Week 2: Finishing the Probability Model

Hello, and welcome back to my blog where I will be detailing the progress I made on my senior project. Last week, I discussed how I had run into a minor roadblock, requiring me to create my own probability model for random ballot truncation. I had also mentioned that there were a few ways that I could’ve constructed the model, which I will detail below:

1. The first method that I had in mind was using the ballot data from every election I downloaded to calculate how often voters ranked each proportion of candidates(ie. 20% of voters ranked 50% of the candidates). This method was appealing because it would allow me to create a single, all-encompassing model based on the most data possible. However, it made a major assumption that made me uncomfortable. In an election with 4 candidates, it would be much more common for a voter to rank all the candidates versus in an election with 12 candidates. This would result in skewed data, which would affect the strength of the results.
2. To address this concern, the second option was to create separate probability models for elections with different numbers of candidates(ie. An election with 4 candidates will have a different model than one with 6 candidates). The one downside of this method is that each probability model will be based on less empirical data than the universal one that would’ve been produced from the first option.

After considering both of these methods, I decided to go with the second option. I figured that even though the models would use less data, they would still be based on enough to mimic realistic voter actions. Additionally, previous research that I had read designed their probability models for elections with a specific number of candidates. The probability models are now finished for every candidate length available, and I will choose . Next week, I will be working on developing the rest of the aspects required for the simulation. Stay tuned!