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Record W2123696482 · doi:10.5555/2693848.2693977

Simulation by example for complex systems

2014· article· en· W2123696482 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

VenueWinter Simulation Conference · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceProcess (computing)Representativeness heuristicBusiness process modelingComplex systemBusiness processInstrumentation (computer programming)Business process managementManagement scienceSystems engineeringData scienceRisk analysis (engineering)Process managementWork in processEngineeringArtificial intelligenceOperations management

Abstract

fetched live from OpenAlex

Our goal is to support capacity management for systems such as hospitals, campuses, and cities, which utilize resources such as people, places, and things in complex ways. Simulation tools have traditionally been used for these sorts of studies, but they require expert model builders to create and maintain abstract business process models of the system under study. This can lead to a lack of representativeness and difficulty in adapting the model for additional or different study scenarios. This paper presents a new simulation approach, Simulation By Example, which overcomes these problems by guiding the simulation using traces, i.e., examples, of the behavior of the actual system but without requiring explicit business process models to be authored. Instead we rely on system instrumentation to capture the traces. We demonstrate the method in two case studies for healthcare systems as described in recent literature.

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)0.002
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 score0.983
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.341
GPT teacher head0.455
Teacher spread0.114 · 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