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Record W3127913862 · doi:10.1017/s1930297500008342

Steady steps versus sudden shifts: Cooperation in (a)symmetric linear and step-level social dilemmas

2021· article· en· W3127913862 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJudgment and Decision Making · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsSocial dilemmaDilemmaEndowmentAsymmetrySocial psychologyMicroeconomicsPsychologyEconomicsPolitical scienceMathematicsLaw

Abstract

fetched live from OpenAlex

Abstract Are groups of people better able to minimize a collective loss if there is a collective target that must be reached or if every small contribution helps? In this paper we investigate whether cooperation in social dilemmas can be increased by structuring the problem as a step-level social dilemma rather than a linear social dilemma and whether cooperation can be increased by manipulating endowment asymmetry between individuals. In two laboratory experiments using ‘Public Bad’ games, we found that that individuals defect less and are better able to minimize collective and personal costs in a step-level social dilemma than in a linear social dilemma. We found that the level of cooperation is not affected by an ambiguous threshold: even when participants cannot be sure about the optimal cooperation level, cooperation remains high in the step-level social dilemmas. We find mixed results for the effect of asymmetry on cooperation. These results imply that presenting social dilemmas as step-level games and reducing asymmetry can help solve environmental dilemmas in the long term.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.126
GPT teacher head0.390
Teacher spread0.264 · 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