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Record W3015697860 · doi:10.1109/tcss.2020.2982885

Cell-DEVS for Social Phenomena Modeling

2020· article· en· W3015697860 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

VenueIEEE Transactions on Computational Social Systems · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDEVSFormalism (music)Computer scienceDiscrete event simulationSystems modelingDistributed computingSystem requirements specificationComplex systemEvent (particle physics)Social systemModeling and simulationFormal specificationTheoretical computer scienceSoftware engineeringArtificial intelligenceSimulation

Abstract

fetched live from OpenAlex

Motivated by the need for formal methods as well as supporting tools to model and simulate social systems, we propose cellular discrete-event system specification as a formalism for modeling social systems. We also propose the use of a toolkit that implements the formalism of cellular discrete-event system specifications to implement and visualize models of social systems. We present examples of social system models that are different in sizes, nature, and rules controlling the interactions within those systems. We show that cellular discrete-event system specification with its unique features can successfully deal with the shortcoming of other modeling techniques. In addition, we show that together with its supporting toolkit, cellular discrete-event system specification is suitable for modeling, simulating, implementing, and visualizing social systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.238
GPT teacher head0.404
Teacher spread0.166 · 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