Bibliographic record
Abstract
Abstract Respect for ownership is widespread in the animal kingdom. Thus, the first individuals to find resources are frequently unchallenged by potential competitors and tend to win contests when disputes arise. Game theory has shown that ownership acceptance can arise as an arbitrary convention to avoid costly disputes, even when there are small differences in the value of the resource to individuals or in their fighting ability. However, if possessors make significant non‐transferable investments in resources, then possessors will also be more motivated to retain them. Similarly, if fighting ability affects fighting outcome and can be reliably assessed, then alternative conventions in which poor fighters concede to good fighters are also favoured. Both sources of asymmetry can ultimately reinforce the ownership advantage and broaden the conditions under which owners remain unchallenged. So, respect for possession readily evolves to avoid costly disputes and is especially favoured when possession reflects an underlying asymmetry. Key Concepts Respect for ownership is widespread in the animal kingdom and is maintained without third‐party enforcement. Classical game‐theory models successfully explain how respect for ownership can evolve as a convention to avoid costly disputes. Differences in fighting ability and value of resource between individuals help explain why respect for property is typically conditional, such that, for example, larger intruders will occasionally challenge owners. If owners tend to be better fighters or value the resource more highly, then this asymmetry will further promote recognition of ownership, taking it above and beyond a convention. As might be expected, the most intense fights between individuals arise when conventional solutions break down – for example, when both individuals believe themselves to be the rightful owner. Other aspects of ownership, such as inheritance and/or division of property, are amenable to game theoretical analysis, but they have seen much less work.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".