Type A Scenarios: The Challenge of Continuity

In one of yesterday’s posts I introduced the scenario approach used in the 4 Steps model, which is based on the three very basic patterns of change we see in the world.  Those patterns are continuity, incremental change, and abrupt change, and by varying them just a bit, we create four basic “types” of scenarios:

Type A: Continuity (more of the same)

Type B: Incremental change, low disruption

Type C: Incremental change, high disruption

Type D: Abrupt change

As I point out in the book, while many scenario methods (and their practitioners) are biased towards identifying and describing scenarios that feature high levels of disruption, in fact in real life high levels of continuity is often the order of the day, no matter what aspect of life you are examining.  To account for that, and to acknowledge that good experts at change (read: professional futurists) need to be able to explain and anticipate historical continuity as well as historical change, the 4 Steps scenario method includes a “Type A” scenario.


Figure 1: Many Scenario Methods are Focused on Disruptive Scenarios

Now, one of the first things readers will likely ask is, “How is a scenario that just features ‘more of the same’ useful?”  The answer is in the process for generating these scenarios, which is more concerned with understanding how and why change happens (or, in this case, doesn’t happen) than it is with the end state itself.

In the 4 Steps model, scenario development is very much a conversation that explores how different forces for change and forces for stability may logically interact to produce the future.  Participants using this approach will have developed a considerable inventory of historical drivers of change, trends, and emerging issues.  The challenge in forecasting a Type A scenario is in logically justifying how, amidst all of the many historical and current drivers for change, things manage to essentially remain unchanged.

When done well, this exercise helps participants become very familiar with the deeper structures of their industry/community/issue and much more aware of the logical ways in which the actors and relationships in their industry interact to produce and forestall change.  And while some might worry that having participants focus on explaining why things won’t change is counterproductive to the “foresight project,” consider that Type A scenarios are built as just one among four scenarios, the other three of which specifically feature change.

In the end, Type A scenarios act as an important reminder of the forces for stability in the world, those things that so often thwart our attempts at creating change and that so often represent the variables that render our optimistic and change-oriented forecasts inaccurate.

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