Bibliographic record
Abstract
Suppose that an individual is selected randomly to propose an alternative, all individuals vote for or against this alternative, and the proposal is implemented and the game ends if a majority is in favor, while otherwise the procedure is repeated until a proposal is accepted by a majority. Every alternative is the outcome of a subgame perfect equilibrium of the resulting extensive game, and, if the individuals' votes are observable, most alternatives are outcomes of subgame perfect equilibria in which every individual's vote is undominated. If only the outcomes, not the individuals' votes, are observable, and the individuals are sufficiently patient, then if the alternatives are distributions of a fixed amount of a good, every alternative is the outcome of a subgame perfect equilibrium with undominated voting, while if the set of alternatives is an interval of numbers and the individuals' preferences are single-peaked, the outcome of a subgame perfect equilibrium with undominated voting is close to the median of the individuals' favorite alternatives. Now suppose that bargaining is on-going. An individual is selected randomly to propose a distribution of a fixed amount of a good, and all individuals vote for or against this proposal. If a majority votes in favor, the proposal is implemented in the current period, and otherwise the status quo is implemented. In both cases, the procedure is repeated in the next period, with the status quo in each period equal to the previous period's outcome. There are examples in which almost any distribution is the outcome of a stationary subgame perfect equilibrium of this model, including distributions in which some of the good is wasted.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.137 | 0.036 |
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".