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Record W2889807906 · doi:10.1073/pnas.1808241115

Endogenous rewards promote cooperation

2018· article· en· W2889807906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSanctionsSocial dilemmaPunishment (psychology)DilemmaMechanism (biology)Public goodPublic economicsPublic goods gameField (mathematics)EconomicsBusinessMicroeconomicsPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Sustaining cooperation in social dilemmas is a fundamental objective in the social and biological sciences. Although providing a punishment option to community members in the public goods game (PGG) has been shown to effectively promote cooperation, this has some serious disadvantages; these include destruction of a society's physical resources as well as its overall social capital. A more efficient approach may be to instead employ a reward mechanism. We propose an endogenous reward mechanism that taxes the gross income of each round's PGG play and assigns the amount to a fund; each player then decides how to distribute his or her share of the fund as rewards to other members of the community. Our mechanism successfully reverses the decay trend and achieves a high level of contribution with budget-balanced rewards that require no external funding, an important condition for practical implementation. Simulations based on type-specific estimations indicate that the payoff-based conditional cooperation model explains the observed treatment effects well.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.373
Teacher spread0.260 · 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