Systems with mostly negative feedback tend to be very stable and predictable. Extraneous factors left out of the model can even be absorbed by the actions of the agents, leading to even less noise than we would expect from a prediction relying on the Central Limit Theorem. However, in systems with positive feedback, we loose some predictability. Small differences can build upon themselves and create large differences, making precise prediction difficult.
