  The aim of this manuscript is to contain the arguments and define the
theoretical objects for building a general framework to model population
dynamics from the ground up, relying mainly on the probabilistic landscapes
defining the dynamics instead of the context-dependent physical specification
of systems. I intend to keep updating and correcting this manuscript. The goal
is for all the different parts to be able to communicate with each other and
for models to be directly comparable and to maintain an explicit connection to
the first principles sustaining them. This modeling paradigm will stem from a
Bayesian perspective on model definition and interpretation and will be
primarily concerned with ecological and evolutionary processes. Populations are
considered to be abstract collections of elements that relate in the same ways,
and the laws of motion ultimately depend on relational properties of elements,
at first irrespective of their constitution. The states of populations are
taken to be their spatial densities, the fundamental quantities shaping the
dynamics of their interactions.
