Does Trust Beget Trustworthiness? Trust and Trustworthiness in Two Games and Two Cultures: A Research Note
Bibliographic record
Abstract
An important unanswered question in the empirical literature on trust is whether trusting begets trustworthiness. In two experimental games, with Japanese and American participants, respectively, we compared trust and trustworthiness to provide an answer to this question. The trustee in the standard Trust Game knows that he or she is trusted, whereas the trustee in the Faith Game does not know whether or not this is the case. Except for this fact, the trustee faces the same choice in both situations. If the simple fact that one is trusted by someone else makes a person more trustworthy to the truster, then the trustee in the Trust Game should behave in a more trustworthy manner. Our results indicate that trust does not beget trustworthiness, at least in one-shot games. The results also indicate that trust and trustworthiness are two sides of the same coin but are quite distinct, partially replicating the recent findings of Buchan, Croson, and Dawes. American trusters were more trusting than their Japanese counterparts in the Trust Game, whereas American trustees were less trustworthy. The nationality difference in trust and trustworthiness is less pronounced in the Faith Game. We conclude that trust researchers should consider the limitations of one-shot games in studying the determinants of trust and trustworthiness.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".