  Continuous adaptation to variable environments is crucial for the survival of
living organisms. Here, we analyze how adaptation, forecasting, and resource
mobilization towards a target state, termed actionability, interact to
determine biological function. We develop a general theory and show that it is
possible for organisms to continuously track their optimal state in a dynamic
environment by adapting towards an actionable target that incorporates just
current information on the optimal state and its rate of change. If the
environmental information is precise and readily actionable, it is possible to
implement perfect tracking without anticipatory mechanisms, irrespective of the
adaptation rate. In contrast, predictive functions, like those of circadian
rhythms, are beneficial if sensing the environment is slow or unreliable, as
they allow better adaptation with fewer resources. To explore potential
actionable forecasting mechanisms, we develop a general approach that
implements the adaptation dynamics with forecasting through a dynamics-informed
neural network.
