Using our Agent-Based Model to scenario test the Canadian federal election

As outlined in our last two posts, our algorithm has “learned” how to simulate the behavioural traits of over 2 million voters in Toronto. This allows us to turn their behavioural “dials” and see what happens.

To demonstrate, we’ll simulate three scenarios:

1. The “likeability” of the Liberal Party falls by 10% from the baseline (i.e., continues to fall);
2. The Conservative Party announces a policy stance regarding climate change much more aligned with the other parties; and
3. People don’t vote strategically and no longer consider the probability of each candidate winning in their riding (i.e., they are free to vote for whomever they align with and like the most, somewhat as if proportional representation were a part of our voting system).

Let’s examine each scenario separately:

1 – If Liberal “likeability” fell

In this scenario, the “likeability” scores for the Liberals in each riding falls by 10% (the amount varies by riding). This could come from a new scandal (or increased salience and impact of previous ones).

What we see in this scenario is a nearly seven point drop in Liberal support across Toronto, about half of which would be picked up by the NDP. This would be particularly felt in certain ridings that are already less aligned on policy where changes in “likeability” have a greater impact. The Libs would only safely hold 13/25 seats, instead of 23/25.

From a seat perspective, the NDP would pick up another seat (for a total of three) in at least 80% of our simulations – namely York South-Weston. (It would also put four – Beaches-East York, Davenport, Spadina-Fort York, and University-Rosedale – into serious play.) Similarly, the Conservatives would pick up two seats in at least 80% of our simulations – namely Eglinton-Lawrence and York Centre (and put Don Valley North, Etobicoke Centre, and Willowdale into serious play).

This is a great example of how changing non-linear systems can produce results that are not linear (meaning they cannot be easily predicted by polls or regressions).

2 – If Conservatives undifferentiated themselves on climate change

In this scenario, the Conservatives announce a change to their policy position on a major issue, specifically climate change. The salience of this change would be immediate (this can also be changed, but for simplicity we won’t do so here). It may seem counterintuitive, but it appears that the Conservatives, by giving up a differentiating factor, would actually lose voters. Specifically, in this scenario, no seats change hands, but the Conservatives actually give up about three points to the Greens.

To work this through, imagine a voter who may like another party more, but chooses to vote Conservative specifically because their positions on climate change align. But if the party moved to align its climate change policy with other parties, that voter may decide that there is no longer a compelling enough reason to vote Conservative. If there are more of these voters than voters the party would pick up by changing this one policy (e.g., because there are enough other policies that still dissuade voters from shifting to the Conservatives), then the Conservatives become worse off.

The intuition may be for the defecting Conservative voters discussed above to go Liberal instead (and some do), but in fact, once policies look more alike, “likeability” can take over, and the Greens do better there than the Liberals.

This is a great example of how the emergent properties of a changing system cannot be seen by other types of models.

3 – Proportional Representations

Recent analysis done by P.J. Fournier (of 338Canada) for Macleans Magazine used 338Canada’s existing poll aggregations to estimate how many seats each party would win across Canada if (at least one form of) proportional representation was in place for the current federal election. It is an interesting thought experiment and allows for a discussion of the value of changing our electoral practice.

As supportive as we are of such analysis, this is an area of analysis perfectly set up for agent-based modeling. That’s because Fournier’s analysis assumes no change in voting behavior (as far as we can tell), whereas ABM can relax that assumption and see how the algorithm evolves.

To do so, we have our voters ignore the winning probabilities of each candidate and simply pick who they would want to (including their “likeability”).

Perhaps surprisingly, the simulations show that the Liberals would lose significant support in Toronto (and likely elsewhere). They would drop to third place, behind the Conservatives (first place) and the Greens (second place).

Toronto would transform into four-party city: depending on the form of proportional representation chosen, the city would have 9-12 Conservative seats, 4-7 Green seats, 2-5 Liberal seats, and 2-3 NDP seats.

This suggests that most Liberal voters in Toronto are supportive only to avoid their third or fourth choice from winning. This ties in with the finding that Liberals are not well “liked” (i.e., outside of their policies), and might also suggest why the Liberals back-tracked on electoral reform – though such conjecture is outside our analytical scope. Nonetheless, it does support the idea that the Greens are not taken seriously because voters sense that the Greens are not taken seriously by other voters.

More demonstrations are possible

Overall, these three scenarios showcase how agent-based modeling can be used to see the emergent outcomes of various electoral landscapes. Many more simulations could be run, and we welcome ideas for things that would be interesting to the #cdnpoli community.

Dumbing down voters

In our last post, our analysis assumed that voters had a very good sense of the winning probabilities for each candidate in their ridings. This was probably an unfair assumption to make - voters have a sense of which two parties might be fighting for the seat, but unlikely that they know the z-scores based on good sample size polls.

