Module 6: Scenarios in the Horizons Foresight Method - Overview

Alternate PDF version: (1 page, 151 KB) 2016-0276-overview-eng.pdf

Module 6: Scenarios and Results

Step 5: Scenarios in the Horizons Foresight Method - Overview

What they are: Scenarios are descriptions of plausible alternative futures. There are many scenario methods,1 including thematic (e.g. high tech, green, feminist, etc.), archetypal (see the Manoa method), the Wiser method (i.e. getting worse, status quo and aspirational), three bears (where the change is small, medium or large), integral analysis (which explores layers of psycho-social change), causal layered analysis (which digs deep into values and worldviews), morphological analysis (where thousands of combinations of elements can be considered), the Field Anomaly Relaxation method and the Shell or Matrix method. The Shell method is the simplest and one of the most common. It is built around two critical uncertainties (e.g. high–low prosperity vs high–low security). Participants deduce the characteristics for each of four futures. Many users become frustrated with the limits of several of the above methods when dealing with complexity. The Horizons Foresight Method combines elements from a number of other scenario methods to create a framework that can accommodate more complexity than most other methods.

Where they fit in the Horizons Foresight Method: Scenarios are started after scanning, system mapping and selection of change drivers are complete. Scenarios take a snapshot of the whole system under different conditions and provide a framework that can incorporate insights from each step into the process.

In the Horizons Foresight Method, scenarios play a variety of roles. They enable a rigorous analysis of how the system could evolve. They are a tool to help participants and non-participants visualize plausible futures. They enable participants to identify challenges and opportunities that current policies and institutions are not prepared to address. Finally, they provide a context to test the robustness of the current assumptions shaping policy and planning (which we collected in the very first step of the process). A number of participants have said that building scenarios helped them develop more robust mental models that improved decision-making in their regular jobs.

Challenges in this step: This scenario method can expand to accommodate a large number of drivers and system elements. Fortunately, each additional component is a linear and not an exponential increase in complexity. However, each additional component does take more time and mental effort, so it is wise to limit the drivers to the most disruptive and the system elements to the ones that provide strategically useful insight.

1 Bishop, P., A. Hines, T. Collins, The current state of scenario development

Scenario methods