Distinguishing complex systems from complicated systems

From: jimruttshow8596

The field of complexity science often distinguishes between complex and complicated systems, a crucial differentiation for understanding how systems behave and how to manage them effectively [00:03:41]. The Cynefin framework, developed by Dave Snowden, categorizes systems as ordered, complex, and chaotic, with further division of ordered systems into “clear” and “complicated” [00:02:46] [00:03:30].

Complicated Systems

A good way to understand complicated systems is to look at the etymology of the word:

• The Latin root of “complicated” is “to unfold” [00:03:51].

Key characteristics of complicated systems include:

• Unfoldability You can unfold something complicated, and it remains the same thing [00:04:01].
• Deconstructible and Reconstructible A complicated system can typically be taken apart and put back together again, because its logic is implicit in the static design [00:04:59] [00:05:05].
• Predictability Humans are generally adept at making things complicated to achieve predictability [00:04:12] [00:04:15].
• Linear Material Causality They typically exhibit linear material causality [00:06:09].
• Boundaries Systems thinking, often associated with complicated systems, defines systems by boundaries, implying they are closed [00:08:09] [00:12:29].

Examples of Complicated Systems

• A lawnmower motor [00:05:34].
• A Boeing 777 airplane [00:05:37].
• An aircraft [00:05:57].

Complex Systems

Conversely, complex systems are fundamentally different:

• Entangled The Greek origin of “complex” means “entangled” [00:03:55]. Something that is entangled is constantly shifting and changing [00:04:06].
• Irreconstructible You cannot take a complex system apart and put it back together again and expect it to work the same way, because much of the information resides in its dynamics [00:05:08] [00:05:14] [00:05:18].
• Non-linear Causality In a complex adaptive system, there is no linear material causality [00:06:07] [00:06:09]. While they have dispositionality and are modulated, they do not have causality in any meaningful sense [00:06:13] [00:06:18]. They do not have easily mapped causality, and even deterministic complex systems can pass through deterministic chaos where cause and effect are practically impossible to map [00:06:25] [00:06:33] [00:06:40].
• Emergence The properties of the whole are always different from the properties of the parts [00:06:47]. Discovering emergence is a key aspect of working with complexity [00:07:10] [00:07:13].
• Open Systems Generally, complex systems are open or at least semi-permeable to an outside [00:11:19] [00:11:49]. They deal with systems that have multiple connections by definition, making them open [00:12:33] [00:12:35]. At the system level, they are not subject to the second law of Thermodynamics, though the overall system of systems might be [00:12:06] [00:12:09].
• Managing Dispositionality With a complex system, one cannot control the output or aim for a specific goal [00:08:35] [00:08:39]. Instead, management focuses on describing the current state and identifying possible next steps [00:08:43] [00:08:45]. One must start journeys with a sense of direction rather than fixed goals [00:09:28] [00:09:30].
• Influencing Emergence By understanding and managing constraints and constructors (types of modulators), one can influence emergence [00:09:34] [00:09:38] [00:10:40]. If enough “magnets” (modulators) can be controlled with real-time feedback, it’s possible to influence the system [00:10:28] [00:10:33].

Examples of Complex Systems

• Bramble bushes in a thicket, where everything is entangled, and unintended consequences are certain [00:04:32] [00:04:36] [00:04:42].
• A human cell [00:05:20].
• An economy [00:05:27].
• Mayonnaise [00:05:59].
• Superconductors, where the emergent property cannot be predicted from individual electron behavior [00:06:48] [00:06:56].
• Human systems [00:12:15].

Implications for Management and Strategy

Misinterpreting a complex system as merely complicated can lead to radical errors [00:04:19] [00:04:21]. For instance, assuming a system operates in the center of a Gaussian distribution (as with Six Sigma) when it actually operates in a power-law or fat-tail distribution (like a crisis) will lead to failure [00:16:55] [00:16:59] [00:17:05].

Instead of traditional planning cycles (like Soviet Russia’s five-year plans), complexity theory suggests mapping the present and identifying adjacent possibles – where the system can go next [01:09:50] [01:10:09] [01:10:47]. This means shifting from outcome management to dispositional management, focusing on influencing the system’s dispositionality so desired outcomes are more probable [01:32:41] [01:19:33] [01:20:46] [01:20:51].