Phenomenological Simulators of Critical Infrastructures
Bibliographic record
Abstract
The objective of this chapter is to introduce and discuss the main phenomenological approaches that have been used within the CI M&S area. Phenomenological models are used to analyse the organizational phenomena of the society considering its complexity (finance, mobility, health) and the interactions among its different components. Within CI MA&S, different modelling approaches have been proposed and used as, for example, physical simulators (e.g. power flow simulators for electrical networks). Physical simulators are used to predict the behaviour of the physical system (the technological network) under different conditions. As an example, electrical engineers use different kind of simulators during planning and managing of network activities for different purposes: (1) power flow simulators for the evaluation of electrical network configuration changes (that can be both deliberate changes or results from of the effects of accidents and/or attacks) and contingency analysis, (2) real time simulators for the design of protection devices and new controllers. For the telecommunication domain one mat resort to network traffic simulators as for example ns2/ns3 codes that allow the simulation of telecommunication networks (wired/wireless) at packet switching level and evaluate its performances. Single domains simulators can be federated to analyse the interactions among different domains. In contrast, phenomenological simulators use more abstract data and models for the interaction among the different components of the system. The chapter will describe the main characteristic of some of the main simulation approaches resulting from the ENEA and UBC efforts in the CIP and Complexity Science field. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".