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Title: Reasoning with incomplete information : within the framework of Bayesian networks and influence diagrams
Author: Enderwick, Tracey Claire
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2008
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Human cognitive limitations make it very difficult to effectively process and rationalise information in complex situations. To overcome this limitation many analytical methods have been designed and applied to aid decision-makers in complex situations. In some cases, the information gained is comprehensive and complete. However, very often it is the case that information regarding the situation is incomplete and uncertain. In these cases it is necessary to reason with incomplete and uncertain information. The probabilistic graphical models known as Bayesian Networks and Influence Diagrams provide a powerful and increasingly popular framework to represent such situations. The research described here makes use of this framework to address a number of aspects relating to incomplete information. The methods presented are intended to provide support in areas of measuring the completeness of information, assessing the trade-off of speed versus quality of decision-making and incorporating the impact of unrevealed information as time progresses. Two measures are investigated to determine the completeness levels of influential observable information. One measure is based on mutual information. This measure is ultimately shown to fail, however, since it can result in a negative completeness value. The other measure focuses on the range reductions of either the probabilities (for the Bayesian Networks) or the utilities (for the Influence Diagrams) when observations are made. Analytical models were developed to determine the trade off between waiting for more information or making an immediate decision. A number of experiments involving participants in imaginary decision-making scenarios were also conducted to gain an understanding of how people intuitively weight such choices. The value of unrevealed information was utilised by applying likelihood evidence. Unrevealed information relates to something we are looking for but have not yet found. The longer time passes without it being found, the more confident we can become that it is not actually there.
Supervisor: McNaught, Ken Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available