Scenarios allow for the consideration of possible future situations given key critical uncertainties influencing systems change. They have the potential to informing decision-making by exploring a variety of possible futures around a core issue. They explore the most uncertain and unexpected futures and can stretch thinking around risks and challenges in the future.  Since they examine all possible futures, including desirable ones, they can help ensure that all vulnerabilities are explored and accounted for. Therefore, in situations and contexts involving a high degree of change, uncertainty, and complexity, scenarios can drive discussions, and provide a medium for informing choice in future decision making. See below for the different types of scenarios that can be created:

Reference scenarios: Sometimes also referred to as “predictive scenarios”, they set out to address the question “what is expected to happen?” and include forecasts as well as what-if analyses.

Explorative scenarios: Attempt to map “what can or might happen?” and explore what future developments may be triggered either by exogenous driving forces (developments that are external and cannot be influenced by the decision makers in question), by endogenous driving forces (developments that are internal and can be influenced by decision makers), or by both.

Normative scenarios: Sometimes referred to as “anticipatory scenarios.” Aim to illustrate “how can a specific target be reached?” or “how might a specific threat be avoided?” and thus include both backcasting studies and planning exercises.

Problem-focused scenario exercises centre on the factors shaping future developments and usually emphasize the product rather than the process.

Actor-centric exercises focus on the relationship of specific actors to their environment and primarily see scenarios as a basis for strategic conversations (particularly in an organizational learning context).

Reflexive interventionist scenario processes are developed around the interactions between various actors and their environment (and vice versa) with the aim to inform action learning (especially in a public policy context).

Uncertainty and complexity can be used to define ways of exploring how uncertain we are about future developments of key drivers; and how well we understand the complexity of the system and its causalities. While there are a wide range of methodologies that can be used for developing scenarios depending on context, three are presented on the left, and the figure on the right locates scenarios with respect to uncertainty and complexity:

Two Axes: These are illustrative rather than predictive and tend to be high-level (with the possibility of adding more detail). They are best suited for testing the robustness of medium to long-term policy direction. They tend to look out 10-20 years.

Branch Analysis: These are suited to developing scenarios around specific and known turning-points (e.g. elections) These look out 5 years.

Cone of Plausibility: These offer a more deterministic model of the way in which drivers lead to outcomes, by explicitly listing assumptions and how these might change. These suit contexts with limited drivers. They look out a few months to 2-3 years.

Scenarios lie somewhere in between forecasts and speculations, that is, when the degree of uncertainty and complexity is of an intermediate level. Scenarios therefore have an exploratory character.