进化与发育动力学的变分综合
A variational synthesis of evolutionary and developmental dynamics
AI总结：
本文提出自然选择的变分表述，用贝叶斯力学和路径积分将表型适合度与系统发育学习统一起来，揭示种群必须按不同自然种类及其嵌套元种群来建模，而非同种个体种群。
摘要：
本文引入自然选择的变分表述，特别关注“事物”的本质，以及不同“种类”的“事物”如何相互区分并相互影响。我们利用特定划分的贝叶斯力学来理解缓慢的系统发育过程如何约束快速的表型过程，并如何被后者约束。主要结果是将适应性适合度表述为表型适合度的路径积分。在表型尺度和系统发育尺度上，最小作用量路径可分别被解读为推断过程和学习过程。在这一观点下，表型在生成模型下主动推断其生态位状态，而该模型的参数通过自然选择（贝叶斯模型选择）学习得到。由此产生的变分综合呈现出一些意想不到的方面。也许最值得注意的是，不可能描述或建模一个同种个体种群本身。相反，有必要考虑相互影响的不同自然种类的种群——以及嵌套的元种群。本文仅限于描述数学工具及伴随的思想。后续工作将使用这些方法进行模拟和数值分析，并确定与其他相关进化数学表述的接触点。
英文摘要：
This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain - and are constrained by - fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (bayesian model selection). The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations - and nested meta-populations - of different natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses - and identify points of contact with related mathematical formulations of evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
