BONUS: Organizations as Ecosystems — Understanding Complexity, Innovation, and the Three-Body Problem at Work

In this fascinating conversation about complex adaptive systems, Simon Holzapfel helps us understand why traditional planning and control methods fail in knowledge work — and what we can do instead.

Understanding Ecosystems vs. Systems

"Complex adaptive systems are complex in nature and adaptive in that they evolve over time. That's different from a static system." — Simon Holzapfel

Simon introduces the crucial distinction between mechanical systems and ecosystems. While mechanical systems are predictable and static, ecosystems — like teams and organizations — are complex, adaptive, and constantly evolving. The key difference lies in the interactions among team members, which create emergent properties that cannot be predicted by analyzing individuals separately. Managers often fall into the trap of focusing on individuals rather than the interactions between them, missing where the real magic happens. This is why understanding your organization as an ecosystem, not a machine, fundamentally changes how you lead.

In this segment, we refer to the Stella systems modeling application.

The Journey from Planning to Emergence

"I used to come into class with a lesson plan — doop, doop, doop, minute by minute agenda. And then what I realized is that I would just completely squash those questions that would often emerge from the class." — Simon Holzapfel

Simon shares his transformation from rigid classroom planning to embracing emergence. As a history and economics teacher for 10 years, he learned that over-planning kills the spontaneous insights that make learning powerful. The same principle applies to leadership: planning is essential, but over-planning wastes time and prevents novelty from emerging. The key is separating strategic planning (the "where" and "why") from tactical execution (the "how"), letting teams make local decisions while leaders focus on alignment with the bigger picture.

"Innovation Arrives Stochastically"

"Simply by noticing the locations where you've had your best ideas, we notice the stochasticness of arrival. Might be the shower, might be on a bike ride, might be sitting in traffic, might be at your desk — but often not." — Simon Holzapfel

Simon unpacks the concept of stochastic emergence — the idea that innovation cannot be scheduled or predicted in advance. Stochastic means something is predictable over large datasets but not in any given moment. You know you'll have ideas if you give yourself time and space, but you can't predict when or where they'll arrive. This has profound implications for managers who try to control when and how innovation happens. Knowledge work is about creating things that haven't existed before, so emergence is what we rely on. Try to squash it with too much control, and it simply won't happen.

In this segment, we refer to the Systems Innovation YouTube channel.

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