Never tell me the odds: Typical return-to-player information increases gamblers’ perceived chances of winning
Bibliographic record
Abstract
• Previous research on RTP messaging used higher-than-average payouts. • We test the impact of an industry-average RTP message. • RTP message increased perceived chances of winning when compared to no information. • House edge messages performed better than RTP, but no better than no information. • Gamblers deserve better information about gambling products. Previous research has shown that gamblers consistently misunderstand return-to-player (RTP) information, and participants shown an RTP of 93% reported that they were more likely to win than those who were shown no information. However, this effect might have been inflated by a higher-than-average RTP percentage. We experimentally test the impact of showing an industry-average RTP message of 90% on gamblers’ perceived chances of winning, in two studies across two countries (UK and US). Slot players from Prolific (N = 6062) were shown either an RTP message (“This game has an average percentage payout of 90%”), two different House Edge (HE) messages (“This game keeps 10% of all money bet” or “This game is programmed to cost you 10% of your stake on each bet”) or No-Information, and asked to rate their perceived chances of winning at a new slot machine. Across both studies and countries, participants rated their perceived chances of winning as significantly higher with a typical 90% RTP message than with No Information, with large effect sizes ( ORs > 5). Both HE messages significantly outperformed RTP, but were no better than No-Information. These effects were moderated by PGSI in the No-Information condition, with participants with higher PGSI responding with higher chances of winning, but not in the other conditions. These results show an undesired side-effect of the consistently ineffective RTP information and confirm the superiority of HE over RTP, although none of the messages were superior to No-Information. Gamblers deserve to be better informed.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".