MétaCan
Menu
Back to cohort
Record W4239336595 · doi:10.1109/wsc.2018.8632368

A SYMMETRIC FORMALISM FOR DISCRETE EVENT SIMULATION WITH AGENTS

2018· article· en· W4239336595 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

Venue2018 Winter Simulation Conference (WSC) · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsDEVSFormalism (music)InitializationDataflowComputer scienceTheoretical computer scienceDiscrete event simulationDistributed computingModeling and simulationAlgorithmParallel computingProgramming languageSimulation

Abstract

fetched live from OpenAlex

In designing a general modeling formalism for domain experts, a key challenge is to support a broad selection of their preferred paradigms yet minimize their exposure to complexity. With this aim, a formalism called Symmetric DEVS is proposed for specifying models that incorporate elements of discrete event simulation, dataflow programming, and agent-based modeling. Symmetric DEVS is based on the Discrete Event System Specification (DEVS) formalism, but differs in that atomic and composite nodes for discrete events are complemented with function and collection nodes for dataflow and agents. Like DEVS, nodes communicate over simulated time via message ports, but they also feature flow ports accommodating initialization and finalization operations. To minimize conceptual complexity, specifications are pared down to the essential elements and formulated to exhibit a high degree of symmetry. This paper defines the mathematical elements of Symmetric DEVS and presents an example of each of the four types of nodes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.930
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.211
GPT teacher head0.465
Teacher spread0.254 · 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