# CODA
Against Hidden Reductionism: Why Complexity Thinking is Not Systems Thinking
Essay
Jelenko Dragisic
There is a great deal of talk these days about systems thinking. Entire libraries are dedicated to it, workshops branded in its name, consultants selling it as the next evolution in leadership or strategy. It is, at first glance, an attractive proposition. After all, who wouldn’t want to understand the world as a system of interconnected parts, delicately balanced and constantly interacting? Yet, I can’t shake the sense that this surge of enthusiasm masks something deeper and problematic. Systems thinking, at least in how it’s widely practised and sold, feels less like an invitation to think differently and more like an old habit dressed in a new language. It is, in many cases, a form of hidden reductionism.
We need to pause and ask: what kind of thinking is truly required when dealing with complexity? Because what passes as systems thinking often stops at mapping parts and their relations—essentially a sophisticated form of disassembly and reassembly. It is a clever way of returning to familiar ground, where we believe that by breaking things down, naming the parts, understanding their function, and then piecing them back together, we will somehow master the system. It’s comforting, linear, and ultimately false. The mistake, I believe, lies in the assumption that every system can be treated the same way, regardless of its nature. Not every system is complex.
We need to pause and ask: what kind of thinking is truly required when dealing with complexity?
We need a way of thinking and diagnosing that respects the irreducibility of complex systems.
Complicated systems—machines, bureaucratic processes, static
frameworks—lend themselves well to this approach. They have parts, those parts
have functions, and when you tweak them, predictable outcomes follow. But when
we are dealing with complex systems, something fundamentally different is at
play. Complexity is not a matter of scale or number of parts. It is about how
the system behaves—its adaptiveness, its capacity for emergence, its
self-organisation, its resilience. A complex system is not a static object
waiting to be understood by pulling it apart; it is a living, dynamic, evolving
entity, irreducible to its individual components.
This is why I find the popular label “complex adaptive systems” both redundant and misleading. A system that is complex must be adaptive. Adaptivity is not an add-on; it is a defining feature. You cannot describe a complex system without acknowledging its capacity to evolve, respond, reorganise, and surprise. To separate adaptivity out as if it were some optional feature suggests a lack of grasp of what complexity actually entails.
The deeper issue, however, is not merely a matter of terminology. It is about the way we habitually think. Our default mode of understanding is analytical—we carve out the world into pieces, categorise them, attempt to grasp the relationship between those pieces, and assume that by assembling this knowledge, the whole will reveal itself. We approach systems, phenomena, even people, as puzzles to be solved by way of dissection. It’s the same whether we are analysing an organisation, an ecosystem, or an individual human being. We disassemble and then hope to reconstruct.
But what if that very instinct is the obstacle? Complexity thinking, as I see it, demands something far more radical. It requires that we resist, consciously and continually, this urge to break things down into discrete, manageable parts. Because in complex systems, the “parts” are not truly parts. They are not independent units with fixed boundaries. Instead, they are better understood as dimensions, as particular lenses or prisms through which the whole system can be glimpsed.
Take, for example, the usual list of attributes associated with complex systems—emergence, resilience, self-organisation, order, chaos, equilibrium, entropy. The common mistake is to treat these as parts that can be isolated, measured, optimised, or corrected. But they are not parts in any meaningful sense. They are dimensions—ways in which the whole system manifests itself when viewed from a particular angle. They are not separable components that can be removed, studied, and then reinserted. Rather, the whole system is always present, visible differently depending on the dimension through which we choose to observe it.
It’s like looking at a human being and thinking you can understand them by cutting off their hand or isolating their ear. Of course, you can describe the anatomy of a hand or an ear. But what you lose in the process is the recognition that the hand only makes sense as part of a living, breathing, whole person. The hand contains a trace of the whole person; it is not merely an object attached to a body. To truly understand a person through their hand, you must see the entire person in the hand—not as a separate part, but as a dimension through which the person reveals themselves.
This is why I find the popular label “complex adaptive systems” both redundant and misleading. A system that is complex must be adaptive. Adaptivity is not an add-on; it is a defining feature. You cannot describe a complex system without acknowledging its capacity to evolve, respond, reorganise, and surprise. To separate adaptivity out as if it were some optional feature suggests a lack of grasp of what complexity actually entails.
