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Record W2406743839

Effects of Social Inhibition on Selection of Artifact Capabilities.

2013· article· en· W2406743839 on OpenAlex
Felicitas Mokom, Ziad Kobti

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

VenueThe Florida AI Research Society · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsArtifact (error)Computer scienceProcess (computing)Selection (genetic algorithm)Order (exchange)Field (mathematics)Artificial intelligenceBusiness
DOInot available

Abstract

fetched live from OpenAlex

Tool or artifact use is prevalent in the human race. Over time humans learn, evolve and modify these capabilities in order to achieve their goals facilitating their adaption in an ever changing environment. Once an artifact capability is learned however, humans are often faced with the decision making process of which capabilities to apply at any given time. These decisions are not only affected by their internal states but also the social environment in which they operate. In this study we present a computational multi-agent simulation model that investigates how social inhibition affects the artifact capability-selection process. Inspired by models of social inhibition in the field of specialization, we demonstrate that functioning in a social environment often leads to the inability to select and perform the capabilities that we inherently desire. The model also tests the effects of demand on the capability selection process. Experiments conducted demonstrate that at a group level social inhibition may contribute to a decline in the performance of the group. It is also observed that group performance increases alongside demand suggesting that higher demand may reduce the effects of so-

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.027
GPT teacher head0.349
Teacher spread0.323 · 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