Bibliographic record
Abstract
Although possession is 'nine-tenths of the law', respect for ownership is widespread in the animal kingdom even without third-party enforcement. Thus, the first individuals to find objects are frequently left unchallenged by potential competitors and tend to win contests when disputes arise. Game theory has shown that respect for ownership ('Bourgeois' behaviour) can arise as an arbitrary convention to avoid costly disputes. However, the same theory predicts that a paradoxical respect for lack of ownership ('anti-Bourgeois' behaviour) can evolve under the same conditions and in some cases is the only stable outcome. Despite these predictions, anti-Bourgeois behaviour is rare in nature, whereas respect for ownership is frequently not absolute. Here, we review extensions of the classic models involving repeated interactions, confusion over roles, strategic coordination of behaviour ('secret handshakes'), owner-intruder asymmetries and continuous control of fighting investment. Confusion over roles and owner-intruder asymmetries in fighting ability may explain why respect for ownership is often partial. Moreover, although most model extensions facilitate the evolution of Bourgeois-like behaviour, secret handshakes and continuous control of fighting investment render the alternative anti-Bourgeois convention unstable. We develop these insights to highlight several key areas for future investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".