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Blog Series: Part 5

How Far in the Future? Determining The Timeframe of Anticipatory Methods

Posted on 1st of December 2023 by Sara Marcucci, Stefaan Verhulst

How Far in the Future? Determining The Timeframe of Anticipatory Methods
How Far in the Future? Determining The Timeframe of Anticipatory Methods

Migration policy, characterized by its multifaceted challenges, presents a unique terrain where anticipatory methods can offer valuable insights. Globally, governments, international organizations, and research institutions are actively involved in devising strategies to anticipate and comprehend migration patterns. However, the practical effectiveness and relevance of these methods exhibit significant variations when applied in real-world scenarios.

This blog is dedicated to navigating the intricacies of anticipating migration for policy by exploring their timeframe of anticipation within our list of methods (see our previous blog for the list). By doing so, it aims to capture the complexities and variations inherent in the practical implementation of these methods. 

It's important to note that the evaluation of the timeframe of anticipation for these methods is context-dependent. The same method can indeed be used for both short-term and long-term timeframes, in specific cases. The scores provided here are general assessments and may vary based on specific contextual factors. 

Defining Timeframe of Anticipation

The timeframe of anticipation reflects the method’s aim to make accurate anticipation within different time horizons, ranging from short-term to long-term anticipations. Scores from 1 to 5 were given based on the method's temporal range, its ability to potentially adjust anticipations in response to evolving conditions, and its strategic outlook:

  • Short-Term Anticipations 

    • 1 = Immediate or Backward Focus: The method is geared towards the shortest term, offering insights for immediate future scenarios. It may not extend significantly beyond the near future.

    • 2 = Near-Term Emphasis: The method focuses on the near-term, providing insights for the immediate and short-term future. It addresses tactical planning needs with a limited outlook.

  • Medium-term Anticipations

    • 3 = Moderate Horizon: The method encompasses a moderate temporal range, offering insights into both medium- to long-term anticipations. It aligns with both tactical and strategic planning needs.

  • Long-term Anticipations

    • 4 = Long-Term Perspective: The method takes a longer-term perspective, offering insights across short, medium, and long-term anticipations. It aligns well with strategic planning needs, covering a broad temporal spectrum.

    • 5 = Far-Reaching Vision: The method has a far-reaching temporal range, seamlessly providing insights that span the shortest to the longest term. It aligns seamlessly with both immediate and long-term strategic planning needs, offering a comprehensive outlook.

Exploring Timeframe of Anticipation

Understanding the temporal dimensions of anticipatory methods can be essential for effective selection of the appropriate method to employ based on the specific circumstances. In Table 1 below, we present a heatmap representing how far in the future the selected methods look, from lighter (shorter term) to darker (longer term) shades of blue.

In the presented heatmap, the arrangement of anticipatory methods is based on an observed tendency for their application in investigating specific timeframes. However, it is crucial to note that this placement does not restrict the inherent capabilities of each method. Rather, it reflects a general trend identified through analysis and observation of:

  • The Repository of Cases:

    • Scores were allocated based on the inclusion of diverse use cases within the repository of cases associated with the method. It is important to note that the repository represents a limited sample and may not comprehensively reflect the entire method. The scores for the timeframe of prediction acknowledged that the evaluation extended beyond the specific cases included in the repository.

  • The Temporal Orientation as Described in the Literature:

    • Evaluation of how the methods are characterized in the literature with respect to their temporal orientation. Higher scores are assigned to methods that are more consistently described as having a forward-looking perspective, extending further into the future. Conversely, lower scores are given to methods characterized in the literature as having a more immediate or short-term focus. 

Anticipatory methods often possess a degree of versatility and can be applied across various time frames. This heatmap serves as a visual representation of the prevalent usage patterns found in literature and practical applications, and it is not prescriptive of the exclusive temporal scope for each method.


Simulation and Modeling

Participatory Action Research

Focus Group Discussions

Delphi Method

Horizon Scanning

Environmental Scanning

Expert Panels

Red Teaming

Risk Assessment

System Dynamics Modeling

Wildcards Analysis

Narrative Interviews

Citizen Panels

Stress Testing

Innovation Workshops

Scenario Planning

Futures-creative Models

Early Warning Systems

Expert Interviews

Technology Roadmapping

Trend Analysis

Weak Signal Analysis

Science Fiction Narratives

Counterfactual Analysis

SWOT Analysis

Morphological Analysis

Cross-Impact Analysis

Game Theory


Table 1: Heatmap of Timeframe of Prediction of Selected Methods.

