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The Evolution of Ownership Acceptance

2017· other· en· W2754726762 on OpenAlexaff
Thomas N. Sherratt, Mike Mesterton‐Gibbons

Bibliographic record

VenueEncyclopedia of Life Sciences · 2017
Typeother
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsCarleton University
Fundersnot available
KeywordsPossession (linguistics)ConventionEnforcementBusinessValue (mathematics)Competitor analysisLaw and economicsResource (disambiguation)MicroeconomicsIndustrial organizationEconomicsMarketingLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.022
GPT teacher head0.307
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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