  We demonstrate with a thought experiment that fitness-based population
dynamical approaches to evolution are not able to make quantitative,
falsifiable predictions about the long-term behavior of evolutionary systems. A
key characteristic of evolutionary systems is the ongoing endogenous production
of new species. These novel entities change the conditions for already existing
species. Even {\em Darwin's Demon}, a hypothetical entity with exact knowledge
of the abundance of all species and their fitness functions at a given time,
could not pre-state the impact of these novelties on established populations.
We argue that fitness is always {\it a posteriori} knowledge -- it measures but
does not explain why a species has reproductive success or not. To overcome
these conceptual limitations, a variational principle is proposed in a
spin-model-like setup of evolutionary systems. We derive a functional which is
minimized under the most general evolutionary formulation of a dynamical
system, i.e. evolutionary trajectories causally emerge as a minimization of a
functional. This functional allows the derivation of analytic solutions of the
asymptotic diversity for stochastic evolutionary systems within a mean-field
approximation. We test these approximations by numerical simulations of the
corresponding model and find good agreement in the position of phase
transitions in diversity curves. The model is further able to reproduce
stylized facts of timeseries from several man-made and natural evolutionary
systems. Light will be thrown on how species and their fitness landscapes
dynamically co-evolve.
