Background: Following the rapid increase of asylum seekers arriving in the European Union in 2015/16, policymakers have invested heavily in improving their foresight and forecasting capabilities. A common method to elicit expert predictions are Delphi surveys. This approach has attracted concern in the literature, given the high uncertainty in experts’ predictions. However, there exists limited guidance on specific design choices for future-related Delphi surveys. Objective: We test whether or not small adjustments to the Delphi survey can increase certainty (i.e., reduce variation) in expert predictions on immigration to the EU in 2030. Methods: Based on a two-round Delphi survey with 178 migration experts, we compare variation and subjective confidence in expert predictions and assess whether additional context information (type of migration flow, sociopolitical context) promotes convergence among experts (i.e., less variation) and confidence in their own estimates. Results: We find that additional context information does not reduce variation and does not increase confidence in expert predictions on migration. Conclusions: The results reaffirm recent concerns regarding the limited scope for reducing uncertainty by manipulating the survey setup. Persistent uncertainty may be a result of the complexity of migration processes and limited agreement among migration experts regarding key drivers. Contribution: We caution policymakers and academics on the use of Delphi surveys for eliciting expert predictions on immigration, even when conducted based on a large pool of experts and using specific scenarios. The potential of alternative approaches such as prediction markets should be further explored.

中文翻译：

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减少德尔菲调查中的不确定性：欧盟移民案例研究（作者：Rhea Ravenna Sohst、Eduardo Acostamadiedo、Jasper Tjaden）

背景：随着2015/2016年抵达欧盟的寻求庇护者迅速增加，政策制定者投入了大量资金来提高他们的远见和预测能力。得出专家预测的常用方法是德尔菲调查。鉴于专家预测的高度不确定性，这种方法引起了文献的关注。然而，对于未来相关的德尔福调查的具体设计选择的指导有限。目标：我们测试对 Delphi 调查进行小幅调整是否可以提高专家对 2030 年欧盟移民预测的确定性（即减少变异）。 方法：基于对 178 名移民专家进行的两轮 Delphi 调查，我们比较专家预测的变化和主观信心，并评估额外的背景信息（移民流类型、社会政治背景）是否促进专家之间的趋同（即减少变化）和对自己估计的信心。结果：我们发现额外的上下文信息不会减少变异，也不会增加专家对迁移预测的信心。结论：结果再次证实了最近的担忧，即通过操纵调查设置来减少不确定性的范围有限。持续的不确定性可能是由于迁移过程的复杂性以及迁移专家之间关于关键驱动因素的一致意见有限造成的。贡献：我们提醒政策制定者和学者不要使用德尔菲调查来引出专家对移民的预测，即使是基于大量专家并使用特定情景进行的。应进一步探索预测市场等替代方法的潜力。