The Impact of Social Value Orientation and Risk Attitudes on Trust and Reciprocity
Bibliographic record
Abstract
Prior experimental studies provide evidence that the levels of trust and reciprocity are highly susceptible to individuals' preferences towards payoffs, prior experience, capacity to learn more about personal characteristics of each other and social distance. The objective of this study is to examine whether social value orientation as developed by Griesinger and Livingston [Griesinger, D. W., & Livingston Jr., J. W. (1973). Toward a model of interpersonal motivation in experimental games. Behavioral Science, 18, 173-188] and Liebrand [Liebrand, W. B. G. (1984). The effect of social motives, communication and group size on behavior in an n-person multi-stage mixed-motive game. European Journal of Social Psychology, 14, 239-264] and risk preferences can help to account for the variability of trust and trustworthiness. We use the Berg, Dickhaut and McCabe [Berg, J., Dickhaut, J., & McCabe, K. (1995). Trust, reciprocity, and social history. Games and Economic Behavior, 10, 122-142] investment game to generate indices of trust and reciprocity. Prior to their participation in the investment game, all subjects participated in two other games. One is used to measure their social value orientation (a measure of other regarding behavior) and the second to measure risk attitudes. These variables are introduced as treatments in the analysis of the trust and reciprocity data. In addition to these preference related variables, gender is introduced to capture any differences between men and women which may not be encompassed by value orientation and risk attitudes. The statistical analysis indicates that the social value orientation measure significantly accounts for variation in trust and reciprocity. As well, the level of trust exhibited by an investor significantly affects the reciprocity of the responders and this measure of trust interacts with social value orientation. Individuals who are highly pro-social reciprocate more as the sender's trust increases, while those who are highly pro-self reciprocate less as the sender's trust increases. For this sample of participants, the gender variable does not capture any differences in the behavior of men and women that is not already reflected by the differences captured by their value orientations. Risk attitudes do not significantly account for variation in trusting behavior, except for the case where individuals have neither strongly pro-social nor pro-self social value orientations. In this case, more risk-seeking individuals are more trusting.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".