Particle filtering using agent-based transmission 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
Dynamic models are used to describe the spatio-temporal evolution of complex systems. It is frequently difficult to construct a useful model, especially for emerging situations such as the 2003 SARS outbreak. Here we describe the application of a modern predictor-corrector method - particle filtering - that could enable relatively quick model construction and support on-the-fly correction as empirical data arrives. This technique has seen recent use with compartmental models. We contribute here what is, to the best of our knowledge, the first application of particle filtering to agent-based models. While our particle models adapt to different ground-truth conditions, agent-based models exhibit limited adaptability under some model initializations. Several explanations are advanced for such behavior. Since this research serves as an initial foray into this line of investigation, we draw out a clear path of the next steps to determine the possible benefits of using particle filters on agent-based models.
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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 | 0.001 |
| 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 | 0.000 |
| 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