Anticipatory Methods for the Emergence of Radically New Technologies

Abstract

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.