Complexity Basics — Part I: When Simplicity Kills (Not Only in Business)
Complexity Basics — Part I: When Simplicity Kills (Not Only in Business)
The Cynefin framework and its domains
1. Starting point for this four-part series on complexity basics
In my recent WAITS posts on Medium, I’ve linked VUCA (Volatility, Uncertainty, Complexity and Ambiguity) challenges to Business Process Management (BPM), which also includes multiple ways of automating business processes.
However, both offline and online the following questions came up:
- Question 1: Is VUCA outdated so that we need new acronyms / models such as Jamais Cascio’s BANI (Brittle, Anxious, Non-Linear and Incomprehensible) framework?
- Question 2: Is the reference to complexity in general and VUCA in particular rather trivial because we‘ve always lived in such environments? Do you remember, for example, the alpha predators that hunted down our hominid brethren millions of years ago? Thus, there is nothing special or new about it.
To answer these two questions, I put on the glasses of social complexity research, especially Niklas Luhmann’s theory of social systems, and draw on historical data (see, in particular, the charts from Our World in Data), because neither common sense nor time-diagnostic sensitivities lead far in this context.
The topics of this and the other three posts are then:
- Part I: The Cynefin framework and its domains.
- Part II: The distinction of different systems / domains (simple, complicated, complex, chaotic and completely random).
- Part III: The increase of social, media and technological complexity in social evolution, especially in modernity (since the end of the 18th / the early 19th century).
- Part IV: A critique of the BANI framework.
2. The Cynefin framework and its domains
Cynefin (Welsh for habitat and pronounced kuh-NEV-in) is a popular sense- and decision-making framework, developed by Dave Snowden in 1999, which distinguishes the following problem domains (see D.J. Snowden / M.E. Boone, A Leader’s Framework for Decision Making, published in the Harvard Business Review in November 2007):
2.a. Simple contexts
Simple contexts have the following characteristics (Note: In later versions of the Cynefin framework, simple was first changed to obvious, then to clear, see the graphic above):
- Stable and clear cause-and-effect relationships.
- Lay people can understand them.
- Domain of the known knowns.
- Decisions are obvious.
- Leaders can sense, categorize and respond using best practices.
- Leadership style: Command-and-control works fine.
- Business example: Order processing and fufillment.
2.b. Complicated contexts
Complicated contexts have the following features:
- Cause-and-effect relationships are more difficult to understand.
- Experts / specialists are needed.
- Domain of the known unkowns.
- Decisions are not obvious because several options have to be explored.
- Leaders should sense, analyze and respond relying on experts and good (but not best) practice.
- Leadership style: (Agile) Management 3.0 à la Jurgen Appelo, for instance, should work fine here.
- Business example: An existing (complicated) product such as an Enterprise Content Management (ECM) software has to be customized for specific customer segments.
2.c. Complex contexts
Complex contexts have the following characteristics:
- Cause-and-effect relationships can’t be understood or only in retrospect because there are too many causes, effects and positive / negative feedback loops.
- Domain of the unknown unkowns.
- Leaders should probe, sense and respond by shifting into an experimental-creative mode that focuses on emergent practices / patterns.
- Leadership style: (Agile) Management 3.0 in combination with test-and sprint-driven approaches such as Lean Startup / Running Lean, Business Model Canvas, Design Thinking, etc. should work in these contexts.
- Business examples:
Complex contexts are normal for innovative startups because they have to deal with an abundance of complexity, often in three dimensions concurrently:
- Temporal: How does the right timing look like?
- Social:
Who are our potential customers?
And is there even a market for our solution?
Who are our open and latent competitors?
Who are our potential investors?
etc.
- Factual:
What should the product as a solution to the customer’s pain points ultimately look like, even if we don’t know exactly what our potential customers want?
Which functional / non-functional features should it have?
etc.
However, complex situations aren’t limited to innovative startups, but are normal for many organizations due to continual perturbations in both the organization itself and its organizational environment:
Most situations and decisions in organizations are complex because some major change — a bad quarter, a shift in management, a merger or acquisition — introduces unpredictability and flux. [in: D.J. Snowden / M.E. Boone (2007), A Leader’s Framework for Decision Making].
2.d. Chaotic contexts
Chaotic contexts have the following features:
- Cause-and-effect relationships are impossible to understand because everything happens too fast and turbulence prevails.
- Domain of the unknowables.
- Decisive action is imperative.
- Leaders should act, sense and respond while focusing on novel practice.
One excellent technique is to manage chaos and innovation in parallel: The minute you encounter a crisis, appoint a reliable manager or crisis management team to resolve the issue. At the same time, pick out a separate team and focus its members on the opportunities for doing things differently. [in: D.J. Snowden / M.E. Boone (2007), A Leader’s Framework for Decision Making.].
- Leadership style: Direct and top-down for the crisis management team, but maybe an Agile Management approach for the separate team which focuses on innovation and opportunities.
- Typical examples: Catastrophes such as 9/11.
2.e. (Realm of) Confusion:
Here it is unclear which of the problem domains described above apply in a particular situation and how to proceed from there.
A good response in this case is, therefore, to first gather more information and then decide which problem domain(s) are most likely.
2.f. Conclusion
Every leader and manager should know the Cynefin framework because it makes them aware that not every leadership, decision-making and communication style is suitable for every context.
In addition, it’s usually fatal to try to solve complex problems with an analytical, deterministic and linear way of thinking, which is primarily suited for simple and complicated problem domains.
Instead, systemic / systems thinking and its methods, interactions with experimental character and (agent-based) simulations, etc. in combination with a higher degree of internal complexity according to Boisot / McKelvey’s law of requisity complexity and William Ross Ashby’s law of requisite variety are far better suited for dealing with complex problems, since the latter can’t be analytically decomposed and reduced to simple solutions as well as are characterized by nonlinearity and indeterminism.
With this background knowledge, we can now take a look at different system types (see part II of this series).
Thanks for reading and, hopefully, see you in the next post!
Author for WAITS Software und Prozessberatungsgesellschaft mbH, Cologne, Germany: Peter Bormann — September 2023.
