Defined as the resistance of the observed performance curve to negative changes
in the applied environmental variance, and taking advantage of positive changes
in the applied environmental variance.
Basically scaling the observed performance during ideal conditions according to
whatever the variance applied on a given timestep t was.
On a step-down variance tripping, performance should get better by an
amount proportional to the amount of the step-down, and how close you
actually get to that amount is a measure of how adaptive the system is.
On a step-up variance tripping, performance should get worse by an amount
proportional to the amount of the step-up, and how far you actually stay
from that amount is a measure of how adaptive the system is.
Defined as the resistance of the observed performance curve to negative changes in the applied environmental variance, and taking advantage of positive changes in the applied environmental variance.
A(t) = P_observed(t) - (P_ideal(t) variance(t)) if variance(t) > 0 (negative changes) A(t) = P_observed(t) + (P_ideal(t) variance(t)) if variance(t) <= 0 (positive changes)
Basically scaling the observed performance during ideal conditions according to whatever the variance applied on a given timestep t was.
On a step-down variance tripping, performance should get better by an amount proportional to the amount of the step-down, and how close you actually get to that amount is a measure of how adaptive the system is.
On a step-up variance tripping, performance should get worse by an amount proportional to the amount of the step-up, and how far you actually stay from that amount is a measure of how adaptive the system is.