Expert judgments are widespread in many fields, and the way in which they are collected and the procedure by which they are aggregated are considered crucial steps. From a statistical perspective, expert judgments are subjective data and must be gathered and treated as carefully and scientifically as possible. In the elicitation phase, a multitude of experts is preferable to a single expert, and techniques based on anonymity and iterations, such as Delphi, offer many advantages in terms of reducing distortions, which are mainly related to cognitive biases. There are two approaches to the aggregation of the judgments given by a panel of experts, referred to as behavioural (implying an interaction between the experts) and mathematical (involving non-interacting participants and the aggregation of the judgments using a mathematical formula). Both have advantages and disadvantages, and with the mathematical approach, the main problem concerns the subjective choice of an appropriate formula for both normalization and aggregation. We propose a new method for aggregating and processing subjective data collected using the Delphi method, with the aim of obtaining robust rankings of the outputs. This method makes it possible to normalize and aggregate the opinions of a panel of experts, while modelling different sources of uncertainty. We use an uncertainty analysis approach that allows the contemporaneous use of different aggregation and normalization functions, so that the result does not depend on the choice of a specific mathematical formula, thereby solving the problem of choice. Furthermore, we can also model the uncertainty related to the weighting system, which reflects the different expertise of the participants as well as expert opinion accuracy. By combining the Delphi method with the robust ranking procedure, we offer a new protocol covering the elicitation, the aggregation and the processing of subjective data used in the construction of Delphi-based future scenarios. The method is very flexible and can be applied to the aggregation and processing of any subjective judgments, i.e. also those outside the context of futures studies. Finally, we show the validity, reproducibility and potential of the method through its application with regard to the future of Italian families.

The Real-Time Spatial Delphi represents an innovative method tailored to navigate the complexities of uncertain spatial issues. Adopted in Future Studies contexts, this method excels in developing spatial scenarios and leveraging the collaborative insights of experts within a virtual environment to achieve a consensus regarding territorial dynamics. However, while this method yields invaluable spatial insights and statistical metrics, the final outputs often remain confined to expert circles due to their technical complexity. In addition, the outcomes often lack direct policy implications, as they primarily provide an expansive overview of potential future scenarios. In response to these challenges, this paper proposes integrating text-to-image models and generative pre-trained transformers, into the Real-Time Spatial Delphi process. By adopting these advanced tools during the visioning and planning phases, the method endeavors to transform spatial judgments into visually immersive scenarios, while concurrently crafting actionable policy recommendations suitable for evaluation. To validate the approach, we present a case study in the environmental context, for the cities of Cork, Galway, and Limerick, located in Ireland. Through this application, we contribute to Futures Studies by illustrating the method’s capacity to envision plausible futures in the form of real images, considering the formulation of policies to support decision-making.