The deeper issue, however, is not merely a matter of terminology. It is about the way we habitually think. Our default mode of understanding is analytical—we carve out the world into pieces, categorise them, attempt to grasp the relationship between those pieces, and assume that by assembling this knowledge, the whole will reveal itself. We approach systems, phenomena, even people, as puzzles to be solved by way of dissection. It’s the same whether we are analysing an organisation, an ecosystem, or an individual human being. We disassemble and then hope to reconstruct.
But what if that very instinct is the obstacle? Complexity thinking, as I see it, demands something far more radical. It requires that we resist, consciously and continually, this urge to break things down into discrete, manageable parts. Because in complex systems, the “parts” are not truly parts. They are not independent units with fixed boundaries. Instead, they are better understood as dimensions, as particular lenses or prisms through which the whole system can be glimpsed.
Take, for example, the usual list of attributes associated with complex systems—emergence, resilience, self-organisation, order, chaos, equilibrium, entropy. The common mistake is to treat these as parts that can be isolated, measured, optimised, or corrected. But they are not parts in any meaningful sense. They are dimensions—ways in which the whole system manifests itself when viewed from a particular angle. They are not separable components that can be removed, studied, and then reinserted. Rather, the whole system is always present, visible differently depending on the dimension through which we choose to observe it.
It’s like looking at a human being and thinking you can understand them by cutting off their hand or isolating their ear. Of course, you can describe the anatomy of a hand or an ear. But what you lose in the process is the recognition that the hand only makes sense as part of a living, breathing, whole person. The hand contains a trace of the whole person; it is not merely an object attached to a body. To truly understand a person through their hand, you must see the entire person in the hand—not as a separate part, but as a dimension through which the person reveals themselves.
Similarly, when we look at resilience or emergence in a
complex system, we are not identifying a detachable component. We are seeing
the whole system as it expresses itself through resilience or emergence. Each
dimension reveals a different face of the system, but always the system in its
entirety. It’s a shift from part-thinking to dimensional-thinking.
This is where I believe most systems thinking falls short. Despite all its language about interconnection and feedback loops, it still operates on the premise that if you understand the parts well enough, the system will surrender its secrets. It assumes that by mapping, analysing, and optimising individual elements, the whole can be controlled or predicted. But complexity is not controllable. It is not something that can be reverse-engineered like a machine.
What is needed instead is a way of thinking—and a way of diagnosing—that honours the irreducibility of complex systems. A diagnostic tool that resists the temptation to dissect and instead invites us to observe how the whole system shifts when viewed through different dimensions. Such a tool would not promise easy solutions or control; rather, it would offer patterns, insights, and relational understanding. It would teach us to look, not to fragment.
In a time when every field seems eager to slap the word “systems” onto their strategies, the real challenge is to avoid falling back into the same reductionist traps under a new banner. Complexity thinking is not a brand of systems thinking. It is something else entirely—a discipline of resisting the fragmentation instinct, and learning instead to engage with wholes as wholes, in all their unpredictability and aliveness.
This is where I believe most systems thinking falls short. Despite all its language about interconnection and feedback loops, it still operates on the premise that if you understand the parts well enough, the system will surrender its secrets. It assumes that by mapping, analysing, and optimising individual elements, the whole can be controlled or predicted. But complexity is not controllable. It is not something that can be reverse-engineered like a machine.
What is needed instead is a way of thinking—and a way of diagnosing—that honours the irreducibility of complex systems. A diagnostic tool that resists the temptation to dissect and instead invites us to observe how the whole system shifts when viewed through different dimensions. Such a tool would not promise easy solutions or control; rather, it would offer patterns, insights, and relational understanding. It would teach us to look, not to fragment.
In a time when every field seems eager to slap the word “systems” onto their strategies, the real challenge is to avoid falling back into the same reductionist traps under a new banner. Complexity thinking is not a brand of systems thinking. It is something else entirely—a discipline of resisting the fragmentation instinct, and learning instead to engage with wholes as wholes, in all their unpredictability and aliveness.
