Bibliographic record
Abstract
This research examines the “ultimatum game” studied in Experimental Economics. The game goes as follows: a proposer has to split $20 between himself and a responder. After the money is divided, the responder then either accepts or rejects the offer. Accept would results in the money being split according to the proposer’s offer while reject results in $0 for both players. According to mainstream economics’ assumption of self-interest maximization, the responder would accept any amount of money offered by the proposer because anything is better than $0. Meanwhile, the proposer, knowing this, would offer the responder the lowest possible amount. However, results from the experiment shows that most responders rejected low offers and most proposers offer much more than the lowest possible amount. By studying several versions of the ultimatum game and conducting primary research, 5 different variables other than the ratio to which the total sum of money is divided were identified to affect outcome. These are: anonymousness, fear of rejection, perception of the roles, ownership of the money, and total sum of money. Then based on the observations, a graphical model was created that described how the 5 factors affect the game outcome. The implications of this research is that decision making models in economics has to be made more valid by accounting for more qualitative factors such as the ones in this experiment. Only when those factors are accounted for as part of the calculation of utility/satisfaction could the assumption of maximizing self-interest be made.
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 | 0.010 | 0.030 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".