A number of people have been asking whether we are going to analyze the upcoming federal election on October 19, like we did for the Toronto mayoral race last year. The truth is, we never stopped working after the mayoral race, but are back with a vengeance for the next five weeks. We have gathered tonnes of new data and refined our methodology. We have also established a new domain name: psephoanalytics.
The first (and long) step in moving towards agent-based modeling is the creation of the agents themselves. While fictional, they must represent reality β meaning they need to behave like actual people. The main issue in voter modeling, however, is that since voting is private we do not know how individuals behave, only collections of voters β and we do not want them all to behave the exact same way. That is why one of the key elements of our work is the ability to create meaningful differences among our agents β particularly when it comes to the likes of issue positions and political engagement.
As the next Federal General Election gets closer, we’re turning our analytical attention to how the election might play out in Toronto. The first step, of course, is to gather data on prior elections. So, we’ve updated our toVotes data package to include the results of the 2008 and 2011 federal elections for electoral districts in Toronto. This dataset includes the votes received by each candidate in each district and poll in Toronto.
Turnout is often seen as (at least an easy) metric of the health of a democracy β as voting is a primary activity in civic engagement. However, turnout rates continue to decline across many jurisdictions[i]. This is certainly true in Canada and Ontario. From the PsephoAnalytics perspective β namely, accurately predicting the results of elections (particularly when using an agent-based model (ABM) approach) β requires understanding what it is that drives the decision to vote at all, instead of simply staying home.
We value constructive feedback and continuous improvement, so we’ve taken a careful look at how our predictions held up for the recent mayoral election in Toronto. The full analysis is here. The summary is that our estimates weren’t too bad on average: the distribution of errors is centered on zero (i.e., not biased) with a small standard error. But, on-average estimates are not sufficient for the types of prediction we would like to make.
The results are in, and our predictions performed reasonably well on average (we averaged 4% off per candidate). Ward by ward predictions were a little more mixed, though, with some wards being bang on (looking at Toryβs results), and some being way off β such as northern Scarborough and Etobicoke. (For what itβs worth, the polls were a ways off in this regard too.) This mostly comes down to our agents not being different enough from one another.
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 candidateβs supporters (with Toryβs supporters being the most likely to vote by a significant margin - which is why our results are more in his favour than recent polls).
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 βtoo close to callβ, with three barely leaning to Tory (38, 39, and 40) and three barely leaning to Ford (8, 35, and 43). Weβre not predicting Chow will win in any ward, but will come second in fourteen.
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 β meaning they need to behave like actual people might. In developing a proof of concept of our simulation platform (which weβll lay out in some detail soon), weβve 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.
Our most recent analysis shows Tory still in the lead with 44% of the votes, followed by Doug Ford at 33% and Olivia Chow at 23%. Our analytical approach allows us to take a closer, geographical look. Based on this, we see general support for Tory across the city, while Ford and Chow have more distinct areas of support. This still based on our original macro-level analysis, but gives a good sense of where our agents support would be (on average) at a local level.
Given the caveats we outlined re: macro-level voting modeling, weβre moving on to a totally different approach. Using something called agent-based modeling (ABM), weβre hoping to move to a point where we can both predict elections, but also use the system to conduct studies on the effectiveness of various election models. ABM can be defined simply as an individual-centric approach to model design, and has become widespread in multiple fields, from biology to economics.
Based on updated poll numbers (per Threehundredeight.com as of September 16) - where John Tory has a commanding lead - we’re predicting that the wards to watch in the upcoming Toronto mayoral election are clustered in two areas, surprisingly, traditional strongholds for Doug Ford and Olivia Chow. The first set are Etobicoke North & Centre (wards 1-4), traditional Ford territory. The second are in the south-west portion of downtown, traditional NDP territory, specifically Parkdale-High Park, Davenport, Trinity-Spadina (x2), and Toronto Danforth (respectively wards 14, 18-20, and 30).
As with any analytical project, we invested significant time in obtaining and integrating data for our neighbourhood-level modeling. The Toronto Open Data portal provides detailed election results for the 2003, 2006, and 2010 elections, which is a great resource. But, they are saved as Excel files with a separate worksheet for each ward. This is not an ideal format for working with R. We’ve taken the Excel files for the mayoral-race results and converted them into a data package for R called toVotes.
In our first paper, we describe the results of some initial modeling - at a neighbourhood level - of which candidates voters are likely to support in the 2014 Toronto mayoral race. All of our data is based upon publicly available sources. We use a combination of proximity voter theory and statistical techniques (linear regression and principal-component analyses) to undertake two streams of analysis: Determining what issues have historically driven votes and what positions neighbourhoods have taken on those issues Determining which neighbourhood characteristics might explain why people favour certain candidates In both cases we use candidatesβ currently stated positions on issues and assign them scores from 0 (βextreme leftβ) to 100 (βextreme rightβ).
Political campaigns have limited resources -βboth time and financial - that should be spent on attracting voters that are more likely to support their candidates. Identifying these voters can be critical to the success of a candidate. Given the privacy of voting and the lack of useful surveys, there are few options for identifying individual voter preferences: Polling, which is large-scale, but does not identify individual voters Voter databases, which identify individual voters, but are typically very small scale In-depth analytical modeling, which is both large-scale and helps to ‘identify’ voters (at least at a neighbourhood level on average) The goal of PsephoAnalytics* is to model voting behaviour in order to accurately explain campaigns (starting with the 2014 Toronto mayoral race).
The Ex
Last campfire of the vacation
Woodsman
Wilderness
Peaceful spot
