What is the difference between a complicated and complex system?
Based on the discussion, the participants distinguish between complicated and complex systems primarily through the contrast between mechanical/predictable systems and organic/unpredictable systems.
The following sections detail the specific differences highlighted in the text:
1. Mechanical vs. Organic (Nature of Order)
Angus Jenkinson provides the most detailed differentiation, arguing that the two belong to a “different order of order” and should not be conflated[1].
• Complicated Systems: These are mechanical or inorganic systems, such as a car engine. While they may have many factors and parts, they can ultimately be analyzed and understood by an expert through the study of those parts[2],[1]. They are solvable through analysis[1].
• Complex Systems: These are organic systems, such as the human body, a painting, or an organization[1]. They cannot be fully comprehended by analyzing their parts as if they were mechanical components because they possess a quality of “aliveness” or aesthetic wholeness that is lost during reductionist analysis[1].
Jenkinson argues that a major failure in systems thinking is treating complexity as if it were merely complicated. By treating an organic system (like a heart or a company) as a machine, we may gain a false sense of control, but we apply the “wrong mindset for complete understanding”[1].
2. Causal Structures and Predictability
Jim Scully and Douglas Elias differentiate the two based on their logic structures and predictability.
• Logic Structure:
    ◦ Complicated (Systems Thinking): Scully argues that traditional systems thinking typically deals in “causes-and-effects,” attempting to map interrelationships[3].    ◦ Complexity: Complexity science deals in “effects-and-effects.” Scully suggests that living, self-organizing systems have infinite interdependencies (the Butterfly Effect) that make them theoretically impossible to diagram or predict fully[4],[3]. • Predictability:
    ◦ Complicated: These systems are “mechanical, finite, closed-loop logic systems” that can be predictive[5].    ◦ Complex: These systems are “evolving” and “open-loop.” They demonstrate emergent behavior—new states not seen before in the behavior space of the system—which cannot be obtained using predictive logic[5],[6].
3. Control and Boundaries
Gerrit Van Wyk, referencing Sidney Dekker, distinguishes the two based on how we draw boundaries and our ability to exercise control.
• Complicated: When we take a “hard systems approach,” we assign tight boundaries around a problem. This simplifies a problem into one that is merely “complicated,” allowing us to feel we can solve or control it (e.g., finding a broken elevator screw as the cause of a crash)[6].
• Complex: A “soft systems approach” assumes porous boundaries. Van Wyk argues that if humans are involved, all problems are inherently complex. Crucially, he notes that while you can control a hard (complicated) system, “you cannot control a soft [complex] system,” and believing otherwise is a delusion[6].
4. Summary Table of Distinctions
| Feature | Complicated System | Complex System |
|---|---|---|
| Metaphor | Machine / Car Engine[2] | Organism / Human Body / Painting[1] |
| Analysis | Can be analyzed by experts[2] | Cannot be fully grasped by analyzing parts[1] |
| Logic | Causes-and-Effects[3] | Effects-and-Effects[3] |
| Control | Controllable[6] | Uncontrollable / Self-Organizing[4],[6] |
| Behavior | Predictive[5] | Emergent and Evolving[5] |
References
[1] What are the Problems with Systems Thinking.pdf [2] What are the Problems with Systems Thinking.pdf [3] What are the Problems with Systems Thinking.pdf [4] What are the Problems with Systems Thinking.pdf [5] What are the Problems with Systems Thinking.pdf [6] What are the Problems with Systems Thinking.pdf