Give it Away Now: An Explanation of the Empirical Results of the Ultimatum and Centipede Games
Bibliographic record
Abstract
Economics defines individual rationality as consumers making choices that maximize their utility in anticipation of the future consequences of these choices. In theory, a consumer will take his or her income and allocate it towards purchases that maximize his or her utility given his or her stable of reasonably static preferences (in the short run) and estimated changes to preferences in the long run. In order for an agent to maximize his or her utility, the agent must also maximize his or her income. However, behavioural studies on human decisions in economic games (game theory) have shown that consumers do not always maximize their income. Two games in particular (Ultimatum and Centipede) have demonstrated that seemingly rational players may not maximize income, whether for perceived fairness, justice, or punishment. Practical applications of these results are observed in labour relations when striking unionized employees earn less with a labour stoppage than they would have if they had avoided losing time at work. Specifically, a seven week strike in 2008 by CUPE Local 855 (Kawartha Lakes) is examined. It is determined that all four job types in the City of Kawartha Lakes Children’s Services department lost income because of the strike. Reasoning and empirical results from both the Ultimatum and Centipede games will be used to explain the Union’s decision to strike and to strike for as long as they did
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".