Shackle’s approach towards priority setting and decision-making in Science, Technology, and Innovation

• Published in: Futures : the journal of policy, planning and futures studies planning and futures studies, 2021-12, Vol.134, p.102838, Article 102838
• Main Authors: Metcalfe, Stan, Salles-Filho, Sergio, Duarte, Leonardo T., Bin, Adriana, Azevedo, Aníbal T., Feitosa, Paulo H.A.
• Format: Article
• Language: English
• Subjects: Decision making; Decision theory; Empirical analysis; G. L. S. Shackle; Innovations; Literature; Methods; Potential surprise; Uncertainty
• ISSN: 0016-3287; 1873-6378
• DOI: 10.1016/j.futures.2021.102838

•Shackle’s framework is a coherent theoretical foundation for ST&I domain.•Shackle’s approach guides the priority setting and decision-making process in ST&I.•The decision maker’s participation requires planning its operational feasibility.•The analysis of alternatives needs to handle increasing volumes of data. This article explores the possibilities of reprising the potential surprise approach of Shackle’s framework as an approach for prospective, priority setting and decision-making process in Science, Technology, and Innovation (ST&I). These domains have been the focus of extensive studies that have promoted several analytical contributions, although it has often been challenging to grapple with the inherent condition of fundamental uncertainty. This article contributes to the literature in two main ways. First, it demonstrates that Shackle’s potential surprise and ascendency function approach can constitute a coherent theoretical and empirical foundation for ST&I prioritisation and decision-making. Second, it presents an application in a real context to demonstrate its feasibility as an applied tool. Operational conditions and an organisational routine with necessary steps are also suggested to contribute to the development of the proposed approach. Finally, we argue that further studies in the subject require methodological advances, combinations of methods, and incorporation of computational solutions to tackle operational constraints.