Modeling By Elaboration: An Application To Visual Process Simulation
Bibliographic record
Abstract
Attempts to expand the role of OR/MS into the broader community have been limited in part by the difficulties of educating non-OR/MS professionals concerning the intricacies of modeling. The purpose of this paper is to present and illustrate a new methodology called Modeling by Elaboration (MBE). This methodology assists naïve as well as advanced modelers in developing complex models through a process of systematically “elaborating” a fundamental model linked to a specified problem domain. The differences between MBE and other well-known modeling paradigms, such as top-down modeling and rapid prototyping, are discussed. We provide a framework for model elaboration that uses definitional, structural, and hybrid modifications. The MBE process is illustrated using a visual simulation package us applied in a retail-banking domain. Implications of MBE are discussed from three perspectives: learning, software developers, and the organizations for which the models were developed. For future research, we suggest conducting comparative studies of MBE versus traditional modeling approaches in controlled experimental settings, and applying expert systems technology to direct users towards the most appropriate elaboration schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".