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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:
Economic Modeling Using Artificial Intelligence Methods makes an importantcontribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.
Autorentext
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).
Zusammenfassung
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:
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.
Inhalt
Foreword.- Preface.- Acknowledgements.- Introduction to Economic Modeling.- Techniques for Economic Modeling: Unlocking the Character of Data.- Automatic Relevance Determination in Economic Modeling.- Neural Approaches to Economic Modeling.- Bayesian Support Vector Machines for Economic Modeling: Application to Option Pricing.- Rough Sets Approach to Economic Modeling: Unlocking Knowledge in Financial Data.- Missing Data Approaches to Economic Modeling: Optimization Approach.- Correlations versus Causality Approaches to Economic Modeling.- Evolutionary Approaches to Computational Economics: Application to Portfolio Optimization.- Real-time Approaches to Computational Economics: Self Adaptive Economic Systems.- Multi-Agent Approaches to Economic Modeling: Game Theory, Ensembles, Evolution and the Stock Market.- Control Approaches to Economic Modeling: Application to Inflation Targeting.- Modeling Interstate Conflict: The Role of Economic Interdependency for Maintaining Peace.- Conclusions and Further Work.- Index.