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Complexity reigns

Writer's picture: Alessandro FontanaAlessandro Fontana

In the realm of small and medium enterprises, a common belief is encapsulated in the phrase "Chaos reigns.”


This perception suggests that businesses often find themselves in a state of disarray, where order seems elusive. However, a more precise articulation for many of these enterprises might be, "Complexity reigns."


This nuanced shift in language underscores a critical distinction: chaos involves articulated but predictable interactions among components, while complexity suggests a lack of order and predictability, hindering the ability to adapt and evolve.


The most intriguing difference between chaos and complexity lies in the predictability of emergence. Neil Theise, in his book Notes on Complexity, delves into this topic with great depth, making it a must-read for those fascinated by the subject.


He introduces a foundational concept early in the book: a chaotic system is one where, given the same initial conditions, the system will consistently generate the same emergent properties. As Neil puts it, “the whole is predictably greater than the sum of its parts."

The butterfly effect serves as an excellent example of this phenomenon, demonstrating how small changes can lead to predictable outcomes in a chaotic system.


In contrast, complexity is characterized by the whole being unpredictably greater than the sum of its parts. This is unpredictability of life itself, where human interactions and relationships lead to outcomes that cannot be easily foreseen. In complex systems, the interplay of numerous interacting components creates a dynamic environment where emergence is not only unexpected but also continually evolving, mirroring the intricate and often surprising nature of human experiences.

A complexity-focused HCD exploration toolkit - Hopefully, one step closer

Mapping complex systems is no simple task.

To navigate these systems effectively, we need tools and theories that help us uncover and interpret their hidden structure. By leveraging theories like ABM, CAS, Cellular Automata, and Chaos Theory, and by applying visual tools like Causal Loop Diagrams, we can begin to map these intricate systems.

We are firmly in the realm of experimentation, using well-established and widely adopted HCD methodologies, enriched with small elements of innovation that, over the years and through accumulated experience, are necessarily adapted by every skilled designer (like me and my colleagues). The goal is to explore topics that, by their very nature, cannot be “reduced” to something simple without risking oversimplification. What follows is a reflection on how what we already know might be leveraged to deepen our understanding—one step closer to developing a complexity-focused HCD exploration toolkit.

As Human-Centered Designers we can leverage few methodological “tools” to make work this out.

HCD Note 
A “What’s Going On?” mapping, where participants write down all the system elements they interact with on sticky notes. Group these on a large board into clusters (e.g., “Processes,” “Tools,” “People”), allowing participants to identify where unpredictability emerges.

A critical starting point is identifying the components or agents within the system.

By focusing on the individual agents and their interactions, ABM allows us to understand how localized behaviors can scale into system-wide phenomena. These same interactions when studied through computational models, reveal a layer of emergent behavior that is central to understanding complexity.

HCD Note
Run a Role Mapping exercise. Have participants identify key “agents” in the system (e.g., users, employees, tools, processes) and sketch their roles on a map. Use arrows to draw relationships and add quick notes about how each “agent” influences others. This visual interaction-based map replicates the core of ABM but in an intuitive, accessible format.

Emergence, as explored in Complex Adaptive Systems (CAS) theory (Holland, 1992), underscores how the whole is often unpredictably greater than the sum of its parts. CAS highlights the dynamic interplay among agents, where interactions create patterns and structures that evolve over time. This perspective is essential for understanding systems where adaptability and evolution are at the core of their behavior.

HCD Note
Facilitate a Journey Mapping session, focusing on patterns rather than linear steps. Encourage participants to map how different elements of their system “adapt” to changes over time. Use storytelling to highlight how small shifts in one part of the system lead to broader transformations, mirroring the emergent properties of CAS.

For practical exploration of such dynamics, computational tools like Cellular Automata provide a compelling approach. These models simulate how simple rules at the local level can lead to complex patterns globally, illustrating the power of emergence in a tangible way.


Cellular Automata also emphasize the interconnectedness of agents, demonstrating how minor changes can ripple through a system to produce unforeseen results.

HCD Note
Introduce a “Ripple Effects” brainstorming activity. Present participants with a small hypothetical change (e.g., introducing a new policy or tool) and have them collaboratively predict how this change would cascade through the system. Use colored sticky notes to distinguish immediate impacts, secondary effects, and long-term outcomes.

The role of unpredictability is particularly well-illustrated by Chaos Theory, which explores how small changes in initial conditions can lead to vastly different outcomes (Lorenz, 1963).

The butterfly effect, a hallmark of chaos, reminds us that even in systems that seem chaotic, underlying patterns can sometimes be discerned if we know where to look. This insight is particularly valuable in mapping scenarios and understanding the potential trajectories of a complex system.

HCD Note
Run a Scenario Design Game. Divide participants into small groups and give them a single starting condition. Ask each group to propose different ways the system could evolve from that point, emphasizing unpredictability. Reconvene to compare results and discuss the potential “chaos-driven” paths.

Visualization tools like Causal Loop Diagrams and Influence Maps, derived from Systems Dynamics (Forrester, 1961) -  here’s an interesting article from McKinsey on this topic, offer practical methods for representing the intricate relationships within a complex system. These diagrams help identify feedback loops and interaction chains, making it easier to see how individual components and their relationships drive the system’s overall behavior.

HCD Note
Lead a Feedback Mapping workshop. Ask participants to draw out feedback loops they observe in their system. Use a simple structure: arrows for cause-and-effect relationships, with “+” or “–” signs to show reinforcing or balancing feedback. Encourage participants to refine the map collaboratively.

As Neil Theise explains in Notes on Complexity, chaotic systems produce predictable emergent properties under consistent conditions. Complexity, by contrast, is less structured, producing emergent outcomes that are often surprising and continually evolving.

HCD Note
Conclude with a “Path Forward” session. Using the visual tools created during the workshop, have participants identify actionable insights and prioritize opportunities for intervention. Create a shared visual roadmap to transition from complexity mapping to decision-making.


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