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Updated on September 16, 2025

Article 4: The Stacey Matrix

Estimated reading: 7 minutes 87 views Contributors

Understanding Contexts for Creating Generative Conditions

This is the fourth article in an 8-week series exploring foundational systems thinking concepts that deepen your social system mapping practice. Originally written for the 2025 RE-AMP Systems Thinking Academy, and published in my blog these concepts help you understand and work more effectively with the living systems you’re mapping.
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.
For sumApp users: Understanding where your mapping project sits on the Stacey Matrix helps you choose the right approach for facilitating conversations, interpreting data, and supporting system change. Different types of complexity require different facilitation strategies and different ways of engaging with your findings.
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.
In mapping practice: Your stakeholders agree on what needs to be mapped and why. The relationships are straightforward to identify and categorize. You can use standard mapping processes and expect predictable insights.

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.
In mapping practice: The technical aspects of mapping are clear, but stakeholders have different views on what should be mapped, who should be included, or how the results should be used. Success requires relationship-building and careful attention to power dynamics in your mapping process.

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.
In mapping practice: Everyone agrees on the importance of mapping the system, but the relationships are complex and hard to categorize. You need to experiment with different relationship types, gather diverse perspectives on how connections work, and allow your mapping framework to evolve as you learn more about the system.

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.
In mapping practice: The system you’re trying to map is in crisis or rapid transition. Relationships are unstable, stakeholders are overwhelmed, and normal mapping processes feel irrelevant. You may need to focus first on creating basic stability and trust before any systematic mapping can begin.

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 for your mapping practice

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?
Applied to sumApp projects:
  • Assess the complexity of your mapping context before designing your process
  • Match your facilitation approach to the type of complexity you’re facing
  • Recognize when technical mapping challenges require social solutions (and vice versa)
  • Adapt your timeline and expectations based on the complexity conditions
  • Use different engagement strategies for different stakeholder groups based on their complexity context
  • Know when to proceed with standard processes vs. when to embrace experimentation
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.
Continue the series: Follow the complete 8-week series to deepen your understanding of the systems thinking that makes social system mapping truly transformational.
Next: Article 5 introduces the HSD Theory of Change — the bridge from understanding complexity to engaging it through Adaptive Action and Pattern Logic.

 

Article 4: The Stacey Matrix - PreviousArticle 3: Complex Adaptive Systems: Patterns & Paradigms Naturally ShiftingNext - Article 4: The Stacey MatrixArticle 5: Navigating Complexity — The HSD Theory of Change

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