Someone (my brother) asked me the difference between “complicated” and “complex”. I touched on that in the post “What is Complexity?” Let’s try another way, using the example provided by Michel Cotsaftis in “Complex Systems and Self-organization Modelling“: Dogs herding sheep.
The dog has two strategies in order to herd the sheep to its destination:
- The dog can target each sheep individually, chasing each one of them in the direction of the destination. The dog targets a sheep, and engages into a “bilateral” relationship with that sheep trying to get it home. This will not work (or may take forever) because the speed and direction of each sheep will affect the sheep around it in the herd. That’s why good herding dogs will go to Strategy #2.
- The dog makes moves that affect the herd as a whole, understanding that each moving sheep will affect the other sheep. In so doing, the dog builds a model and projects in its head how the herd will react before it makes a move. Then it may correct or make additional moves until the herd gets home.
As you may have guessed it, the first strategy views the problem as “complicated“, while the second views the problem as “complex”. It is the interaction between the parts (how one sheep moving affects the others) that makes the problem complex.
Take a look at this video to see what I mean: