Anticipating the emergence of radically new technologies poses significant methodological challenges due to high uncertainty surrounding their development and diffusion. Conventional forecasting approaches, which rely on stable relationships and historical data extrapolation, are often ill-suited to such conditions. This editorial examines how different anticipatory methods address uncertainty and what this implies for method selection in technology foresight. Drawing on four case studies—quantum technologies in healthcare, fusion energy, defense technologies, and the emergence of technology clusters—the special issue compares horizon scanning, scenario planning, Delphi-based expert elicitation, and computational weak-signal analysis. Using an emerging-technology framework that treats uncertainty as a defining and evolving attribute rather than a temporary knowledge gap, the editorial shows that method suitability depends on the nature and degree of uncertainty; the time horizon becomes meaningful only under specific uncertainty conditions. Foresight methods that structure exploration across multiple plausible futures remain applicable across uncertainty contexts, whereas forecasting is conditionally applicable and depends on predominantly epistemic uncertainty. The comparison further demonstrates that each method has structural limitations, underscoring the need for strategic combinations under higher uncertainty. By positioning uncertainty as the central organizing principle for methodological choice, this editorial contributes to futures and foresight research and offers guidance for designing anticipatory approaches that remain robust under radical uncertainty.

Organizational and political responses to strategic surprises such as the credit crunch in 2008 and the pandemic in 2020 are increasingly reliant on scientific insights. As a result, the accuracy of scientific models has become more critical, and models have become more complex to capture the real-world phenomena as best as they can. So much, so that appeals for simplification are beginning to surface. But unfortunately, simplification has its issues. Too simple models are so generic that they no longer accurately describe or predict real-world cause-effect relationships. On the other hand, too complex models are hard to generalize. Somewhere on the continuum between too simple and too complex lies the optimal model. In this article, the authors contribute to the ongoing discussion on model complexity by presenting a logical and systematic framework of simplification issues that may occur during the conceptualization and operationalization of variables, relationships, and model contexts. The framework was developed with the help of two cases, one from foresight, a relatively young discipline, and the other from the established discipline of innovation diffusion. Both disciplines have a widely accepted foundational predictive model that could use another look. The shared errors informed the simplification framework. The framework can help social scientists to detect possible oversimplification issues in literature reviews and inform their choices for either in- or decreases in model complexity.

The effect of ambiguous and non-salient information on the managerial interpretive process is rarely investigated, although this information is considered to be the basis for strategic decision-making and requires a different process due to its ill-defined nature. This paper explores the process that outperforming managers used to detect and interpret weak signals. Interviews with 13 top-managers were analyzed using a grounded theory approach to allow the process to emerge. The findings confirmed earlier conclusions on perceptual filters that reduce the number and type of signals assessed. Contrary to earlier findings, the process was significantly altered to compensate for this loss in two ways. First, managers deliberately postponed their assessment of a weak signal by discussing it with people with distinct and second, some managers deliberately searched for signals that were unfamiliar and not fitting their mental model. The sample consisted of outperforming top managers, which raised questions about the generalizability of the findings in specific directions: are the alterations in the weak signal process exemplary for outperformers only, and how does its success compare to formal foresight methods. Finally, the findings have managerial relevance since they indicate how to detect and bring weak signals into the company before impact.