When you compare the heartbeat of an animal, like a elephant, and compare it to a human, or a mouse, it very clearly lands on a straight line when charted logarithmically against the body mass (kg) of that animal.
In fact, this is true for more than just heart rate. It is also true for metabolism.
Beyond that, moving from animals and body weight to cities and population, or companies and employees, similar relationships appear to exists against other metrics you might define those entities by. For example, cities produce predictably more patents against the scale of their population, while businesses produce predictably more income against the number of workers employed.
These are just a few of the predictable scaling relationships we see throughout the universe. While there is yet to be discovered, or defined, universal laws that predicts how phenomena grow, organize and develop, the commonality of these relationships strongly suggests that there may be one.
Connections
Link Explanation: The linked note above discuss the desire for people to try to fit the complex world into simplistic frameworks that they understand. This can be a pretty big problem if your attempt to find a pattern, or fit reality into your predefined framework, causes you to believe something is there when it is not.
Part of me feels this is the case with the entire thesis of this book. I don’t feel I am knowledgable enough to mount an intelligent critique of the argument, but I do feel there many be a causation error being made. These scaling patterns fall on different scaling values (slope of the line), meaning that they scale differently. In the case of the company to income example, income and asset value is a very opinionated value. The value of net income in any given year of a business, for example, depends on both the GAAP rules, and the judgement of the accountant and there is significant room for manipulation or error. This means that the net income of one business is not necessarily comparable to the net income of another, especially if they operated in different industries. This is not a refutation that he is seeing a scaling phenomenon in the data, but rather that the underlying assumption beneath the analysis doesn’t make sense in the first place.