Decision-making--Mathematical models

The intent of this thesis is to show how rule structures can be derived from influence diagrams and how these structures can be mapped to existing rule-based shell paradigms. We shall demonstrate this mapping with an existing shell having the Evidence (E) --> Hypothesis (H), Certainty Factor (CF) paradigm structure. Influence diagrams are graphical representations of hypothesis to evidence, directed forms of Bayesian influence networks. These allow for inferencing about both diagnostic and predictive (or causal) behavior based on uncertain evidence. We show how this can be implemented through a Probability (P) to CF mapping algorithm and a rule-set conflict resolution methodology. The thesis contains a discussion about the application of probabilistic semantics from Bayesian networks and of decision theory, to derive qualitative assertions about the likelihood of an occurrence; the sensitivity of a conclusion; and other indicators of usefulness. We show an example of this type of capability by the addition of a probability range function for the premise clause in our shell's rule structure.

This study was prepared to analyze the use of the first-period
certainty equivalence procedure in location decisions. Certainty
equivalence is a mathematical technique which explicitly incorporates
probablistic uncertainty in the decision making process.
The feasible location of an international jetport which would service
the South Florida region is used to illustrate this decision making
technique.