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Record W3037524374 · doi:10.1038/s41598-020-65452-9

Children avoid inefficient but fair partners in a cooperative game

2020· article· en· W3037524374 on OpenAlexfundno aff
Laurent Prétôt, Gorana Gonzalez, Katherine McAuliffe

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsDilemmaPreferenceSocial psychologySocial preferencesPrisoner's dilemmaPsychologyAsk priceSocial dilemmaGame theoryMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

Human adults use a range of social cues to obtain information about potential partners in cooperative contexts: we prefer partners who are competent, wealthy and generous, and those who abide by moral and social rules. One factor that carries particular weight is whether a prospective partner is fair. Here we ask whether children share this preference for fair partners and, if so, whether a prospective partner's past fair behaviour influences children's behaviour in a cooperative dilemma. Six- to nine-year-olds chose between partners who accepted or rejected resource allocations that were either strongly advantageously unequal, strongly disadvantageously unequal, or equal. They then played a one-shot Prisoner's Dilemma Game with their chosen partner. Children overwhelmingly preferred to play with the partner who accepted rather than rejected allocations. Regardless of their partner choice decisions, children tended to defect in the Prisoner's Dilemma Game, yet expected that their partners would be relatively more cooperative. Finally, children were more likely to cooperate with those they believed would cooperate. Together, these findings shed new light on the links between partner choice, fairness and cooperation in child development.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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