ABSTRACT Futures and foresight research offers mature methods for generating consequences, interpreting their meaning, and converging expert judgment, but rarely integrates them into fully specified protocols that diagnose an emerging technology's legitimacy risks before they materialize. This study develops and applies a Sequential Foresight–Delphi Protocol linking Futures Wheel consequence mapping, Causal Layered Analysis (CLA), and Delphi appraisal through prespecified transition rules, ex ante consensus criteria, a stability‐based stopping rule, and retention of dissensus. The protocol diagnoses anticipatory legitimacy, the prospective justifiability of AI‐enabled public authority, across consequential visibility, institutional responsiveness, and narrative alignment. It was applied in Vietnam, a critical illustrative case of compressed digital transformation, with a Quadruple Helix panel (26 invited; 20 completing every round). The panel anticipated efficiency gains cascading into accountability gaps, profiling, exclusion, and faceless administration; CLA traced the risks to a technocratic worldview and participant‐supplied images condensed as the algorithmic mandarin. Across two original Delphi rounds (urgency, feasibility) and two supplementary rounds (legitimacy‐enhancing potential), the panel converged on impact assessment, public registers, human review, and sandboxes, whereas contestability‐by‐design and independent audit reached consensus on urgency and legitimacy‐enhancing potential (for audit, without meeting the stability criterion) yet did not converge on feasibility, retaining the portfolio's two lowest feasibility ratings. We advance this asymmetry as an open–closed proposition for comparative testing. All claims concern expert‐anticipated consequences and judgments; no causal, predictive, or statistically generalizable claims are made.
ABSTRACT Transforming agri‑food and livestock farming systems to increase their sustainability and climate resilience requires a clear understanding of what drives change and how driving forces are related. This study increases systems knowledge by identifying driving forces and actors that shape European agri‐food and livestock farming systems, examining changes in experts' perceptions over a 5‐year period, and exploring perceived relationships among the driving forces. We conducted semi‐structured interviews and an expert workshop in two research phases and examined the collected data with a qualitative content analysis to identify driving forces, actors, and causal relationships. We then calculated three similarity coefficients to quantify the overlap between reported driving forces in these phases for each engaged organization and performed structural analyses of the empirically derived system diagram. In total, we identified 50 (79) driving forces for European agri‐food (livestock farming) systems, 50%–71% of which were mentioned consistently in both phases. Four driving forces, namely the European import of agricultural commodities, per‐capita‐meat demand, speed of agricultural technology development , and technology uptake , were universally recognized as important by all participating experts in both phases. The study identified two additional promising drivers of change in livestock farming systems: increasing political attention to climate change mitigation and adaptation and citizens' environmental awareness . By integrating diverse perspectives, this study enhances our system‑level understanding of drivers of change in European agri‑food and livestock farming systems. This increased knowledge supports the alignment of agricultural and climate policies that can transform these systems, thus increasing their sustainability and climate resilience.
ABSTRACT This paper introduces the Ubuntu Intergenerational Primer (UIP), a culturally grounded cognitive debiasing intervention designed to mitigate future discounting bias among Real‐Time Delphi (RTD) participants in Southern Africa. Learning from prior research engagements and informed by the region's cultural settings, we innovated around the Rip Van Winkle (RVW) technique to produce a more contextually appropriate debiasing tool. In contrast to the RVW individualistic orientation, the UIP situates the Delphi participant within the collectivist Ubuntu‐Intergenerational relationship. The tool positions a Delphi participant as an ancestor fielding questions about the future to a hypothetical future grandchild living in 2070. Implemented as a pre‐survey cognitive priming exercise, the UIP preceded the main RTD survey that explored the futures of cultured meat in Southern Africa by 2070. The RTD data from 35 participants ( n = 35) were analyzed through the Social Practice Theory (SPT) lens. The findings suggest that the UIP functioned as a cognitive scaffold that extended participant's temporal horizons and hence reduced future discounting bias. From the SPT perspective, the primer influenced the meanings associated with cultured meat more than the materials and competencies as enquiries on legacy, responsibility and obligations to future generations dominate. However, while the primer appeared to mitigate future discounting, it may also have simultaneously introduced kinship and legacy biases by directing focus towards imagined descendants. The study contributes methodologically to futures studies by demonstrating how culturally embedded intergenerational cognitive scaffolding deepens engagement with long‐term futures, contextually to Southern Africa. Overall, the findings suggest that UIP offers a promising mechanism for enriching participatory foresight while recommending some further experimental validation.
