Complexity Models

Overview

Complexity models help navigate the ambiguity and uncertainty present in projects, which often involve multiple interconnected systems.

This article provides a quick overview of the two commonly used complexity models.

Cynefin Framework

The Cynefin Framework, created by Dave Snowden, is a tool to help people make decisions in different situations based on how clear or unclear the problem is. It has five categories:

1. Clear: The cause and effect are obvious. You know what to do because there are best practices to follow.
2. Complicated: The problem has some unknowns, but experts can analyze it and find solutions. There are multiple correct answers, and you need to study the facts to decide.
3. Complex: The problem is unpredictable, and you don't know the cause and effect upfront. You need to try different approaches, learn from what happens, and adapt your actions based on what you find.
4. Chaotic: The cause and effect are unclear, and the situation is out of control. You need to act quickly to stabilize things, then figure out what to do next.
5. Disordered: The situation is unclear, and you need to break it down into smaller parts to understand it better and apply the right approach.

The framework helps people understand how to respond to different problems by adjusting their approach depending on how complex or chaotic the situation is.

Stacey Matrix

The Stacey Matrix is a decision-making framework that helps organizations determine the appropriate management approach based on the complexity of a situation. It is similar to the Cynefin framework but categorizes problems based on two dimensions:

1. Certainty (or Agreement) – How much is known about the problem and potential solutions?
2. Complexity (or Technical Uncertainty) – How difficult is the problem to solve?

The matrix divides problems into four zones:

1. Simple (Close to Certainty, Low Complexity): Best practices apply, and solutions are well-known (e.g., following standard operating procedures).
2. Complicated (High Certainty, Moderate Complexity): Requires expert analysis and structured decision-making (e.g., engineering projects).
3. Complex (Low Certainty, High Complexity): Solutions emerge through experimentation, learning, and adaptation (e.g., product innovation, agile development).
4. Chaotic (No Agreement, Very High Complexity): Requires immediate action and improvisation before stability can be restored (e.g., crisis management).