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June 08, 2007

Power laws and all that jazz

With apologies to Tolkien:

Three Power Laws for the Physicists, mathematics in thrall,
Four for the biologists, species and all,
Eighteen behavioral, our will carved in stone,
One for the Dark Lord on his dark throne.

In the Land of Science where Power Laws lie,
One Paper to rule them all, One Paper to find them,
One Paper to bring them all and in their moments bind them,
In the Land of Science, where Power Laws lie.

From an interest that grew directly out of my work chracterizing the frequency of severe terrorist attacks, I'm happy to say that the review article I've been working on with Cosma Shalizi and Mark Newman -- on accurately characterizing power-law distributions in empirical data -- is finally finished. The paper covers all aspects of the process, from fitting the distribution to testing the hypothesis that the data is distributed according to a power law, and to make it easy for folks in the community to use the methods we recommend, we've also made our code available.

So, rejoice, rejoice all ye people of Science! Go forth, fit and validate your power laws!

For those still reading, I have a few thoughts about this paper now that it's been released into the wild. First, I naturally hope that people read the paper and find it interesting and useful. I also hope that we as a community start asking ourselves what exactly we mean when we say that such-and-such a quantity is "power-law distributed," and whether our meaning would be better served at times by using less precise terms such as "heavy-tailed" or simply "heterogeneous." For instance, we might simply mean that visually it looks roughly straight on a log-log plot. To which I might reply (a) power-law distributions are not the only thing that can do this, (b) we haven't said what we mean by roughly straight, and (c) we haven't been clear about why we might prefer a priori such a form over alternatives.

The paper goes into the first two points in some detail, so I'll put those aside. The latter point, though, seems like one that's gone un-addressed in the literature for some time now. In some cases, there are probably legitimate reasons to prefer an explanation that assumes large events (and especially those larger than we've observed so far) are distributed according to a power law -- for example, cases where we have some convincing theoretical explanations that match the microscopic details of the system, are reasonably well motivated, and whose predictions have held up under some additional tests. But I don't think most places where power-law distributions have been "observed" have this degree of support for the power-law hypothesis. (In fact, most simply fit a power-law model and assume that it's correct!) We also rarely ask why a system necessarily needs to exhibit a power-law distribution in the first place. That is, would the system behave fundamentally differently, perhaps from a functional perspective, if it instead exhibited a log-normal distribution in the upper tail?

posted June 8, 2007 10:00 AM in Complex Systems | permalink


I think I'm mainly cautious of power-law distributions because I've seen so many papers that have no clue on statistics (for which the authors should be forced to read your paper for all eternity) but mainly because people then go on to say that their "just so" story is correct, when they've given no consideration to any one of a number of processes that could have led to a power-law or power-law-like distribution... which gets back to your final question.

This seems relevant and rather amusing.

Posted by: Matthew Berryman at June 14, 2007 06:11 AM

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