MétaCan
Menu
Back to cohort
Record W2074171563 · doi:10.1504/ijspm.2008.022052

Spreadsheet vs. multiagent-based simulations in the study of decision making in supply chains

2008· article· en· W2074171563 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Simulation and Process Modelling · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsSupply chainComputer scienceContext (archaeology)Focus (optics)Bullwhip effectMulti-agent systemOperations researchSupply chain managementSimulationIndustrial engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

A game called the Quebec Wood Supply Game (QWSG) is a role-playing simulation based on the Beer Game for teaching Supply Chain (SC) dynamics, and, in particular, the bullwhip effect. In this context, this paper describes and compares two simulators based on the QWSG which may be used to study decision making and its impact on SC dynamics. We first focus on the direct implementation of the QWSG in a spreadsheet program. This spreadsheet model is the base on which we next build a more complex MultiAgent Based Simulation (MABS) in which JACK™ agents represent companies. Finally, we compare the respective advantages of each simulator. We identify the features of a SC model making a spreadsheet simulation impossible, and those for which a spreadsheet simulation is better, as good as, or worse than MABS.

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)0.000
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: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.067
GPT teacher head0.327
Teacher spread0.260 · 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