Week 5: Generating Preference Profiles

Welcome back to my blog, this time for week 5. This week, I decided to focus on generating the preference profiles, which, for all intensive purposes, can be considered the ballots. There are many different ways to generate these profiles, so the majority of my time was spent reading journal articles and similar studies to best design my generation methods. Most of the studies that I read, as well as my previous knowledge lead me to choosing a spatial model to generate the preference profiles, as it was determined to be the most realistic generation method. If you are familiar with the political compass, then this model will seem somewhat familiar. In the political compass, there are two dimensions, one representing an economic scale, and the other a social one. Based on the weight and direction of your preferences, you can be placed on the graph.

Source: Political Compass [1]

The spatial generation model leverages this concept by generating points on a graph like this for both candidates and voters. Each voter’s preference is then determined by its proximities to each candidate, with the closest candidate being ranked first, and the furthest ranked last. 

The example above would illustrate a spatial model with two dimensions, an x and y axis. However, there can be additional dimensions, which add complexity and nuance to the simulation. Furthermore, when generating the profiles, the number of candidates and voters need to be specified. The academic articles helped me when deciding which variables to use. 

There was little research similar to mine that had used spatial generation models, instead opting to use a different, more random model. Despite the different generation model, I will still be basing my variables off of them, so that my results can have direct comparisons. Those studies had generated profiles with 4,5,6, and 7 candidates and with voter populations of 100, 200, 300, 400, 500, 600, and 2000. Since they hadn’t used spatial models in their research, I had to look elsewhere to decide how many dimensions my spatial model would use. I ended up finding research claiming that preferences generated with 2,3, and 4 dimensions were most realistic, so I went with those. 

Next week, I need to program the functions to truncate the profiles, but after that I’ll be able to generate the data and start learning from it. Hang tight!

1. https://www.politicalcompass.org/crowdchart2