In decision making problems under uncertainty, Mean Variance Model (MVM) consistent
with Expected Utility Theory (EUT) plays an important role in ranking preferences for
various alternative options. Despite its wide use, this model is appropriate only when
random variables representing the alternative options are normally distributed and the utility
function to be maximized is quadratic; both are undesirable properties to be satisfied with
actual applications.
In this research, a novel methodology has been adopted in developing generalized models
that can reduce the deficiency of the existing models to solve large-scale decision problems,
along with applications to real-world disputes. More specifically, for eliciting preferences for
pairs of alternative options, two approaches are developed: one is based on Mean Variance
Model (MVM), which is consistent with Expected Utility Theory (EUT), and the second is
based on Analytic Hierarchy Processes (AHP). The main innovation in the first approach is
in reformulating MVM to be based on cumulative functions using simulation. Two models
under this approach are introduced: the first deals with ranking preferences for pairs of lotteries/options with non-negative outcomes only while the second, which is for risk
modelling, is a risk-preference model that concerns normalized lotteries representing risk
factors each is obtained from a multiplication decomposition of a lottery into its mean
multiplied by a risk factor. Both approximation models, which are preference-based using
the determined values for expected utility, have the potential to accommodate various
distribution functions with different utility functions and capable of handling decision
problems especially those encountered in financial economics. The study then reformulates
the second approach, AHP; a new algorithm, using simulation, introduces an approximation
method that restricts the level of inherent uncertainty to a certain limit. The research further focuses on proposing an integrated preference-based AHP model
introducing a novel approximation stepwise algorithm that combines the two modified
approaches, namely MVM and AHP; it multiplies the determined value for expected utility,
which results from implementing the modified MVM, by the one obtained from processing
AHP to obtain an aggregated weight indicator. The new integrated weight scale represents
an accurate and flexible tool that can be employed efficiently to solve decision making
problems for possible scenarios that concern financial economics Finally, to illustrate how the integrated model can be used as a practical methodology to
solve real life selection problems, this research explores the first empirical case study on
Tender Selection Process (TSP) in Kurdistan Region (KR) of Iraq; it is considered as an
inductive and a comprehensive investigation on TSP, which has received minimum
consideration in the region, and regarded as a significant contribution to this research. The
implementation of the proposed model to this case study shows that, for the evaluation of
construction tenders, the integrated approach is an appropriate model, which can be easily
modified to assume specific conditions of the proposed project. Using simulation, generated
data allows creation of a feedback system that can be utilized for the evaluation of future
projects in addition to its capability to make data handling easier and the evaluation process
less complex and time consuming