Image-scenarization: a computer-aided approach for agent-based analysis and design
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.
Bibliographic record
Abstract
Agent-based modeling has been of interest to researchers for some time now. Some research has focused on the analysis and design of such software, but none has truly addressed the need for automated assistance in creating agent-based simulators from initial problem comprehension. This paper proposes an approach addressing the gap and supporting the spiral process of generating an agent-based simulator. In particular, this approach enables the incremental and iterative representation of a problem and its translation into an executable model. Initially using an unconstrained ontology, the designer draws conceptual graphs representing the problem. Progressively, graph elements are linked hierarchically under concepts that are part of a predefined generic Scenarization Vocabulary (i.e., agent, patient, behaviour, attribute, parameter, variable...). This Scenarization semantic defines roles in the simulation. This approach is part of a broader research effort known as IMAGE that develops a toolset concept supporting collaborative understanding of complex situations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it