Hyper-complex systems are those situations that do not conform to the structural and behavioral assumptions required by conventional Systems Thinking and Systems Science methods.
Conventional Systems Thinking methods depend on predicting consequences through modelling causal behaviours. This approach requires that the system being modelled conforms to specific structural assumptions. When real-world situations do not meet these requirements, causal modelling fails to produce reliable predictions.
Conventional Systems Thinking methods rely on tacit assumptions about system structure and behaviour. These assumptions, often implicit rather than explicitly stated, include:
- A system has a definable boundary
- Sub-systems within the system are located inside the boundary
- The structure, ownership, and purposes of sub-systems and the system itself remain constant
- Relationships between the system, sub-systems, and all factors within and outside the system remain constant
- Feedback loops within and outside the system remain constant
- The system does not structurally change over time
- The environment does not structurally change over time
- Events are consequential and follow causal relationships
These assumptions enable conventional Systems Thinking methods to model causal behaviours and predict consequences of interventions. This dependence on structural constancy applies to mechanistic methods such as System Dynamics, Causal Loop Diagramming, Operations Research methods, System of Systems approaches, and Systems Engineering. Equally, softer, more interpretive approaches including Soft Systems Methodology, Critical Systems Thinking, Critical Systems Heuristics, and DASP also require the same assumptions about stable boundaries, constant relationships, and consistent feedback loops to generate meaningful insights about interventions, though this dependence may be less obvious in these more qualitative methods.
Hyper-complex systems violate one or more of these assumptions. When system boundaries shift, feedback loops emerge or dissolve, sub-systems change ownership or purpose, or structural relationships transform, causal modelling loses its predictive power because the causal structure itself is unstable.
Variety Dynamics takes a fundamentally different approach. Instead of predicting consequences through causal modelling, Variety Dynamics focuses on managing the locus of control through variety distributions. This shift from causal prediction to control management allows analysis of situations where causal structures are themselves changing.
Examples:
Hyper-complex - shifting boundaries: A corporate merger where the boundary of "the organization" is actively contested and redefined throughout the process. Different stakeholders have incompatible definitions of system membership. Causal models fail because the system boundary keeps changing. Variety Dynamics analyses changing distributions of control variety rather than assuming fixed system membership.
Hyper-complex - emerging feedback loops: A market where new trading technologies create previously non-existent feedback loops (e.g., high-frequency trading creates price-momentum feedback that didn't exist in manual trading). Causal models become outdated as new feedback mechanisms emerge. Variety Dynamics tracks how variety distributions shift without requiring stable causal structure.
Hyper-complex - structural transformation: An ecosystem undergoing climate-driven regime shift where predator-prey relationships fundamentally reorganise. The causal structure itself transforms. Variety Dynamics analyses the changing topology of variety distributions as relationships transform.
Hyper-complex - ownership changes: A political conflict where control over resources continuously shifts between parties. Causal models cannot hold "who controls what" constant. Variety Dynamics directly models the locus and ownership of power as dynamic variety distributions.
Medical treatment analogy: A medical doctor observed that a patient's body is capable of creating more powerful therapeutic compounds than the doctor can prescribe, and can deliver and manage them far more effectively than conventional medicine. The doctor's role becomes persuading the body to heal itself, rather than directly causing healing through external intervention. Similarly, Variety Dynamics focuses on changing the locus of control rather than predicting causal outcomes.
Complex but not hyper-complex: An established market with stable participants, known feedback loops, and fixed institutional structures - Systems Thinking methods apply because causal structure remains constant even though mental models fail.
Variety Dynamics in Hyper-Complex Systems:
Variety Dynamics changes the locus of control by changing the distributions of variety. Those intervening gain more power over the situation and its outcomes not by understanding or predicting causal processes, but by reshaping who controls what variety. The causal processes by which outcomes happen are irrelevant to this approach - what matters is controlling the variety distributions.
This is particularly powerful in hyper-complex situations where causal structures are unstable and unpredictable. When you cannot model causal behaviours because the causal structure itself keeps changing, you can still analyse and reshape variety distributions to shift power and control. The intervention works regardless of the specific causal pathways, because it operates at the level of variety topology rather than causal mechanism.
Why this distinction matters:
The vast majority of real-world situations involving organisations, markets, ecosystems, social systems, and political conflicts are hyper-complex - they violate the structural constancy assumptions required by causal modelling methods.
When hyper-complex systems are analysed using Systems Thinking methods that assume structural stability, the predictions fail because the causal structure being modelled is itself changing. Practitioners then lose confidence in modelling altogether, reverting to mental judgement and intuition - which, as Forrester demonstrated, typically produces wrong conclusions and unintended consequences in complex systems.
The methodological progression:
For simple and complicated systems: mental and discursive methods work (Axiom 49)
For complex systems meeting structural assumptions: Systems Thinking causal modelling methods work
For hyper-complex systems: causal modelling methods fail; Variety Dynamics is required
Implications:
Recognising hyper-complexity is essential for avoiding misapplication of methods. Using System Dynamics on a hyper-complex system produces dangerously misleading predictions because the model assumes causal structure constancy that doesn't exist.
Hyper-complex systems require analytical frameworks that can handle:
- Dynamic boundaries and shifting system membership
- Emerging and dissolving feedback structures
- Transforming relationships and ownership
- Structural change as a primary variable, not a parameter
Variety Dynamics addresses these requirements by focusing on managing the locus of control through variety distributions, rather than predicting outcomes through causal modelling. Where conventional Systems Thinking requires fixed causal structures, Variety Dynamics analyses the shifting topology of variety distributions and the changing locus of power and control. The goal shifts from causal prediction to control management.