Overconfidently Conspiratorial: Conspiracy Believers are Dispositionally Overconfident and Massively Overestimate How Much Others Agree With Them
Bibliographic record
Abstract
There is a pressing need to understand why people believe in conspiracies. Although past work has focused on needs and motivations, we propose an alternative driver of belief: overconfidence. Across eight studies with 4,181 U.S. adults, conspiracy believers consistently overestimated their performance on numeracy and perception tests (even after taking their actual performance into account). This relationship with overconfidence was robust in controlling for analytic thinking, the need for uniqueness, and narcissism, and it was strongest for the most fringe conspiracies. We also found that conspiracy believers-particularly overconfident ones-massively overestimated (>4×) how much others agree with them: Although conspiratorial claims were believed by a majority of participants only 12% of the time, believers thought themselves to be in the majority 93% of the time. This was evident even when asked to rate agreement among counter-partisans, indicating that conspiracists are genuinely unaware that their beliefs are on the fringe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".