  • Short-Term Anticipations:

    • Definition: Short-term anticipations in the realm of anticipation for migration refer to anticipating trends and patterns that are expected to unfold in the immediate to near future. In our research, this encompasses all methods generally used or suited for looking at a timeframe of anticipation within the next month to three years. In this research, this involved methods with a timeframe score of 1 (Immediate Focus) and 2 (Near-Term Emphasis).

    • Example Method – Simulation and Modeling: Simulation and modeling involve creating mathematical or computational models that simulate the behavior of complex systems over time. These models consider various current parameters, providing a systematic analysis of potential short-term outcomes. An innovative example, for instance, lies in the utilization of an adaptive machine learning algorithm that seamlessly integrates administrative statistics and non-traditional data sources on a large scale, specifically tailored for forecasting asylum applications within the European Union (EU). This algorithm stands out for its real-time monitoring of key drivers in both countries of origin and destination, enabling the early detection of changes in migration patterns. It models individual migration flows between countries separately on moving time windows, incorporating lagged effects for a more comprehensive analysis. Notably, the algorithm delivers forecasts of asylum applications up to four weeks ahead, facilitating timely decision-making and resource allocation. Furthermore, its dynamic assessment of how patterns of drivers evolve over time provides nuanced insights into the functioning and changes within migration systems.

  • Medium-term Anticipations:

    • Definition: Medium-term anticipations extend the horizon to a timeframe that spans several years. In our research, this covers methods looking at anticipating trends and scenarios in three to ten years. This allows for more strategic planning and adaptability to gradual shifts in migration dynamics. In this research, this involved methods with a timeframe score of 3 (Moderate Horizon).

    • Example Method – Scenario Planning: Scenario planning involves creating plausible future scenarios to anticipate how migration patterns might evolve. Usually, this method proves to be particularly relevant to plan for scenarios over the medium and long term. Decision-makers can use this method to explore a range of potential outcomes and develop strategies that are adaptable to different scenarios. For example, the Mixed Migration Centre (MMC)/Rabat Process Secretariat conducted a scenario planning exercise looking at potential scenarios in 2030. This exercise aimed to test a new tool and offer insights into the scenario-building process, facilitating strategic futures analysis for improved anticipation of change, identification of blind spots, and effective planning on mixed migration. The scenario planning exercise provides insights into potential migration scenarios over the medium term, allowing decision-makers to develop strategies that consider various plausible outcomes within the next three to ten years.

  • Long-term Anticipations:

    • Definition: Long-term anticipations extend even further into the future. In our research, this includes methods looking at a timeframe of anticipation spanning beyond a decade. This timeframe is crucial for developing strategies that address more profound and transformative shifts in migration patterns. In this research, this involved methods with a timeframe score of 4 (Long-Term Perspective) and 5 (Far-Reaching Vision).

    • Example Method – Futures-Creative Model: Futures-creative models are imaginative thinking exercises that stimulate innovative solutions by exploring unconventional future scenarios. They can be conducted using tools such as futures wheel, futures artifacts, and machine learning, and encourage participants to explore often far-reaching futures based on present circumstances. For instance, the Forum for the Future conducted a futures wheel exercise to analyze the potential ramifications of the ongoing Russian invasion of Ukraine in the long term. This exercise primarily focused on interconnected systems and signal mapping to anticipate consequences and intervention opportunities. The futures wheel exercise provided insights into the long-term consequences and intervention opportunities related to the war in Ukraine, identifying potential impacts on migration patterns, supply chains, and food systems.

In our next blog, we will define and explore the level of maturity of anticipatory methods, so as to inform decision makers on how to choose and employ each method based on their respective levels of advancement. 

If you haven’t already, check out the previous blogs on the blog series webpage and stay tuned as we uncover innovative approaches to address the complexities of migration!

We would like to express our gratitude to members of the BD4M Alliance Anna Rosinka (JRC) for their valuable reviews of this piece before its publication.

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