This is what happens when your discipline values methodology above real insight. If I were doing this analysis, I'd note that the first chart is probably consi

This is what happens when your discipline values methodology above real insight. If I were doing this analysis, I'd note that the first chart is probably consistent with male scores being drawn from one beta distribution (mapped to the interval (1,5)) and female scores being drawn from another. But I'd stop there, and observe that any number of phenomena could generate results like that: the beta distribution is extremely common. To build a better explanatory model, I'd start digging into why people are voting the way they do. This means quite a lot of messy data analysis, and working at the level of individual user records. If you don't have that data there's not much more that can be said. Instead, this guy makes some assumptions without any evidence, and takes the fact that his (complex) model can generate a common distribution as validation for his ideas. Frankly, what bothers me about the article isn't that it's naive and a bit sexist. It's that untrained people can find this sort of analysis persuasive. Because hey, agent-based modelling is complex and impressive, right?