A team of eight institutes, let by the UPF Barcelona School of Management and including universities from Norway, Poland, Estonia and the United Kingdom show how the inclusion of neglected currents of thought such as non-Ricardian economics, bioeconomics and a set of qualitative-quantitative methods from post-normal science leads to richer perspectives. Using this palette allows gazing into more possible futures.
The proof of this approach is demonstrated with case studies in the energy, water, health and climate domains.
Looking at how energy policies affect for example Sámi population one discover how energy issue are never purely techno-economic but involve networks of relation between different communities and cultures, whose interests risk the invisibility if the issue is framed reductively.
The same data- and model-driven approach applied to issues of water security reveals that the potential impact of irrigation in the water cycle can be much more serious than previously thought once the existing uncertainties are properly mapped.
Modelling has made it to the headlines and become enmeshed in socio-political conflicts. Thus policy prescription based on cost-benefit analysis and related concepts, such as the value of a statistical life (VSL) lead to neat diagnoses based on hyper precise numbers. But are we looking at all numbers? Are we looking at the right numbers?Andrea Saltelli, UPF Barcelona School of Management
The same patterns emerge looking at the conflicted issue of pesticides and pollinators decline. While natural sciences – chemistry, biology and entomology are surely needed to understand the issue, the problem is also one of conflicted interests, regulatory capture and power differentials, not unlike the picture painted by Rachel Carson in the now old Silent Spring book.
Using the methods developed in the PNS tradition one discovers how never as with the present pandemics have numbers, and the attendant activities of measuring and modelling, taken centre-stage. Yet these numbers, often delivered by academics and media alike with extraordinary precision, rely on a rich repertoire of assumptions, including forms of bias, that can significantly skew both the numbers per se and the trust we repose in them.