I hate to have to do this, but it looks like I’m not going to be able to make it to campus at all tomorrow due to a last-minute kerfuffle. It’s especially a bummer since it was our last scheduled time to meet and now the Granovetter model kind of ends on an anticlimax.

As partial recompense, I offer to you the following extra credit activity: download our Granovetter code from last class, and run it on your system several times. Observe how the percentage of rebels changes over time, and more importantly, how the eventual percentage of rebellious people changes from run to run.

Update the code and produce (1) several (we’ll say several == 5) time series plots of the percentage of rioters over time, for different runs of the simulation, and (2) a plot of a parameter sweep showing the total number of agents (N) versus the final percentage of rioters. (To avoid complexity, you can use a fixed number for “final” percentage — for example, run every simulation for 20 steps, and then just compute the percentage of rioters at the end of those steps. I won’t make you write code to detect the point when there are no additional rebels and stop there.)

Does your parameter sweep align with your intuition? It doesn’t mine! I find the results of this model, and the conclusions from the parameter sweep, puzzling indeed.

To claim these XP (I’ll award up to +15XP for it) send me (where “me” == stephen_at_UMW) your six plots as attachments to an email with subject line “DATA 420 last class activity” no later than next Monday at midnight. In the body of the email, write one paragraph describing the takeaways from your plots and what you learned from the Granovetter model.