  Decision-making processes often involve dealing with uncertainty, which is
traditionally addressed through probabilistic models. However, in practical
scenarios, assessing probabilities reliably can be challenging, compounded by
diverse perceptions of probabilistic information among decision makers. To
address this variability and accommodate diverse preferences regarding
uncertainty, we introduce the Probabilistic Abstract Decision Framework (PADF).
PADF offers a structured approach for reasoning across different decision
criteria, encompassing the optimistic, pessimistic, and Laplace perspectives,
each tailored to distinct perceptions of uncertainty. We illustrate how PADF
facilitates the computation of optimal decisions aligned with these criteria by
leveraging probabilistic rules. Furthermore, we present strategies for
optimizing the computational efficiency of these rules, leveraging appropriate
independence assumptions to navigate the extensive search space inherent in
PADF. Through these contributions, our framework provides a robust and
adaptable tool for effectively navigating the complexities of decision-making
under uncertainty.
