Discrete-Event Modeling and Simulation of Diffusion Processes in Multiplex Networks
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
A variety of phenomena (such as the spread of diseases, pollution in rivers, etc.) can be studied as diffusion processes over networks (i.e., the diffusion of the phenomenon over a set of interconnected entities). This research introduces a method to study such diffusion processes in multiplex dynamic networks. We use a formal Modeling and Simulation methodology (in our case, DEVS, Discrete-Event System Specification). We use DEVS formal models to integrate models defined using Agent-Based Modeling and Network Theory. We present (1) an Architecture to study Diffusion Processes in Multiplex dynamic networks (ADPM) and (2) a systematic Process to define, implement, and simulate diffusion processes over such networks. We show a theoretical definition and a concrete implementation of ADPM. We show how to use ADPM and the process in a case study based on a real nuclear emergency plan; this illustrates the application of the process, the architecture, and the developed software. Different scenarios are studied as Diffusion Processes to demonstrate the usability of ADPM.
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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 | 0.000 | 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 | 0.000 |
| 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