Endogenous versus Exogenous Fairness Indices in Repeated Ultimatum Games
Bibliographic record
Abstract
In ultimatum games, we often observe some participants rejecting offers that may be normally viewed as “fair” while others accept even lower offers that are typically viewed as “unfair”. The objective of this study is to construct and examine an endogenous fairness index that helps explain this phenomenon. To achieve this objective, we construct a repeated ultimatum game environment in which each participant plays the roles of both the sender and the receiver with two different participants. We conjecture that the ratio of the amount that an individual receives divided by the amount the individual sends, captures the benchmark of what constitutes a fair offer for that individual when an offer-acceptance decision has to be made. Our design includes a fixed- and random-partners treatment in the repeated ultimatum game as an attempt to identify and isolate the effects of social distance on offer-acceptance decisions. In addition to the inclusion of the fairness indices in the offer-acceptance models, we introduce measures of social value orientations and risk attitudes as control variables in our analyses. We find that our belief-related fairness index is, in some cases, a better explanatory variable for offer-acceptance decisions than the conventional “offer index” and in other cases significantly augments the “offer index”. As well, the offer-acceptance model including the belief-related fairness index can account for likelihoods of accepting less fair offers that can, at times, exceed likelihoods of accepting more fair offers.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".