Evolutionary Computation in Economics and Finance

1. Front Matter

   Pages I-XII

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2. An Overview

   1. Front Matter

      Pages 1-1

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   2. Evolutionary Computation in Economics and Finance: An Overview of the Book

      • Shu-Heng Chen

      Pages 3-28

3. Games

   1. Front Matter

      Pages 29-29

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   2. Playing Games with Genetic Algorithms

      • Robert E. Marks

      Pages 31-44

   3. Genetic Algorithm Learning and Economic Evolution

      • Thomas Riechmann

      Pages 45-60

   4. Using Symbolic Regression to Infer Strategies from Experimental Data

      • John Duffy, Jim Engle-Warnick

      Pages 61-82

4. Agent-Based Computation Economics

   1. Front Matter

      Pages 83-83

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   2. The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents

      • Jing Yang

      Pages 85-105

   3. On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming

      • Shu-Heng Chen, Chia-Husan Yeh, Chung-Chih Liao

      Pages 107-122

   4. Exchange Rate Volatility in the Artificial Foreign Exchange Market

      • Jasmina Arifovic

      Pages 123-134

   5. Using an Artificial Market Approach to Analyze Exchange Rate Scenarios

      • Kiyoshi Izumi, Kazuhiro Ueda

      Pages 135-157

   6. Emulating Trade in Emissions Permits: An Application of Genetic Algorithms

      • Rosalyn Bell, Stephen Beare

      Pages 159-173

   7. Cooperative Computation with Market Mechanism

      • Masayuki Ishinishi, Hiroshi Sato, Akira Namatame

      Pages 175-188

   8. Hysteresis in an Evolutionary Labor Market with Adaptive Search

      • Leigh Tesfatsion

      Pages 189-210

   9. Computable Learning, Neural Networks and Institutions

      • Francesco Luna

      Pages 211-232

   10. On Two Types of GA-Learning

       • Nicolaas J. Vriend

       Pages 233-243

   11. Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences

       • David B. Fogel, Kumar Chellapilla, Peter J. Angeline

       Pages 245-269

5. Financial Engineering

   1. Front Matter

      Pages 271-271

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   2. Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics

      • George G. Szpiro

      Pages 273-286

After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.

• Finance
• agents
• algorithms
• computational economics
• control
• economics
• evolution
• evolutionary computation
• game theory
• genetic algorithms
• genetic programming
• linear optimization
• modeling
• neural networks
• programming
• algorithm analysis and problem complexity

• AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan, R. O. China

  Shu-Heng Chen

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