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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Prof, can this theory of bounded rationality be used to enhance to enhance the Analytical hierarchy process in selection the suitable bidder or tender who can deliver Eskom project without delaying or causing cost-escation of the project budget cost.
I would know if i can use the artificial intelligent to enhance Analytical Hierarchy Process to select the most preferriable supplier or tender based on the cognitive or human gudgement (expertise of Price, cost, quality and technical) which bidder will most likely deliver Eskom project without delaying the project.
I am looking at attributes such as cost, price, competency, quality and technical capabilities of the suppliers or bidder and generate selection based on the pair-wise comparison of the alternatives based on those attributes. so can artificial intelligent enhance this method of AHP to give optimal solution in selection of bidder or tender.
if i would like to do a rearch into (USE OF ARTIFICIAL INTELIGENT TO ENHANCE AHP for vendor selection in public sector)
From: Shandukani Vhulondo
student no: 920413511
Cell no: 0814523209