Radical framing effects in the ultimatum game: the impact of explicit culturally transmitted frames on economic decision-making
Bibliographic record
Abstract
Many studies have documented framing effects in economic games. These studies, however, have tended to use minimal framing cues (e.g. a single sentence labelling the frame), and the frames did not involve unambiguous offer expectations. Results often did not differ substantially from those in the unframed games. Here we test the hypothesis that, in contrast to the modal offer in the unframed ultimatum game (UG) (e.g. 60% to the proposer and 40% to the responder), offers in a UG explicitly framed either as a currency exchange or a windfall will closely conform to expectations for the frame and diverge substantially from the modal offer. Participants recruited from MTurk were randomized into one of two conditions. In the control condition, participants played a standard UG. In the treatment conditions, players were provided a vignette explicitly describing the frame with their roles: some were customers and bankers in a currency exchange, and others were in a windfall scenario. We predicted (i) that modal offers in the currency exchange would involve an asymmetric split where greater than 80% went to customers and less than 20% went to bankers, and (ii) that variation in windfall offers would converge onto a 50–50 split with significantly less variation than the control condition. Our first prediction was confirmed with substantial effect sizes ( d = 1.09 and d = −2.04), whereas we found no evidence for our second prediction. The first result provides further evidence that it is difficult to draw firm conclusions about economic decision-making from decontextualized games.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".