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Dave Roberts says the "OODA Loop" helps managers make decisions and act on them using a four-stage model: observation, orientation, decision and action.
Open image in new window Fig. 12 Comparative group size averages: (model observation data).
Open image in new window Fig. 13 Comparative average age: (model observation data).
Open image in new window Fig. 11 Comparative attrition rates: (model observation data).
Open image in new window Fig. 10 Linear regression: group size average (model observation data).
In principle, for the nonlinearities in the SV model observation equation, extensions to the popular KF, such as the extended Kalman filter (EKF) [19], the unscented Kalman filter (UKF) [54], and other Sigma-Point Kalman filters [55] should be applicable.
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As in past agent-based modeling studies, we conclude with model observations which support future theory generation for mitigating the negative organizational impacts of STEM worker attrition [2].
For age distribution, model observations show that STEM worker average age increases with STEM density.
These model observations have implications for theory generation for mitigating STEM shortage risks.
Model observations provide insight into emergent grouping properties not easily identifiable through traditional computational analysis [6].
In an analog model, observations of a process are used for the simulation of another similar natural process.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com