How many seats will the Conservative Party win in Canada’s April 28, 2025 parliamentary election?
Here’s what Jeremy Lichtman‘s Multi-AI Oracle forecasts today:
Model value:
* less than 172: 85%
* between 172 and 205: 10%
* between 206 and 240: 4%
* more than 240: 1%
Here’s how Jeremy’s Multi-AI Oracle says it arrived at these probabilities:
Obtaining outside data.
Checking internet sources, using keywords: Canada Conservative Party election seats
Results from around the web: {think}
Okay, let’s tackle this query about the Canada Conservative Party’s election seats using the latest web results. The user wants a summary with relevant data tables and sources.
First, I’ll check the search results provided. Result [1] is the Wikipedia page for the 2025 Canadian federal election. It mentions the election date, the new 343-seat map, and that the writs were issued in March 2025. This gives context on the election structure.
Results [2] and [3] are from YouGov, both dated April 23, 2025. They project a Liberal majority but discuss seat changes between parties. The Conservatives are expected to gain seven seats from the NDP but lose some to the Liberals in Ontario. They also mention key battlegrounds like the Greater Toronto Area and British Columbia. These are crucial for understanding the Conservative Party’s seat dynamics.
Result [4] lists candidates but doesn’t directly address seats. Result [5] from Axios talks about Canada-U.S. relations but not seat numbers. So, the main sources are [1], [2], and [3].
Now, compiling the data: The total seats are 343, with a majority at 172. The YouGov projection shows Liberals winning 172 (majority), Conservatives 112, Bloc 32, NDP 20, Greens 4, and others 3. The seat changes from 2021 need to be noted, like Bloc losing 11 seats to Liberals.
For the table, I’ll structure it with Party, Projected Seats, and Change from 2021. Highlighting Conservative losses in specific Ontario ridings as per the YouGov data. The time series data isn’t evident from the sources, so maybe omit that unless inferred, but it’s better to stick to available info.
Including URLs is necessary. The user also wants tables, so format the projected seats and key battlegrounds as tables. Ensure citations are correctly placed after each relevant sentence, using [1], [2], [3].
Double-checking for any other data points: The new electoral districts from the 2021 census are mentioned in [1], which might affect seat distribution but isn’t quantified. The focus should be on the YouGov projections for Conservative seat changes.
Alright, structure the answer with an intro, projected seat breakdown in a table, key battlegrounds in another table, and conclude with sources. Make sure to cite each data point properly.
The 2025 Canadian federal election, scheduled for April 28, will use a new 343-seat electoral map based on the 2021 census, with 172 seats required for a majority[1].
YouGov’s MRP projection (published April 23, 2025) indicates the Conservative Party is expected to win 112 seats, marking a net loss compared to previous elections despite some gains[2][3].
### Projected Seat Distribution
The Conservatives face challenges from Liberal gains in Ontario and Quebec, while attempting to offset losses through NDP-held ridings. Seven projected NDP-to-Conservative seat flips contrast with 13 Liberal gains from Conservative strongholds[2][3].
### Sources
1. https://en.wikipedia.org/wiki/2025_Canadian_federal_election
2. https://yougov.co.uk/politics/articles/52054-first-yougov-mrp-2025-canadian-federal-election-shows-liberals-on-track-to-win-modest-majority-poll
3. https://today.yougov.com/politics/articles/52054-first-yougov-mrp-2025-canadian-federal-election-shows-liberals-on-track-to-win-modest-majority-poll
OpenAI gives a base rate of (Carolyn’s note: OpenAI failed to return a base rate. See instead the Bayesian base rate below.)
The question difficulty is rated 7 (0 to 10)
Historical weighted factors include:
Previous election seat count for Conservatives, 0.4
Recent polling and projections, 0.4
Impact of new electoral boundaries, 0.2
A Bayesian calculation could be performed as follows:
Base rate adjusted by recent YouGov projections, historical performance in prior elections, and the impact of new electoral boundaries. Starting at 0.32 (112 out of 343 seats), adjustments based on projected seat losses and potential gains in battleground regions.
Bayesian base rate: 0.29
Sufficient news to provide a good forecast? 1 (0 or 1)
News is relevant, topical and unbiased? 1 (0 or 1)
Question classification: scenario_based_forecast
Expected time (days) until question close: 3
The following were considered in order to produce this base rate:
Base rate derived from current projections (YouGov) and historical seat counts. Conservative historical performance indicates difficulty in surpassing 112 seats, as current projections suggest losses in previously strong regions.