So, we've loosened that statistical knowledge a fair amount, whereby voters only have some sense of who is really in the running in their ridings. While that doesn't change the importance of "likeability" (still averaging around 50% of each vote), it does change which parties' votes are driven by "likeability" more than their policies.

Now, it is in fact the Liberals who fall to last in "likeability" - and by a fairly large margin - coming last or second last in every riding. This suggests that a lot of people are willing to hold their nose and vote for the Libs.

On average, the other three parties have roughly equal "likeability", but this is more concentrated for some parties than for others. For example, the Greens appear to be either very well "liked" or not "liked" at all. They are the most "liked" in 13/25 ridings and least "liked" in 9/25 ridings - and have some fairly extreme values for "likeability". This would suggest that some Green supporters are driven entirely by policy while others are driven by something else.

The NDP and Conservatives are more consistent, but the NDP are most "liked" in 10/25 ridings whereas the Conservatives are most "liked" in the remaining 2/25 ridings.

As mentioned in the last post, we'll be posting some scenarios soon.

Using Agent-based modeling to explain polls

Modeling to explain, not forecast

The goal of PsephoAnalytics is to model voting behaviour in order to accurately explain political campaigns. That is, we are not looking to forecast ongoing campaigns – there are plenty of good poll aggregators online that provide such estimation. But if we can quantitatively explain why an ongoing campaign is producing the polls that it is, then we have something unique.

That is why agent-based modeling is so useful to us. Our model – as a proof of concept – can replicate the behaviour of millions of individual voters in Toronto in a parameterized way. Once we match their voting patterns to those suggested by the polls (specifically those from CalculatedPolitics, which provides riding-level estimates), we can compare the various parameters that make up our agents behaviour and say something about them.

We can also, therefore, turn those various behavioural dials and see what happens. For example, what if a party changed its positions on a major policy issue, or if a party leader became more likeable? That allows us to estimate the outcomes of such hypothetical changes without having to invest in conducting a poll.

Investigating the 2019 Federal Election

As in previous elections, we only consider Toronto voters, and specifically (this time) how they are behaving with respect to the 2019 federal election. We have matched the likely voting outcomes of over 2 million individual voters with riding-level estimates of support for four parties: Liberals, Conservatives, NDP, and Greens. This also means that we can estimate the response of voters to individual candidates, not just the parties themselves.

First, let’s start with the basics – here are the likely voter outcomes by ridings for each party, as estimated by CalculatedPolitics on October 16.

As these maps show, the Liberals are expected to win 23 of Toronto’s 25 ridings. The two exceptions are Parkdale-High Park and Toronto-Danforth, which are leaning NDP. Four ridings, namely Eglinton-Lawrence, Etobicoke Centre, Willowdale, and York Centre, see the Liberals slightly edging out the Conservatives. Another four ridings, namely Beaches-East York, Davenport, University-Rosedale, and York South-Weston, see the Liberals slightly edging out the NDP. The Greens do no better than 15% (Toronto Danforth), average about 9% across the city, and are highly correlated with support for the NDP.

What is driving these results? First, a reminder about some of the parameters we employ in our model. All “agents” (e.g., voters, candidates) take policy positions. For voters, these are estimated using numerous historical elections to derive “natural” positions. For candidates, we assign values based on campaign commitments (e.g., from CBC’s coverage, though we could also simply use a VoteCompass). Some voters can also care about policy more than others, meaning they care less about non-policy factors (we use the term “likeability” to capture all these non-policy factors). As such, candidates also have a “likeability” score. Voters also have an “engagement” score that indicates how likely they are to pay attention to the campaign and, more importantly, vote at all. Finally, voters can see polls and determine how likely it is that certain parties will win in their riding. Each voter then determine, for each party a) how closely is their platform aligned with the voter’s issue preferences; b) how much do they “like” the candidate (for non-policy reasons); and c) how likely is it the candidate can win in their riding. That information is used by the voter to score each candidate, and then vote for the candidate with the highest score, if the voter chooses to vote at all. (There are other parameters used, but these few provide much of the differentiation we see.)

Based on this, there are a couple of key take-aways from the 2019 federal election:

• “Likeability” is important, with about 50% of each vote, on average, being determined by how much the voter likes the party. The importance of “likeability” ranges from voter to voter (extremes of 11% and 89%), but half of voters use “likeability” to determine somewhere between 42% and 58% of their vote.
• Given that, some candidates are simply not likeable enough to overcome a) their party platforms; or b) their perceived unlikelihood of victory (over which they have almost no control). For example, the NDP have the highest average “likeability” scores, and rank first in 18 out of 25 ridings. By contrast, the Greens has the lowest average. This means that policy issues (e.g., climate change) are disproportionately driving Green Party support, whereas something else (e.g., Jagmeet Singh’s popularity) is driving NDP support.

In our next post, we’ll look at some scenarios where we change some of these parameters (or perhaps more drastic things).