The impact of language on decision-making: Auction winners are less cursed in a foreign language
Bibliographic record
Abstract
As foreign language use becomes more commonplace in the globalized market, we ask whether using a foreign language systematically impacts financial decisions. We conducted a lab experiment in Beijing, China, with 357 native Mandarin Chinese speakers who know English as a foreign language. We ran a series of sealed-bid, common value auctions, where winning bidders often pay more than the object is worth and hence suffer from the “winner’s curse.” Here we show that using a foreign language reduces the winner’s curse, as winning bidders were less likely to overbid for the object. When using a native tongue, bidders adopted a naïve strategy, while with a foreign language they got closer to the Nash equilibrium bid. However, as bidders received feedback on others’ bidding behavior across consecutive auctions, bidding across the language treatments converged to the naïve bid. These results suggest that the language through which individuals make bidding decisions can have influential effects on financial decision making in market settings.
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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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".