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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".