Causality, Correlation And Artificial Intelligence For Rational Decision Making
Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman-Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisibility of rationality. The theory of the marginalization of irrationality for decision making is also introduced to deal with satisficing within irrational conditions. The methods proposed are applied in biomedical engineering, condition monitoring and for modelling interstate conflict.
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1 Introduction to Artificial Intelligence based Decision Making
2 What is a Correlation Machine?
3 What is a Causal Machine?
4 Correlation Machines Using Optimization Methods
5 Neural Networks for Modeling Granger Causality
A Unified View
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activation function Artificial Intelligence auto-associative network auto-associative neural networks Bayesian bounded rationality causal machine causal relationship cause chapter Computational Intelligence concept condition monitoring correlation machine counterfactual crossover d-separation data set decision making process described directed acyclic graphs distribution dynamics effect EM algorithm error estimate missing Figure flexibly-bounded rationality fuzzy inference system Gaussian genetic algorithm Granger causality Granger causality model hidden units identify International Journal irrational k-means Lagazio layer logic marginalization of irrationality Marwala Mathematical matrix maximization mechanism missing data estimation missing values MLP neural network Monte Carlo multi-layer perceptron mutation Nelder-Mead Nelwamondo network weights nonlinear number of hidden observed output parameters particle swarm optimization Pearl predict principal component analysis probability problem Proceedings Random A1 rational decision relevant Rubin simulated annealing solution Statistical studied successfully applied support vector machine theory of bounded theory of causality tion trained variables were randomly whereas Zhang