models as a tool for the study of complex systems. A further goal is that a common base framework will lead to the growth of user communities in specific areas of application. By providing an organizing framework to guide the development of more problem-specific structures, and by dealing with a whole range of issues that affect their fundamental correctness and their ability to be developed and reused, Swarm has sought to make the use of agent-based models a legitimate tool of scientific investigation that also meets the practical needs of investigators within a community.
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Product details

  • Hardback | 306 pages
  • 178.8 x 228.1 x 24.9mm | 694.01g
  • Dordrecht, Netherlands
  • English
  • 2002 ed.
  • XXXI, 306 p.
  • 0792374193
  • 9780792374190

Table of contents

List of Figures. Preface. Contributing Authors. Introduction. Part I: A Modest Proposal. 1. Prospect for an Economics Framework for Swarm; C. Bruun. Part II: New Tools. 2. Automated Trading Experiments with MAML; L. Gulyas, T. Vincze. 3. VSB - Visual Swarm Builder - A Visual tool for Swarm Agent Based Environment; A. Perrone, M. Tenuti. 4. SWIEE - a Swarm Web Interface for Experimental Economics; R. Boero. Part III: Financial Applications. 5. Contagion of Financial Crises under Local and Global Networks; A. Cassar, N. Duffy. 6. Simulating Fractal Financial Markets; M. Corazza, A. Perrone. 7. Growing Theories from the `Bottom Up'. A Simple Entry-Exit Model; D. Delli Gatti, et al. 8. Cognitive Agents Behaving in a Simple Stock Market Structure; P. Terna. Part IV: Other Contributions. 9. Production Partnerships Formation with Heterogeneous Agents: a Simulation in SWARM; D. Fiaschi, et al. 10. Casino World: An Agent-based Model with Heterogeneous Risk Preferences and Adaptive Behavior; M. Harrington, D. Higa. 11. Search in Artificial Labour Markets: a Simulation Study; M.D. Sapienza, M. Fontana. Index.
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About Francesco Luna

Francesco Luna is a specialist in Computable Economics
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