25 October 2014

Final predictions by ward

As promised, here is a ward-by-ward breakdown of our final predictions for the 2014 mayoral election in Toronto. We have Tory garnering the most votes in 33 wards for sure, plus likely another 5 in close races. Six wards are “too close to call”, with three barely leaning to Tory (38, 39, and 40) and three barely leaning to Ford (8, 35, and 43). We’re not predicting Chow will win in any ward, but will come second in fourteen.

Ward Tory Ford Chow Turnout
1 41% 36% 23% 48%
2 44% 34% 22% 50%
3 49% 31% 20% 51%
4 50% 31% 19% 51%
5 49% 32% 19% 50%
6 46% 33% 21% 50%
7 43% 36% 21% 49%
8 39% 39% 22% 47%
9 42% 37% 21% 50%
10 45% 35% 20% 50%
11 40% 36% 24% 49%
12 40% 36% 23% 49%
13 55% 13% 32% 49%
14 48% 17% 35% 47%
15 43% 36% 21% 50%
16 57% 29% 14% 50%
17 43% 33% 24% 49%
18 47% 16% 37% 47%
19 48% 15% 36% 45%
20 49% 16% 36% 44%
21 56% 12% 32% 49%
22 57% 12% 31% 48%
23 45% 34% 21% 48%
24 48% 33% 20% 50%
25 55% 30% 14% 50%
26 42% 23% 35% 49%
27 52% 14% 34% 46%
28 48% 17% 35% 47%
29 46% 21% 33% 50%
30 52% 14% 34% 48%
31 42% 23% 35% 49%
32 57% 12% 31% 49%
33 45% 35% 20% 49%
34 46% 34% 21% 50%
35 38% 41% 21% 49%
36 44% 37% 19% 50%
37 41% 38% 21% 50%
38 40% 39% 21% 49%
39 40% 39% 21% 50%
40 41% 39% 20% 50%
41 41% 38% 21% 50%
42 41% 38% 21% 48%
43 40% 40% 21% 50%
44 49% 35% 16% 50%

Final predictions

Our final predictions have John Tory winning the 2014 mayoral election in Toronto with a plurality 46% of the votes, followed by Doug Ford (29%) and Olivia Chow (25%). We also predict turnout of at least 49% across the city, but there are differences in turnout among each candidate’s supporters (with Tory’s supporters being the most likely to vote by a significant margin - which is why our results are more in his favour than recent polls). We predict support for each candidate will come from different pockets of the city, as can be seen on the map below.


These predictions were generated by simulating the election ten times, each time sampling one million of our representative voters (whom we created) for their voting preferences and whether they intend to vote.

Each representative voter has demographic characteristics (e.g., age, sex, income) in accordance with local census data, and lives in a specific ‘neighbourhood’ (i.e., census tract). These attributes helped us assign them political beliefs – and therefore preferences for candidates – as well as political engagement scores that come from various studies of historical turnout (from the likes of Elections Canada). The latter allows us to estimate the likelihood of each specific agent actually casting a ballot.

We’ll shortly also release a ward-by-ward summary of our predictions.

In the end, we hope this proof-of-concept proves to be a more refined (and therefore useful in the long-term) than polling data. As the model becomes more sophisticated, we’ll be able to do scenario testing and study other aspects of campaigns.

10 October 2014

Making agents

The first (and long) step in moving towards agent-based modeling is the creation of the agents themselves. While fictional, they must be representative of reality – meaning they need to behave like actual people might.

In developing a proof of concept of our simulation platform (which we’ll lay out in some detail soon), we’ve created 10,000 agents, drawn randomly from the 542 census tracts (CTs) that make up Toronto per the 2011 Census, proportional to the actual population by age and sex. (CTs are roughly “neighbourhoods”.) So, for example, if 0.001% of the population of Toronto are male, aged 43, living in a CT on the Danforth, then roughly 0.001% of our agents will have those same characteristics. Once the basic agents are selected, we assign (for now) the median household income from the CT to the agent.

But what do these agents believe, politically? For that we take (again, for now) a weighted compilation of relatively recent polls (10 in total, having polled close to 15,000 people, since Doug Ford entered the race), averaged by age/sex /income group/region combinations (420 in total). These give us average support for each of the three major candidates (plus “other”) by agent type, which we then randomly sample (by proportion of support) and assign a Left-Right score (0-100) as we did in our other modeling.

This is somewhat akin to polling, except we’re (randomly) assigning these agents what they believe rather than asking, such that it aggregates back to what the polls are saying, on average.

Next, we take the results of an Elections Canada study on turnout by age/sex that allows us to similarly assign “engagement” scores to the agents. That is, we assign (for now) the average turnout by age/sex group accordingly to each agent. This gives us a sense of likely turnout by CT (see map below).

There is much more to go here, but this forms the basis of our “voter” agents. Next, we’ll turn to “candidate” agents, and then on to “media” agents.

Happy thanksgiving!