Bibliographic record
Abstract
Divide the dollar is a two-player simultaneous derived from a game invented by John Nash because its strategy space has an entire subspace of Nash equilibria. This study describes and explores a family of generalizations of divide the dollar with easily controlled properties. If we view divide the dollar as modeling the process of making a bargain, then the generalized game makes it easy to model the impact of external subsidies on bargaining. Classical divide the dollar is compared to four generalizations representing a simple subsidy in three different amounts and a more complex type of subsidy. The distribution of simple strategies that arise under replicator dynamics is compared to the bids that arise in populations of evolving, adaptive agents. Agents are encoded using a finite state representation that conditions its transitions on the result of bargains. These results fall into three categories, the first player obtains a higher amount, the second one does, or the agents fail to make a deal. The replicator dynamic results are compared to obtain the naive degree of distortion caused by the subsidies. The results for evolving agents are then examined to figure out the degree to which adaption compensated for or amplifies this distortion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".