Bibliographic record
Abstract
We model an infinitely-repeated conflict between two factions who both have a desire to exact revenge for past destruction suffered. The destruction suffered by a player is a stock that grows according to his opponent’s destructive efforts and the rate at which past destruction is forgotten (i.e., depreciates). This gives a differential game. We find that a desire for revenge can cause a low-ability player to exert a higher effort than a high-ability player, which means that the former may have a higher probability of success in a given period. Given a desire for revenge, we find that, the conflict initially escalates and eventually reaches a steady state. When there is no desire for revenge, the conflict reaches a steady state immediately. The conflict is sufficiently less destructive if the rate at which past destruction is forgotten is sufficiently high. We briefly discuss how our results apply to the USA’s invasion of Iraq, reconstruction assistance to Lebanon after the 1975-1990 war, and the Israeli-Palestinian conflict.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".