17 October 2019

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).



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