Many major questions in the theory of evolutionary dynamics can in a
meaningful sense be mapped to analyses of stochastic trajectories in game
theoretic contexts. Often the approach is to analyze small numbers of distinct
populations and/or to assume dynamics occur within a regime of population sizes
large enough that deterministic trajectories are an excellent approximation of
reality. The addition of ecological factors, termed "eco-evolutionary
dynamics", further complicates the dynamics and results in many problems which
are intractable or impractically messy for current theoretical methods.
However, an analogous but underexplored approach is to analyze these systems
with an eye primarily towards uncertainty in the models themselves. In the
language of researchers in Reinforcement Learning and adjacent fields, a
Partially Observable Markov Process. Here we introduce a duality which maps the
complexity of accounting for both ecology and individual genotypic/phenotypic
types onto a problem of accounting solely for underlying information-theoretic
computations rather than drawing physical boundaries which do not change the
computations. Armed with this equivalence between computation and the relevant
biophysics, which we term Taak-duality, we attack the problem of "directed
evolution" in the form of a Partially Observable Markov Decision Process. This
provides a tractable case of studying eco-evolutionary trajectories of a highly
general type, and of analyzing questions of potential limits on the efficiency
of evolution in the directed case.

本论文提出了 Taak-duality，一种将生态和基因型 / 表型类型都考虑在内的术语，并利用这种等价关系研究了高度一般化类型的生态进化轨迹，分析了有向进化的效率潜在限制。