This is the fourth article in my 8-week series exploring foundational systems thinking concepts, written for the 2025 RE-AMP Systems Thinking Academy. Whether you’re following along with the series or diving deep into systems practice, this article explores a framework for understanding different types of complexity and choosing approaches that fit the conditions you’re navigating.
While the dynamics of a Complex Adaptive System (CAS) reveal the fundamental forces at play, the Stacey Matrix offers a complementary lens. It provides a visual and conceptual framework for understanding the nature of complexity within human systems — particularly when it comes to decision-making and collective action.
The Stacey Matrix plots situations along two distinct but interrelated dimensions:
- Agreement (Social Dimension): The degree of consensus among stakeholders about what needs to be done. Low agreement signals diverse perspectives, values, and priorities — calling for relational skills like dialogue, perspective-taking, and facilitation.
- Certainty (Technical Dimension): The degree of predictability in outcomes based on cause-and-effect understanding. Low certainty indicates technical complexity, requiring expertise, experimentation, and exploratory learning.
By mapping situations along these social and technical axes, we gain insight into the nature of the system’s complexity — and how we might best engage it.
Understanding these dynamics matters because while we cannot directly control outcomes in a CAS, we can shape the conditions that influence a system’s trajectory. The Stacey Matrix serves not as a recipe book for action, but as a pattern-sensing guide — helping us match our engagement to the living context we are entering.
The Matrix and Its Zones
Simple (High Agreement, High Certainty)
Stable situations where cause-and-effect is clear, problems are well-defined, and solutions are widely agreed upon. A gear-logic approach (top-down planning, standard procedures) can be effective here — because the system conditions are predictable and directly linked.
Socially Complicated (Low Agreement, High Certainty)
Solutions may be clear and predictable, but divergent values, politics, or power dynamics make the situation turbulent. The relational field is complex and collaboration is difficult. Conditions for success require not just technical expertise but attention to building trust, fostering dialogue, and navigating the relational field.
Technically Complicated (High Agreement, Low Certainty)
There is broad alignment on goals, but uncertainty about how to achieve them. The technical landscape is complex and the pathways forward are not fully known. Conditions for success involve gathering diverse expertise, encouraging experimentation, strengthening information flows, and coordinating across boundaries. Progress emerges through iterative learning and adaptive exploration, not predefined plans. Even with agreement on the “what,” the “how” must be discovered through interaction with a shifting environment.
Complex (Low Agreement, Low Certainty)
Conditions are unpredictable and fluid, shaped by dynamic relationships and emergent patterns. There is little consensus on goals, little clarity on cause-and-effect. Success relies on cultivating environments for continuous learning, strengthening connections, navigating tensions with care, and supporting experiments that reveal emerging possibilities. Here, creating conditions means deepening trust, amplifying feedback loops, sensing into the evolving field, and embracing adaptive action. Solutions arise not from planning, but from relational responsiveness to what is unfolding.
Chaotic (Very Low Agreement, Very Low Certainty)
Conditions are unstable and volatile, with no clear patterns of cause-and-effect and no shared understanding of goals or priorities. In chaos, relationships fragment, information flows break down, and actions tend to become reactive or panicked. Creating conditions in chaos requires immediate stabilization: taking decisive action to create enough safety, coherence, and anchoring for more adaptive strategies to become possible. Prolonged chaos risks collapse. The task is not to impose control, but to calm turbulence enough for connection, learning, and resilience to re-emerge.
Complexity Within Systems
Living systems are rarely uniform. Within any system, different parts may exist in different states of stability and complexity — sometimes even pulling in different directions.
Healthy systems don’t aim for uniform conditions. They weave a living balance: pockets of coherence and stability provide anchoring and resilience, while zones of fluidity and exploration allow for adaptation, renewal and surprise.
Navigating complexity, then, is not about forcing the whole system toward a single state. It’s about sensing what each part needs: where stability nourishes life, where emergence must be tended, and where space must be opened for new patterns to unfold.
Why this matters when working with complex systems
The Stacey Matrix reminds us that effective stewardship is relational. It invites us to ask:
- Where might we strengthen anchoring conditions?
- Where might we open space to invite emergence?
- Where might we simply listen more deeply to what the system itself is longing to become?
Working skillfully with living systems means learning to dance between these dynamics — with patience, discernment, and care.
🔍 Appendix C: Understanding Framework Variations
Before we dive into examples, it’s important to know that there are several frameworks that explore similar territory, including the Human Systems Dynamics (HSD) Landscape Diagram and Dave Snowden’s Cynefin Framework. Each emerged from early systems thinking research and offers valuable ways of seeing complexity. For our purposes, however, we work primarily with the original Stacey Matrix. It most clearly distinguishes between technical complexity (low certainty) and social complexity (low agreement) — a distinction that is crucial for the kind of relational, systemic work we are undertaking.
🔍 Appendix D: Examples of System States in the Stacey Matrix (Impact Network Context)
Simple Example:
Implementing a standardized reporting template across organizations where both the value and the process are well agreed upon.
Socially Complicated Example:
Rolling out a set of equitable engagement principles where technical best practices are clear, but diverse cultures and power dynamics create resistance.
Technically Complicated Example:
Building a shared online knowledge platform: agreed upon in principle, but requiring exploration and expertise to develop effectively.
Complex Example:
Shifting entrenched community norms around a social issue, where causes are debated and outcomes are unpredictable.
Chaotic Example:
Responding to an external crisis (natural disaster, sudden policy change) where rapid, stabilizing action must precede collaboration and adaptation.
How do you recognize different types of complexity in your own work? I’m curious about situations where you’ve experienced the tension between social and technical complexity, or moments when you’ve had to shift your approach based on the conditions you were facing. Share your examples in the comments below.
Next week: Article 5 introduces the HSD Theory of Change — the bridge from understanding complexity to engaging it through Adaptive Action and Pattern Logic.