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.
