Week 9: Analyzing the data. For real this time.

Hello readers, and welcome back to my blog! This week I picked up from where I was after being thrown off course from last week’s debacle. And luckily, my pace has returned to normal and I am back on track.

Throughout the week I worked on more carefully analyzing the data produced from the simulation. With over 840,000 preference profiles generated, there was a lot of data to sort through. The first thing that I wanted to do was isolate each variable (number of candidates, dimensions, and voters). This would allow me to pinpoint how each of them affected the elections and the voting methods’ abilities to select their true winning sets.

Controlling each of these variables produced interesting results, especially in regard to my previous research. In my previous research in which ballots were still force truncated, but completely filled, the number of voters and candidates significantly impacted the performance of each of the voting methods. On the other hand, in this research project, those variables had a much weaker effect on the results.

Beyond isolating those variables, I created graphs to be used both in my final deliverable as well as my final presentation. I also took note and explored other interesting trends that caught my eye, which will all be recorded and described in the final research paper that I produce. 

Analyzing the data this week was a lot of fun for me because I got more practice with conducting data analysis with python, a skill that I intend to hone further in the future. 

I now have all the information I need to finalize my research paper and presentation, which will be the tasks for next week. See you then!