system
Given the following post, respond as if you're commenting on Reddit:
human
When and why do social scientists use log scales

I noticed that in some instances (the popular science talks on why we might not be doomed by Hans Rosling, stick out for me) data like global income distribution is represented using a log scale. 

Now I know that it makes sense to use log scale for doing stuff like plotting the progress of an exponential process. 
But what are other reasons to use one, like in the casw as above?
gpt
Log scales are useful for describing phenomena where changes occur mainly multiplicatively instead of additively.

To construct a silly example, let's say we're playing a gambling game where everyone flips a coin and if they win, they gain $10, otherwise they lose $10. If you wanted to plot the final distribution of winnings, a linear scale is appropriate. If you played for 10 rounds, the largest possible value (if everyone starts at $0) is $100, so you can imagine a histogram with x-ticks for each $10.

If instead, the gambling game had you double your money if you won, and halve your money if you lost, then with 10 rounds if you start with $1 then the largest possible winnings is 2^10. If you wanted to depict the actual winnings value, again a linear scale is appropriate, but if you wanted to depict the number of win events minus the number of loss events, then a log scale is appropriate, with base 2. In a log scale, the largest x-tick would be 10, representing 10 wins and 0 losses.

Many real-world phenomena increase/decrease multiplicatively rather than linearly (ie, many real-world phenomena are described well by exponential functions), and log scale plots are useful for depicting multiplicatively changing phenomena when you care more about the number of "change events" rather than the actual value. It turns out this is often the case, so log-plots are quite useful.