Foresight and Horizon Scanning are often regarded as identical concepts with identical models and purposes. But they can be completely different activities, both in and for any kind of prospective activity as well as for strategy purposes. Scanning is no debate and no strategy building. Whereas Foresight is more process‐oriented and always includes a Horizon Scanning phase, Horizon Scanning is rather found at the beginning of any forward‐looking activity and can be an automatized stand‐alone approach for identifying “things to come” ‐ which often means the identification of new science and technology and providing information about them. This paper wants to shed light on some of the confusions in Foresight and Horizon Scanning (HS) that often occur in organizations, among researchers and practitioners being thus of practical and scientific relevance for using an integrated model. The contribution is backed up by a study conducted for the European Commission (Cuhls et al., 2015) conducted to clarify for the Commission’s Foresight department (A6) how the strategic intelligence part of a foresight, the Horizon Scanning, is intertwined with the sense‐making part in similar projects in other parts of the world. The contribution thus starts with a broad definition of Foresight and of Horizon Scanning, explains findings from the study on objectives, methods used and relates the discussion to the EU standard “Foresight Cycle” derived from EFFLA. The conclusions are relevant for decision‐making and strategy processes in the European Commission, for national foresight processes but also for company's and other users.

This contribution explains new variants of Argumentative Delphi surveys that can also be used in sociological research, some examples and the learnings from and limits of argumentative surveys with feedback. Argumentative Delphi surveys are not new. As Christian Dayé explains in his book, the early expert surveys and especially the Delphi surveys used explanations and arguments for exchanging knowledge - but always without direct interation (Dayé, C. (2020). Experts, Social Scientistss and Techniques of Prognosis in cold war of America. Socio-Historical Studies of the Social and Human Sciences, palgrave McMilan, Switzerland:41, see also Cuhls, K. (1998). Technikvorausschau in Japan. Ein Rückblick auf 30 Jahre Delphi-Expertenbefragungen. Physica. [Technology Foresight in Japan]). The very first approaches of Delphi surveys did not only make use of expert knowledge in judging issues under uncertainty or were trying to make accurate predictions with statistical analysis, but there were also tests in groups of students. In some of them, the groups did not only choose and tick boxes, but gave reasons or comments for their judgments. Modern Argumentative Delphi surveys do ask for comments AND use a variety of open questions for adding information to the statistical findings. This way of performing a Delphi survey gets more and more ground and can be analysed in a fast way by new means of text mining and Delphi software tools. But they have their limits - especially as they are very demanding for the participants and the analysts. If many people participate, many arguments are given, and they can quickly go beyond the limits of the participants‘ understanding and their time availability. Some lessons learned from recent Delphi projects are reported. This is closing the cycle to Dayé’s description of expert knowledge inclusion in policy-making - a way of integration of expert opinion without direct interaction.

We contribute to the Responsible Research and Innovation (RRI) literature in two ways: (i) we consider how societal aspects are taken into account in research and innovation (R&I) activities in four fundamentally different scenarios, as opposed to analysing current practices; and (ii) put the emphasis on the political conditions of the interactions among the actors, as opposed to focussing on RRI principles and instruments. In the Kingdom of RRI citizens participate directly in decision-making processes; Fortress Europe depicts a libertarian system; Failed Democracy is a populist regime; while Benevolent Green Eurocrats describes a technocratically coordinated strong state. The scenarios offer novel insights into the nature and repercussions of possible policy problems, that is, efficacy; efficiency; legitimacy of R&I activities; societal involvement; equity; and freedom of research. Meaningful interactions between lay people and professional actors in an innovation system can be safeguarded even in the harshest ideological and political framework.