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© 2013

Economic Modeling Using Artificial Intelligence Methods

  • Presents new insights into the modeling of economic data

  • Proposes a structure for evaluating economic strategies such as inflation targeting founded on artificial intelligence techniques

  • Addresses causality and proposes new frameworks for dealing with this issue

  • Applies evolutionary computing to model complex evolving economic environments in an accessible way

Book

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

About this book

Introduction

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.

Keywords

Artificial Intelligence Bayesian Boolean Reasoning Causality Computational Intelligence Decision Rules Econometrics Economic Modeling Economics Financial Engineering Financial Modeling Fuzzy Set Game Theory Rough Set

Authors and affiliations

  1. 1.Faculty of Engineering & the Built EnvirUniversity of JohannesburgJohannesburgSouth Africa

About the authors

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).

Bibliographic information

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Reviews

From the reviews:

“The book explores the application of artificial intelligence methods to economic data modelling. … the book is well addressed to graduate students as well as researchers and practitioners in the field of finance and economics.” (Vangelis Grigoroudis, zbMATH, Vol. 1269, 2013)