Ideally, the news feed would contain the following sorts of information for a better forecast:
Additional data on key demographic changes, detailed analysis of local candidates’ influence, and shifts in media sentiment could further refine this forecast.
Some potential divergent considerations that might affect the base rate:
Potential shifts in voter sentiment, unexpected political events, and errors in current polling could lead to variations from current projections, potentially benefiting the Conservatives in some regions.
The following chain of events are necessary for the question to resolve positively (Carolyn’s note: Meaning for the Conservative party to win more seats than Liberals):
– The Conservative Party gains additional seats from the Liberals in key battleground regions. Unlikely
– The Conservative Party maintains its current seats in regions where they have a stronghold. Moderate
– The Conservative Party successfully flips previously NDP-held ridings to their favor. Possible
Querying Claude (AI predicts: [“less than 172”: 0.85, “between 172 and 205”: 0.1, “between 206 and 240”: 0.04, “more than 240”: 0.01] – confidence: 7)
Querying Mistral (AI predicts: [“less than 172”: 0.95, “between 172 and 205”: 0.03, “between 206 and 240”: 0.01, “more than 240”: 0.01] – confidence: 7)
Querying OpenAI (AI predicts: [“less than 172”: 0.6, “between 172 and 205”: 0.25, “between 206 and 240”: 0.1, “more than 240”: 0.05] – confidence: 6)
Question Type: Multiple Choice
Confidence: 7
MAPD: Avg: 0.11666666666667, Max: 0.23333333333333
# LLM responses: 3
Model value:
* less than 172: 85%
* between 172 and 205: 10%
* between 206 and 240: 4%
* more than 240: 1%
The YouGov MRP projection from April 2025 forecasts the Conservative Party to secure around 112 seats, which falls well below the 172 seats needed for a majority and represents a loss of 15 seats from their performance in 2021. The party faces significant challenges due to losing ground in key strongholds like Ontario and Quebec, as well as struggling to make substantial gains in NDP-held ridings. The new 343-seat electoral map and prevailing political trends underline these difficulties, with key regions showing Liberal advancements. However, the predictions could be undermined by potential inaccuracies in polling, last-minute voter sentiment shifts, or unexpected political developments that might benefit the Conservatives. Systematic polling errors or a sudden collapse of Liberal support could also lead to a more favorable outcome for the Conservative Party than currently projected.
Guide to Jeremy’s Bots Reports (These don’t all apply to the current version of his bot.)
Bayesian analysis: From Britanica: A prior probability distribution for a parameter of interest is specified first. The evidence is then obtained and combined through an application of Bayes’s theorem to provide a posterior probability distribution for the parameter.
MAPD: Mean Absolute Percentage Deviation calculates the average percentage difference between actual values and predicted values, providing a relative measure of error.
Median: We collect the output values from the LLMs in an array. We then take the median value. This is between 0 and 1 (i.e. 0.15 = 15%).
Base rate: Currently we take OpenAI’s value as the base rate. We are working on a better value using a different prompt that takes into account process. The base rate helps to determine if the median makes sense.
SD: The standard deviation between the median and the base rate.
Confidence: We query each of the LLMs on how confident they are of their predictions (between 0 and 10) and take a median. Because the LLMs tend to be overconfident, we take anything lower than 6 as being low confidence. This factors into the overall model.
Conf Mode: Based on the confidence value. >=9 is high confidence. Below 6 is low confidence (this is also triggered by an exceptionally high SD).
Mellers: This refers to Barbara Mellers, specifically a paper she wrote that includes a formula for moving values towards an extreme (i.e. 0 or 1).
Reverse Mellers: This uses the formula from above, but with a sub-1 coefficient to move the values closer to 50%.
Theory of Mind: We ask the LLMs what they think other LLMs would predict. We hope that this makes them consider the questions more deeply.
Beta Distribution: Currently unused, but possibly of interest. This is based on the median, the base rate and the SD.
Close Type: We noticed that the appropriate base case for some questions is closer to the extremes, while others are closer to 50%. When we have a low confidence value, this helps us to determine whether to extremize or de-extremize the value. ‘A’ implies closer to zero. ‘B’ implies closer to 50%. ‘C’ implies closer to 100%.
# LLM Responses: The count of LLMs that responded. We query 5 currently, but they have a relatively high API failure rate.
Model value: For normal confidence mode, this is the median. For high confidence, this is the Mellers value. For low confidence, this is either the Mellers or Reverse Mellers (depending on the Close Type).
