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Record W2153223313 · doi:10.5555/2349508.2349517

Rethinking M&S to enhance creativity and computational discovery

2009· article· en· W2153223313 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

VenueSummer Computer Simulation Conference · 2009
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCreativityAmbiguityComputational creativityComputer scienceFutures studiesRealization (probability)Cognitive scienceManagement scienceGenerative grammarCognitionArtificial intelligenceData scienceHuman–computer interactionEngineeringPsychologyMathematicsProgramming language

Abstract

fetched live from OpenAlex

While M&S has been widely used in engineering and computational sciences to facilitate empirical insight, optimization, and experimentation, the role of simulation in supporting early foresight phases of creative problem solving received less attention. We advocate models of creative cognition to rethink simulation modeling so that creativity is enhanced rather than stifled. Generative Parallax Simulation (GPS) is introduced as a strategy and a generic and abstract specification for its realization is presented. GPS is based on an evolving ecology of ensembles of models that aim to cope with ambiguity, which pervades in early phases of model-based science.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.702
Threshold uncertainty score0.631

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 studies0.0000.000
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 head0.082
GPT teacher head0.409
Teacher spread0.327 · 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