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.