Data Peasant

A mathematical model predicting 2020 political instability


To think about this year, I've taken to Peter Turchin. Turchin is a "mathematical historian", who has observed cyclical patterns in political instability. Take America: around 1870, there was a wave of assassinations during the Civil War Reconstruction. Fifty years later, we had the first Red Scare, and turbulent class warfare (such as the Battle of Blair Mountain. Fifty years later, we saw the civil rights and anti-war movements. Fifty years later brings us to now.

Turchin created a toy model to "predict" these cycles of instability. The model has three parts: a mass mobilization term, an elite mobilization term, and state fiscal distress.



Mass mobilization is inversely proportional to wages (w), and directly proportional to the size (Nurb) and age (A) of the urban population.


Elite mobilization is inversely proportional to income (epsilon) and the number of elite positions (E), and directly proportional to the number of elites clamoring for those positions (sN).


State fiscal distress is inversely proportional to GDP (G), and directly proportional to the state debt, as well as public distrust in the state.

From this model, you can generate a lot of hot takes about why the past couple of years have been so insane. One of my own hot takes is the following: the masses mobilize through protest, the elites mobilize through "cancel culture". People talk about cancel culture as if it is classic mob mentality, but I think I disagree. The clearest instances of cancel culture have occurred in journalism, academia, tech, entertainment, and politics. And they seem to occur in the most elite portions of these market sectors: NYTimes, Yale, Google, etc.

If we apply Turchin's EMP formula, we basically have a problem of elite overproduction, driving down elite income, leading to ... vindictive online behavior.

Whats even more sad is that there are instances of random nobody's getting laser beamed off the internet by a "mob". But nobody is paying attention to random nodes on the network, until a highly connected node is feeling a little frisky. The elites are to blame. They are getting notoriety and respect for it.