Series:

Advanced Information and Knowledge Processing

5 total works

Cover of Economic Modeling Using Artificial Intelligence Methods

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.

Cover of Militarized Conflict Modeling Using Computational Intelligence

Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict.

Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power.

Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.

Cover of Artificial Intelligence in Economics and Finance Theories

As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificialintelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision.

The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.


Cover of Artificial Intelligence Techniques for Rational Decision Making

Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon's bounded rationality theory are flexible due to advanced signal processing techniques, Moore's Law and artificial intelligence.

Artificial Intelligence Techniques for Rational Decision Making examines and defines the concepts of causal and correlation machines and applies the transmission theory of causality as a defining factor that distinguishes causality from correlation. It develops the theory of rational counterfactuals which are defined as counterfactuals that are intended to maximize the attainment of a particular goal within the context of a bounded rational decision making process. Furthermore, it studies four methods for dealing with irrelevant information in decision making:

  • Theory of the marginalization of irrelevant information
  • Principal component analysis
  • Independent component analysis
  • Automatic relevance determination method

In addition it studies the concept of group decision making and various ways of effecting group decision making within the context of artificial intelligence.

Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.

Cover of Artificial Intelligence and Economic Theory: Skynet in the Market

This book theoretically and practically updates major economic ideas such as demand and supply, rational choice and expectations, bounded rationality, behavioral economics, information asymmetry, pricing, efficient market hypothesis, game theory, mechanism design, portfolio theory, causality and financial engineering in the age of significant advances in man-machine systems. The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence concepts such as the swarming of birds, the working of the brain and the pathfinding of the ants.

Artificial Intelligence and Economic Theory: Skynet in the Market analyses the impact of artificial intelligence on economic theories, a subject that has not been studied. It also introduces new economic theories and these are rational counterfactuals and rational opportunity costs. These ideas are applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict. Artificial intelligence ideas used in this book include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms. It, furthermore, explores ideas in causality including Granger as well as the Pearl causality models.