SAVE Simulated Spatial Models

Serial Approval Vote Election

Serial Approval Vote Election (SAVE)

Spatial Models

The third representation is a spatial model in which individual voter ideals and proposed motions are represented as positions in an issue space. Each voter v prefers closer motions over those further away. An example of a spatial model with 100 voters and 3 motions is shown in Figure 1.

Try it out yourself

Control description ,,fold,,

The spatial model control:

  • Cycle elections : randomly generate an electorate, where the voter distribution is a uniform distribution with an added constraint to coerce a strong majority cycle.
  • Uniform electorate : Randomly generate an electorate using an unconstrained uniform distribution.
  • Normal electorate : Randomly generate an electorate using a normal distribution.

Once an electorate has been generated, you can select and then drag any voter ideal or motion to a new position.

The spatial model, with tournament, Copeland scores, and average distance

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Figure 1: One possible spatial model generating the original cycle preference profile over 3 motions and 100 voters seen in the Profiles explorable. This spatial model uses an unweighted Euclidean metric.

Things to look for in this simulation ,,fold,,

Spatial models represent voter ideals and motions as positions in an issue space, which in this simulation is a 2-dimensional Euclidean field. This representation of the electorate has a lot going on. I'll start by pointing out a few basics.

  1. Each electorate is randomly generated. The two main electorate distributions are uniform and normal. In a uniform distribution the voters are equally likely to appear anywhere in the field. In a normal distribution, the voters are more likely to be near the center and less likely to be near the edges. The cycle elections are severely constrained in order to generate the extreme preference profile cycle you see with the Original cycle button in Preference Profiles: Figure 1. What I find interesting about the Cycle elections is how many of them look almost reasonable, as if they could show up naturally.
  2. While the voters are generated randomly, the initial motion are not. In any real voting situation, which is what I'm trying to model, every single choice we have is proposed by someone. If I want to propose an motion and I don't know what anyone else wants, I'd probably just propose my ideal motion. I expect anyone else would do the same. Accordingly, each of the three motions are always the ideals of the first three randomly generated voters. (Which is why each of the motion circles is centered on a voter ideal circle.
  3. Each of the lines is the perpendicular bisector of a line segment between a pair of motions: A and B, A and C, and B and C. If a voter is on the A side of the AB line, that voter considers A to be closer to their own ideal than B is, and will thus voter for A over B when given that choice. The fact that it is very unlikely for a voter to be exactly centered on a line is one justification for people working with preference profiles to ignore ties.
  4. Speaking of preference profiles, each region bounded by the lines corresponds to a single preference order. If a voter is on the A side of the AB line, the C side of the AC line, and the C side of the BC line, the voter has the preference order C≻A≻B. These lines also show that some preference profiles are simply not possible in certain electorates, particularly when the three lines do not intersect within the frame.
  5. In addition to the majority decisions, tournament digraph, and Copeland scores, the spatial model lets us introduce a new metric: average distance. The field is a 400x400 grid. The average distance score is simply the average distance between a motion and the voters in the electorate. The order of the Average Distance scores tends to agree with the order of the Copeland scores. But this metric introduces a new concept that is simply not modeled in tournaments or preference profiles. The average distance is a measure of how good an motion is for the electorate. The lower the average distance the better the motion is.

So far, we've just looked at static metrics. But spatial models allow for exploring ideas in many ways that just aren't possible with tournaments or preference profiles. For example, let's do a quick experiment on what our metrics tell us.

Pick an electorate, any electorate. Now move motion C out of the way in the emptiest bottom corner, and move "A" as far as it will go to the top left corner and B as far as it will go to the top right corner. Focus on just A and B. Your model should be in one of three states: A beats B, A loses to B, or they tie. If it's a tie, just pick one, otherwise move the originally loosing motion about a quarter of the way across the top, directly toward the other motion. The moving motion just picked up more votes. Note the vertical line midway between A and B, As that line crosses voters, they change their preference between A and B and the moving motion picks up another vote.

If you push it close enough, the moving motion might be able to pick up all the votes and get a 100:0, totally unanimous victory. It is also possible, if the motions are close but not exactly aligned, for the vote to swing wildly and end up anywhere between total victory and total defeat.

Now, return your moving motion to its original corner and look at the average distance scores for A and B. It's very likely that the winning motion has a better average distance score. Do the same slow move toward the other motion, but this time focus on the average distance score of the moving motion. What you'll see is the moving motion's average distance score starts decreasing (getting better) until somewhere in the middle where it flattens out, and then as you continue moving to the other motion the average score increases (gets worse) until the two motions coincide and get the same average distance score.

A second exercise is to start with any electorate and its three motions. The idea is to pick a losing motion and move it until it is a Condorcet winner (beating both of the other motions). Then repeat the process with one of the new losers. Continue this and see where you end up. It might help to look at the distance scores.

Other things to note are that when you move voters, their votes do not change until they cross a line. When you move any motion, however, two of the lines move and can easily change many votes. If you move one motion directly toward another motion, the line between them stays in the same orientation, and the moving motion will pick up some or all of votes from voters initially between the two motions. If you move one motion toward the other but do not do so directly, it is possible that the line between them will change slope. Changing the slope of the line can mean gaining some votes on one side and losing voter on the other.

Date: 2019-10-30 Wed 00:00

Author: Thomas Edward Cavin

Created: 2025-08-14 Thu 21:19

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