The Prisoner's Dilemma: Me versus We
Bibliographic record
Abstract
Examples Perhaps the best-known interdependence situation is the Prisoner's Dilemma, a situation that derives its name from the classic anecdote about two prisoners who were accused of robbing a bank. In this anecdote, described in Luce and Raiffa (1957), the district attorney, unable to prove that they were guilty, created a dilemma in an attempt to motivate the prisoners to confess to the crime. The prisoners were put in separate rooms, where each prisoner was to make a choice: to confess or not to confess. The district attorney sought to make confessing tempting to the prisoners by creating a situation in which the sentence was determined not only by their own confessing or not but also by the fellow prisoner's confessing or not. Yet irrespective of the fellow prisoner's choice, the choice to confess yielded a better outcome (or less worse outcome) than did the choice not to confess. Specifically, when the other confessed, confessing yielded “only” an 8-year sentence, whereas not confessing yielded a 10-year sentence. And when the other did not confess, confessing yielded only a 3-month sentence, whereas not confessing yielded a 1-year sentence. So, from this perspective, it seems rational for each prisoner to confess to the crime. However, the crux of the dilemma is that the outcome following from both confessing (an 8-year sentence) is worse than the outcome following from both not confessing (a 1-year sentence).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".