The way predictions are made is changing. Data scientists are competing with traditional statisticians, and Big Data analysis is competing with the study of statistic samples. This change mirrors a wider paradigm shift in the conception of society and what rules its structural dynamics. In order to understand this change, you need to know the power law.
If facts happen randomly in a two-axis world, it’s quite possible that they will distribute as in a Gaussian bell curve, with most happenings concentrated around the average. But if facts are interlinked and co-evolve so that a change in one quantity results in a proportional change in the other quantity, it’s more probable that they will distribute as in a power-law graph—in a ski-jump shape, in which the average isn’t important and polarization is unavoidable.
The distributions of a large set of phenomena observed in physics, biology, and astronomy follow a power law, and this kind of curve was much discussed when it was applied to the understanding of the Internet. Study of the number of links to specific Web pages soon demonstrated that some pages were attracting more links, and that with the growth of the network it was more and more likely that new pages would link to those pages. In such a network, some nodes became hubs and other pages were only destinations. The number of links to pages followed the power law, and one could predict that the dynamics of the network would bring about a polarization of resources, as in the Barabási–Albert model, an algorithm invented by Albert-László Barabási and Réka Albert. This kind of understanding has consequences.
As the Internet became more and more important for society, the network theory became part of the very notion of social dynamics. In a network society, the power law is becoming the fundamental pattern.
In social sciences, prediction has often been more a kind of shaping the future than a description of what will actually happen. That sort of shaping by predicting has often relied on the assumptions used in the predicting process: Predicting something that will happen in a society relies on an idea of society. When scholars shared the assumptions defined in the notion of “mass society” (mass production, mass consumption, mass media, in which almost everybody behaves the same, both at work and when consuming), in their view fundamental characters were the same and diversity was randomly distributed. Gauss ruled. In a mass society, most people were average, different people were rare and extreme, thus society was described by a bell curve—a Gaussian curve, the “normal” curve. Polls based on statistic samples could predict behaviors.
But in a network society the fundamental assumptions are quite different. In a network society, all characters are linked and co-evolve, because a change in a character will probably affect other characters. In such a society, the average doesn’t predict much, and scholars need a different fundamental pattern.
The power law is such a fundamental pattern. In this kind of society, resources aren’t random; they co-evolve and they polarize. In finance, as in knowledge, resources are attracted by abundance. The rich get unavoidably richer.
Understanding this pattern is the only way for a network society to oppose inequality without looking for solutions that were good in a mass society. Bernardo Huberman, a network theorist, has observed that the winner takes all in a category—that is, in a meaningful context. For example, the best search engine wins in the search-engine category but not necessarily in the whole of the Web, thus not necessarily in the social-network category. In such a network, innovation is the most important dynamic to oppose inequality, and real competition is possible only if new categories can emerge. If finance is only one big market, then the winner takes all. If rules ensure that different banks can play only in different categories of financial services, then there’s less concentration of resources and less global risk.
In a mass society, everything tends to go toward the average: The middle class wins, in a normal distribution of resources. In a network society, resources are attracted by the hub, and differences inevitably grow. The mass society is an idea of the past, but the network society is a challenge for the future.
The power law can help our understanding of, and maybe correct the dynamics of, networks, given a growing awareness of its fundamental patterns. Predictions are narratives, and good narratives need some empirical observations. Moore’s law is useful to those who share the technocentered narrative of the exponential growth of computer abilities. The power law is useful to those who want to critically study the evolution of a network.