Visual ODD: A Standardised Visualisation Illustrating the Narrative of Agent-Based Models
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 models (ABMs) are commonly used tools across diverse disciplines, from ecology to social sciences and technology. Despite the effectiveness of the widely adopted Overview, Design concepts, and Details (ODD) protocol in ensuring transparency in ABM design and assumptions, the accompanying model descriptions are often lengthy, making quick overviews challenging. To facilitate comprehension, manuscripts, presentations, and posters often include visualisations of the model. Yet, the diversity of visualisation approaches complicates model comparisons and requires additional time for viewers to grasp the figure layouts. Additionally, these visualisations are usually poorly linked to corresponding sections of the written ODD model description. To address these challenges, we propose the standardised visual ODD (vODD) aimed to provide a quick overview of models and simplify the link to the written model description for readers who are more interested in specific elements. The standardised visualisation assigns defined positions for ODD elements for easy reference and comparison. We provide examples and guidance on constructing vODDs, along with templates for modellers to create their own visuals. While advocating for simplicity, we also illustrate how more complex models can still be effectively depicted in such visualisations. By establishing a generalised visualisation applicable to agent-based and other simulation models, we aim to improve the rapid comprehension of models and streamline graphical model representations in manuscripts, presentations, and posters.
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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 | 0.001 | 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