 It seems clear that Kuhnian revolutions are bifurcations in the nonlinear dynamical sense, and it seems plausible to think that Kuhnian revolutions may have a fat-tailed or power-law distribution (or worse) when their size is plotted over time on an appropriate scale. Each of these features is a “hallmark of nonlinear dynamics” (Hooker 2011A, 5; 2011B, 850, 858). To elaborate a bit: one intriguing suggestion coming from work in nonlinear dynamics is that scientific changes may be like earthquakes and many other phenomena (perhaps including punctuated equilibrium events of the Gould-Eldredge sort as well as mass extinction events in biology) in following a power-law distribution in which there are exponentially fewer changes of a given magnitude than the number of changes in the next lower category. For example, there might be only one magnitude 5 change (or above) for every ten magnitude 4 changes (on average over time), as in the Gutenberg-Richter scale for earthquakes. If so, then scientific revolutions would be scale free, meaning that large revolutions in the future are more probable than a Gaussian normal distribution would predict. Such a conclusion would have important implications for the issue of scientific realism.