Scalability is a key element of complexity science. Many complex systems tend to be selfsimilar across levels—the same dynamics work at multiple levels. They are explained by scaling laws. Scalability is a key element of complexity science. Many complex systems tend to be selfsimilar across levels—the same dynamics work at multiple levels. They are explained by scaling laws. Scalability results from what Mandelbrot calls fractal geometry. A cauliflower is an obvious example. Fractals often show Pareto distributions and are signified by power laws. Researchers find organization-related power laws in intrafirm decisions, consumer sales, salaries, size of firms, movie profits, director interlocks, biotech networks, and industrial districts, for example. Power laws signify Pareto distributions, which show “fat tails,” (nearly) infinite variance, unstable means, and unstable confidence intervals. Pareto distributions are alien to most quantitative organizational researchers, who are trained in Gaussian statistics and are trained to go to great lengths to configure their data to fit the requirements of linear regression, normal distributions, and related statistical methods.


Via Alessandro Cerboni