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Record W4283734218 · doi:10.5539/ass.v18n7p39

Personality Difference and Strong-Strong Coalition—Experimental Research Based on Punishment Mechanism

2022· article· en· W4283734218 on OpenAlexvenueno aff
Jun Liu, Cuicui Zhu, Di Xiang

Bibliographic record

VenueAsian Social Science · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOpinion Dynamics and Social Influence
Canadian institutionsnot available
Fundersnot available
KeywordsPunishment (psychology)IndividualismPersonalityPower (physics)Value (mathematics)Social psychologyPsychologySocial exchange theoryPositive economicsMicroeconomicsEconomicsPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Research on social value orientation divides rational people into three categories: individualist, pro-social, and competitor. This kind of research does not focus on network structure. Network exchange theory emphasizes that power comes from an exclusive structure, but it ignores the personality characteristics of people. This paper combines these two studies to explore how punishment mechanisms affect individual coalition strategies in an exclusive structure. The experimental results prove that: (1) when the strong is not in the coalition, there is no significant difference in the benefits of the strong power individualist person and the strong power pro-social person; (2) when two people with different social value orientations are strong-strong coalition, the two will form a “betrayal chain” whose benefit is close to the equilibrium value of the “compromise chain”. The strong power individualists are more likely to “betray” and “take advantage” more than the strong power pro-socialists; (3) the intensity of punishment is inversely related to the frequency of “betrayer”. Analysis of the dialogue data revealed that (4) the strong will properly take care of the weak, and the weak will “approach” the strong; (5) heavy punishment will have a deterrent effect, allowing the actors, especially pro-social to internalize the “punishment mechanism”. This study incorporates both personality and structure into the model, and such studies can explain many power phenomena.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.368
Teacher spread0.321 · 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 designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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