We are developing and applying a range of methods for integrated multi-attribute evaluation under risk, subject to incomplete or imperfect information, and evaluations of decision situations using imprecise utilities, probabilities, and weights, as well as qualitative estimates between these components derived from sets of weight, utility and probability measures. To avoid some mathematical aggregation problems when handling set membership functions and similar, we use higher-order distributions for better discrimination between the possible outcomes.

These methods have been applied in a variety of decision situations, such as Covid-19 mitigation, large-scale energy planning, allocation planning, de-mining, portfolio risks, gold mining, and many others, and is suitable, for instance, in interactive multi-criteria decision analysis approaches to synthesize outcome predictions and stakeholder preferences from multiple perspectives into decision recommendations.

The  methodological components could be partitioned into:

(i) a co-creative preference elicitation component,
(ii) a multi-criteria component,
(iii) a risk analytical component, and
(iv) an aggregation and analysis component.

We emphasize the involvement of stakeholders in the decision-making processes and model development as it is generally essential for catering to stakeholder requirements, but also for increasing the acceptability of the decisions. Policy-makers need, for instance, to weigh their decisions against the political costs of implementing sometimes unpopular sets of measures. Not least in emergency situations, a distributed decision-making process could contribute to ensuring that the responsibilities for the results are as well distributed, lowering the political costs and making way for the consideration of a variety of criteria relevant to the problems at hand.