Economic Modeling Using Artificial Intelligence Methods

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Business & Finance, Economics, Theory of Economics, General Computing
Cover of the book Economic Modeling Using Artificial Intelligence Methods by Tshilidzi Marwala, Springer London
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Tshilidzi Marwala ISBN: 9781447150107
Publisher: Springer London Publication: April 2, 2013
Imprint: Springer Language: English
Author: Tshilidzi Marwala
ISBN: 9781447150107
Publisher: Springer London
Publication: April 2, 2013
Imprint: Springer
Language: English

Economic Modeling Using Artificial Intelligence Methodsexamines 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Economic Modeling Using Artificial Intelligence Methodsexamines 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:

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.

More books from Springer London

Cover of the book Mathematics for Computer Graphics by Tshilidzi Marwala
Cover of the book Cases in Structural Cardiac Intervention by Tshilidzi Marwala
Cover of the book Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace Vehicles by Tshilidzi Marwala
Cover of the book Stochastic Modeling for Reliability by Tshilidzi Marwala
Cover of the book Chest Pain with Normal Coronary Arteries by Tshilidzi Marwala
Cover of the book Max-linear Systems: Theory and Algorithms by Tshilidzi Marwala
Cover of the book Interactive Media: The Semiotics of Embodied Interaction by Tshilidzi Marwala
Cover of the book Electronic Identity by Tshilidzi Marwala
Cover of the book Children’s Orthopaedics and Fractures by Tshilidzi Marwala
Cover of the book Model-Based Fault Diagnosis Techniques by Tshilidzi Marwala
Cover of the book Wireless Sensor Networks by Tshilidzi Marwala
Cover of the book The Human Foot by Tshilidzi Marwala
Cover of the book Conflict and Catastrophe Medicine by Tshilidzi Marwala
Cover of the book Cognitive Informatics in Health and Biomedicine by Tshilidzi Marwala
Cover of the book Pappus of Alexandria: Book 4 of the Collection by Tshilidzi Marwala
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy