It’s election day! 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: 65%
* between 172 and 205: 25%
* between 206 and 240: 8%
* more than 240: 2%
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 election seats. The user wants current web results from various sources and a summary with tables. First, I need to check the provided search results.
Looking at the search results, result [1] from Simon Fraser University has detailed election data up to 2021, including seat counts for each party. The 2015, 2019, and 2021 elections are mentioned here. The Conservatives got 99 seats in 2015, 121 in 2019, and 119 in 2021. That’s a key point.
Result [2] is the Wikipedia page for the Conservative Party. It shows election results from 2004 to 2021, with seat numbers each year. There’s a table here that I can extract. For example, in 2011, they had 166 seats, which dropped to 99 in 2015, then increased to 121 in 2019 and 119 in 2021. This aligns with the SFU data.
Result [3] is another Wikipedia list of elections. It mentions the 1993 and 1997 elections, but those are older. The user is probably more interested in recent data, so maybe focus on post-2000 results.
Result [4] from the House of Commons confirms the majority governments, which is useful context. Result [5] from Britannica gives historical background on the party’s formation but not recent seat data.
Now, I need to create a summary and tables. The user asked for time series data. Using the data from [1] and [2], I can compile a table showing election years, seats won, change from previous, mean, median, etc. Let’s list the seats from 2004 to 2021.
Calculating the changes: from 2004 to 2006, seats increased by 25. Then 2006 to 2008: +19, 2008 to 2011: +23, 2011 to 2015: -67, 2015 to 2019: +22, 2019 to 2021: -2. The mean of these seat counts is (99+124+143+166+99+121+119)/7 ≈ 124.14. The median is 124 when ordered. The standard deviation would be around 28.5.
The user also wants URLs, so list the sources used: SFU, Wikipedia pages, House of Commons, and Britannica.
Make sure to cite each data point with [1][2] etc. Also, check if there’s any 2025 data, but the latest in the sources is 2021. The Conservative Party’s performance over the years shows fluctuations, with a peak in 2011 under Harper and a decline in 2015, then partial recovery.
Need to present this clearly, using tables for elections and statistical measures. Avoid markdown in the thinking, but in the final answer, use the specified formatting with headers and citations
The Conservative Party of Canada has experienced fluctuating seat counts in federal elections since its formation in 2003. Below is a summary of key election results and statistical analysis based on data from 2004 to 2021.
—
## Election Seat Results (2004–2021)
*Note: Baseline comparison to the merged parties’ 2000 performance.*
—
## Statistical Analysis of Seat Counts (2004–2021)
Key trends:
– **Peak performance**: 166 seats (2011 majority under Harper)[2].
– **Sharpest decline**: -67 seats in 2015 after Harper’s third term[1][5].
– **Recent stability**: Seats fluctuated narrowly between 119–121 in 2019–2021[1][2].
—
## Data Sources
1. [Simon Fraser University Election Archive] (https://www.sfu.ca/~aheard/elections/1867-present.html)
2. [Conservative Party Wikipedia] (https://en.wikipedia.org/wiki/Conservative_Party_of_Canada)
3. [House of Commons Historical Data] (https://www.ourcommons.ca/procedure/procedure-and-practice-3/App10-e.html)
4. [Britannica ‘s Conservative Party Overview] (https://www.britannica.com/topic/Conservative-Party-of-Canada)
No current predictions from market analysis sites (Kalshi, Polymarket) or 2025 data were found in the provided sources.
OpenAI gives a base rate of proportion of elections with Conservative Party winning between 206 and 240 seats.
The question difficulty is rated 6 (0 to 10)
Historical weighted factors include:
Recent election performances, 0.4
Public opinion trends, 0.3
Major political events or scandals, 0.3
(Carolyn’s
note: OpenAI’s base rate is drastically different from the Bayesian
base rate below because it didn’t take into account recent
events leading to polling data indicating that the Conservative Party of
Canada has been harmed by US President Trump’s treats to annex Canada.
Before Trump’s threats, the Conservative Party was on
track to win a majority of the seats.)
A Bayesian calculation could be performed as follows:
Prior probability (base rate) updated using public opinion trends and impact of any current scandals. Assume independence of events. Posterior probability calculated by incorporating recent polling data indicating increased support. If base rate is 0.14 and polling indicates 10% increase in support, update as follows: P(Winning|Polling) = (0.14 * 1.1). Adjust for external event likelihood.
Bayesian base rate: 0.154
Sufficient news to provide a good forecast? 1 (0 or 1)
News is relevant, topical and unbiased? 1 (0 or 1)
Question classification: reference_class
Expected time (days) until question close: 180 ( Carolyn’s note: Gigantic error. The election is today, so the answer should be zero. Alas, this error is typical of generative AIs such as the three incorporated into Jeremy’s Multi-AI Oracle.)
The following were considered in order to produce this base rate:
The base rate was calculated using historical seat wins by the Conservative Party and considering their fluctuations in recent elections. The most seats they have won in the period examined (since the 2003 reformation of the party) was 166, indicating that achieving a seat count between 206 and 240 is fairly uncommon.
Ideally, the news feed would contain the following sorts of information for a better forecast:
Data on current government policies and voter sentiments closer to the election date, including polling data from various trusted sources, would be beneficial. Understanding economic indicators and their potential influence on voter behavior would also improve estimates.
Some potential divergent considerations that might affect the base rate:
Factors such as notable changes in political landscape, emerging parties attracting conservative voters, or unexpected political scandals could significantly alter the forecasted probability.
The following chain of events are necessary for the question to resolve positively:
– Conservative Party wins 206 to 240 seats in the next Canadian federal election Low
– Conservative Party gains significant political momentum and public support Medium
– Current government scandals or failures shifting voter preference significantly Medium
Querying Claude (AI predicts: [“less than 172”: 0.65, “between 172 and 205”: 0.25, “between 206 and 240”: 0.08, “more than 240”: 0.02] – confidence: 6)
Querying Mistral (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 OpenAI (AI predicts: [“less than 172”: 0.15, “between 172 and 205”: 0.35, “between 206 and 240”: 0.3, “more than 240”: 0.2] – confidence: 6)
Question Type: Multiple Choice
Confidence: 6
MAPD: Avg: 0.23333333333333, Max: 0.46666666666667
# LLM responses: 3
Model value:
* less than 172: 65%
* between 172 and 205: 25%
* between 206 and 240: 8%
* more than 240: 2%
The analyses of the various AIs converge on the understanding that the Conservative Party of Canada, historically peaking at 166 seats in 2011, has shown stability between 119 and 121 seats in recent elections. The historical data, with a mean seat count of 124.1 and a standard deviation of 28.5, suggests that while current polling hints at potential gains, achieving more than 206 seats would require a substantial departure from past performance. However, surpassing their peak requires overcoming significant historical challenges and external influences. Several factors could alter these predictions, including political scandals, economic shifts, changes in leadership or party dynamics, electoral boundary adjustments, and broader changes in voter sentiment. Therefore, while a moderate gain appears plausible, reaching significantly higher seats would need overcoming both historical trends and leveraging emergent political opportunities..
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).
