Gaussian and Paretian distributions differ radically. The main feature of the Gaussian distribution . . . can be entirely characterized by its mean and variance . . . A Paretian distribution does not show a well-behaved mean or variance. A power law, therefore, has no average that can be assumed to represent the typical features of the distribution and no finite standard deviations upon which to base confidence intervals . . .
Cuando no hay independencia, se producen realimentaciones:
Gausian distributions tend to prevail when events are completely independen of each other. As soon as you introduce the assumption of interdependence across events, Paretian distributions tend to surface because positive feedback loops tend to amplify small initial events.
Hay que empezar a pensar de otra forma
... a Paretian world requires a much more dynamic view of the world, one that looks for patterns in evolving relationships, rooted deeply in context, and that understands how these changing patterns reshape who we are as well as our opportunities for growth.
Lo he visto (de casualidad) en The Power of Power Laws.