Uncertainty on Discrete-Event System Simulation
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
Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic Systems. Uncertainty-Aware Discrete-Event System Specification (UA-DEVS) is a formalism for modeling Discrete-Event Dynamic Systems that include uncertainty quantification in messages, states, and event times. UA-DEVS models provide a theoretical framework to describe the models’ uncertainty and their properties. As UA-DEVS models can include continuous variables and non-computable functions, their simulation could be non-computable. For this reason, we also introduce Interval-Approximated Discrete-Event System Specification (IA-DEVS), a formalism that approximates UA-DEVS models using a set of order and bounding functions to obtain a computable model. The computable model approximation produces a tree of all trajectories that can be traversed from the original model and some erroneous ones introduced by the approximation process. We also introduce abstract simulation algorithms for IA-DEVS, present a case study of UA-DEVS, its IA-DEVS approximation and, its simulation results using the algorithms defined.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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