Bibliographic record
Abstract
We propose a model of cycles of distrust and conflict.Overlapping generations of agents from two groups sequentially play coordination games under incomplete information about whether the other side consists of "extremists" who will never take the good/trusting action.Good actions may be mistakenly perceived as bad/distrusting actions.We also assume that there is limited information about the history of past actions, so that an agent is unable to ascertain exactly when and how a sequence of bad actions originated.Assuming that both sides are not extremists, spirals of distrust and conflict get started as a result of a misperception, and continue because the other side interprets the bad action as evidence that it is facing extremists.However, such spirals contain the seeds of their own dissolution: after a while, Bayesian agents correctly conclude that the probability of a spiral having started by mistake is sufficiently high, and bad actions are no longer interpreted as evidence of extremism.At this point, one party experiments with a good action, and the cycle restarts.We show how this mechanism can be useful in interpreting cycles of ethnic conflict and international war, and how it also emerges in models of political participation, dynamic inter-group trade, and communication -leading to cycles of political polarization, breakdown of trade, and breakdown of communication.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".