This image illustrates a crucial difference in predictability between single-factor and multi-factor systems.
In the Sequential (Serial) model:
- Each step (A→B→C→D) proceeds independently without external influences.
- All causal relationships are clearly defined by “100% accurate rules.”
- Ideally, with no other associations, each step can perfectly predict the next.
- The result is deterministic (100%) with no uncertainty.
- However, such single-factor models only truly exist in human-made abstractions or simple numerical calculations.
In contrast, the Parallel model shows:
- Multiple factors (a, b, c, d) exist simultaneously and influence each other in complex ways.
- The system may not include all possible factors.
- “Not all conditions apply” – certain influences may not manifest in particular situations.
- “Difficult to make all influences into one rule” – complex interactions cannot be simplified into a single rule.
- Thus, the result becomes probabilistic, making precise predictions impossible.
- All phenomena in the real world closely resemble this parallel model.
In our actual world, purely single-factor systems rarely exist. Even seemingly simple phenomena consist of interactions between various elements. Weather, economics, ecosystems, human health, social phenomena – all real systems comprise numerous variables and their complex interrelationships. This is why real-world phenomena exhibit probabilistic characteristics, which is not merely due to our lack of knowledge but an inherent property of complex systems.
With Claude
Lechuck Park 