Economic Modeling Using Artificial Intelligence Methods

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Springer Science & Business Media, Apr 2, 2013 - Computers - 261 pages

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena.

The artificial intelligence techniques used to model economic data include:

  • multi-layer perceptron neural networks
  • radial basis functions
  • support vector machines
  • rough sets
  • genetic algorithm
  • particle swarm optimization
  • simulated annealing
  • multi-agent system
  • incremental learning
  • fuzzy networks

Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation.

Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.

 

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Contents

Chapter 1 Introduction to Economic Modeling
1
Chapter 2 Techniques for Economic Modeling Unlocking the Character of Data
22
Chapter 3 Automatic Relevance Determination in Economic Modeling
45
Chapter 4 Neural Approaches to Economic Modeling
65
Chapter 5 Bayesian Support Vector Machines for Economic Modeling Application to Option Pricing
82
Chapter 6 Rough Sets Approach to Economic Modeling Unlocking Knowledge in Financial Data
101
Chapter 7 Missing Data Approaches to Economic Modeling Optimization Approach
119
Chapter 8 Correlations Versus Causality Approaches to Economic Modeling
137
Chapter 10 RealTime Approaches to Computational Economics Self Adaptive Economic Systems
173
Chapter 11 Multiagent Approaches to Economic Modeling Game Theory Ensembles Evolution and the Stock Market
194
Chapter 12 Control Approaches to Economic Modeling Application to Inflation Targeting
215
Chapter 13 Modeling Interstate Conflict The Role of Economic Interdependency for Maintaining Peace
232
Chapter 14 Conclusions and Further Work
253
Biography
257
Index
259
Copyright

Chapter 9 Evolutionary Approaches to Computational Economics Application to Portfolio Optimization
155

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About the author (2013)

Tshilidzi Marwala, born in Venda (Limpopo, South Africa), is the Dean of Engineering at the University of Johannesburg. He is a senior member of the IEEE and distinguished member of the ACM. He is the youngest recipient of the Order of Mapungubwe and was awarded the President Award by the National Research Foundation. His research interests include the applications of computational intelligence to engineering, computer science, finance, social science and medicine.

In addition to Economic Modeling Using Artificial Intelligence Methods, he has previously published 3 books with Springer: Condition Monitoring Using Computational Intelligence Methods (2012), Militarized Conflict Modeling Using Computational Intelligence Techniques (2011); and Finite Element Model Updating Using Computational Intelligence Techniques (2010).