Abstract:
This thesis describes two studies conducted within a naturalistic
decision making paradigm. Study One examines the choice of university for
master level education. This decision is presented as a consequential choice
decision task. Students, who had been offered placements at Cranfield
University for the 1998/99 term, participated in this research. Factors
influencing the participant’s decision to attend or not to attend Cranfield were
collected with a questionnaire specifically designed for this purpose. The final
data set contained 267 questionnaires.
Study two describes a decision where a disruptive passenger threatens
a hypothetical flight. Sixty-five professional members of flight crew
participated in a series of semi-structured telephone interviews during which
they described their decision-making process for dealing with this situation.
This decision process is presented as a pattern-matching task.
Artificial neural networks were used to model the decision on the basis
of the input variables (questionnaire items in study one and interview variables
in study two) undertaken to produce an empirically verifiable model of the
participants decision making process.
Cross-validation of the results showed that decision outcomes could be
predicted on the basis of the models. The cross-validation results, in terms of
classifications are compared with discriminant function analysis classification
results, to determine if neural networks or discriminant function analysis is a
more appropriate form of analysis for modelling a naturalistic decision. Both
studies show that neural networks outperformed the discriminant function
analysis results in terms of classification. Press’s Q analyses also support this
finding.
It is suggested that neural networks may be a viable way of modelling
naturalistic decisions.
