Cyclic and multilevel causation in evolutionary processes
Cyclic and multilevel causation in evolutionary processes
复制标题
进化过程中的循环和多层次因果关系
DOI：
10.1007/s10539-020-09753-3
复制
发表时间：
2020
影响因子：
2.5
通讯作者：
Gerstein, Mark
中科院分区：
文献类型：
--
作者：
Warrell, Jonathan;Gerstein, Mark
许多进化模型都隐含着因果过程。然而，诸如进化变量之间的因果反馈和在多个层次上起作用的进化过程等特征意味着传统的因果模型会错过重要的现象。我们在这里开发了一个通用的理论框架，用于分析进化过程，借鉴最近的方法，因果建模开发的机器学习文献，扩展了珍珠做演算，将循环因果相互作用和多级因果关系。我们还开发了必要的信息理论的概念，在我们的框架中分析因果信息动态，引入因果概括的部分信息分解框架。我们展示了我们的因果框架如何有助于澄清复杂性状分析和癌症遗传学背景下的概念问题，包括在表观遗传和环境反馈过程存在下将观察到的性状变异分配给遗传，表观遗传和环境来源，以及分别使用多级因果模型对癌症突变过程的适应性变异，以及通过信息理论界限将因果引起的与这些变量中观察到的变化相关联。在这个过程中，我们引入了一般类的多级因果进化过程，通过粗粒度的关系，在多个层次上连接进化过程。此外，我们展示了如何在我们的框架中制定一系列的健身模型，以及价格方程的因果模拟（推广概率赖斯方程），澄清实现/概率健身和直接/间接选择之间的关系。最后，我们认为我们的框架在生物学和进化的基础问题，包括随附性，多级选择和个性的潜在相关性。特别是，我们认为，我们的多级因果进化过程，结合最小描述长度原则，提供了一个概念框架，在该框架中，多层次的选择识别可能会减少到一个模型选择问题。
Many models of evolution are implicitly causal processes. Features such as causal feedback between evolutionary variables and evolutionary processes acting at multiple levels, though, mean that conventional causal models miss important phenomena. We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearls do-calculus to incorporate cyclic causal interactions and multilevel causation. We also develop information-theoretic notions necessary to analyze causal information dynamics in our framework, introducing a causal generalization of the Partial Information Decomposition framework. We show how our causal framework helps to clarify conceptual issues in the contexts of complex trait analysis and cancer genetics, including assigning variation in an observed trait to genetic, epigenetic and environmental sources in the presence of epigenetic and environmental feedback processes, and variation in fitness to mutation processes in cancer using a multilevel causal model respectively, as well as relating causally-induced to observed variation in these variables via information theoretic bounds. In the process, we introduce a general class of multilevel causal evolutionary processes which connect evolutionary processes at multiple levels via coarse-graining relationships. Further, we show how a range of fitness models can be formulated in our framework, as well as a causal analog of Prices equation (generalizing the probabilistic Rice equation), clarifying the relationships between realized/probabilistic fitness and direct/indirect selection. Finally, we consider the potential relevance of our framework to foundational issues in biology and evolution, including supervenience, multilevel selection and individuality. Particularly, we argue that our class of multilevel causal evolutionary processes, in conjunction with a minimum description length principle, provides a conceptual framework in which identification of multiple levels of selection may be reduced to a model selection problem.
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影响因子：
64.8
作者：
通讯作者：
--
使用循环结构方程模型进行因果分析
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
Nagase;M. and Kano;Y.
通讯作者：
Y.
影响因子：
1.1
作者：
Krakauer, David;Bertschinger, Nils;Ay, Nihat
通讯作者：
Ay, Nihat
DOI：
10.1073/pnas.0602530103
发表时间：
2006-07-18
影响因子：
11.1
作者：
Traulsen, Arne;Nowak, Martin A.
通讯作者：
Nowak, Martin A.