Uncertainty and risk are pervasive issues in
decision making. With a wide range of causes and types of
uncertainty, there are correspondingly many approaches to their
treatment in decision analysis. Some are tackled through discussion
and creativity techniques to help decision makers set the boundaries
of their problem; others, are tackled through modelling techniques,
e.g. probability, to reflect the randomness in the external world;
yet others are approached through the use of sensitivity and
robustness studies to explore the possible consequences of lack of
precision in estimates and judgements. Where there is a need for
much more work is in the interfaces between the approaches for
dealing with different types of uncertainty. Thus the issues that
are going to be addressed relate to the bringing together of
specific expertises in aspects of handling uncertainty within
decision modelling to build a more comprehensive overview and
integrated methodology to tackle all the various uncertainties in a
decision
problem. Specific subjects which are going to be considered include: \\
- \emph{Formalisms for modelling uncertainty, with particular emphasis
in decision making situations}. Besides probability theory,
several other formalisms have been developed in order to take
into account situations of lack of knowledge or of ambiguous
information (possibility theory, belief functions etc.). The
issue here is how to understand, within a decision situation,
which formalism could be more appropriate, and how to combine
them within a decision aiding process (how to discriminate
between solutions which are highly preferred but assume unlikely
events and solutions which are not preferred but robust with
respect to uncertainty?). Qualitative Decision
Theory results need to be enhanced here and combined with
findings in preference modelling and the use of appropriate
languages for this purpose. \\
- \emph{Sensitivity and Robustness}. While it is known how to
conduct specific sensitivity analyses on a class of models, it is
still required to have more comprehensive methodologies for developing
complete sensitivity analyses, especially in the case of large
complex decision models. Moreover, it is necessary to further investigate
the specific class of robustness issues in decision making: obtaining
results which will be useful independently from variations
which may occur in data and/or scenarios. \\
- \emph{Sequential Decision Making}. With the increasing amount of data
faced by decision makers and the increasing speed with which their
decisions need to be made, it is often the case that decisions
have to be taken online before having access to all of the relevant data.
Sequential decisions are also important in uncertain domains in
which decisions impact the environment and therefore the context
for future decisions. It is important that robust
policies ---policies with guaranteed performance when the nominal
model deviates from the unknown true model--- can be obtained.
Robustness is an increasingly important issue in decision making,
a major challenge being to obtain a robust policy for
dynamic decision models.