摘要翻译：
尽管众所周知，很难精确地确定是什么使生活如此独特和非凡，但人们普遍认为，它的信息方面是一个关键属性，也许是关键属性。生命系统独特的信息叙述表明，生命可能以依赖于上下文的因果影响为特征，特别是，自上而下（或向下）的因果关系--即组织等级中较高的层次影响和约束较低层次的动态--可能是生命系统等级结构的主要贡献者。在这里我们提出，生命的起源可能对应于与因果结构转移相关联的物理转变，在这种转变中，信息获得了对它所实例化的物质的直接的、依赖于上下文的因果效力。这种转变可能类似于更传统的物理转变（例如热力学相变），关键的区别是确定给定系统处于哪个阶段（非生命或生命）需要动力学信息，因此只能通过确定因果结构来推断。我们讨论了基于这一假设的一些潜在的新的研究方向，包括这种转变的潜在度量可能适合实验室研究，以及所提出的机制如何对应于生命系统特有的（算法）信息处理特征的开始。
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英文标题：
《The Algorithmic Origins of Life》
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作者：
Sara Imari Walker and Paul C. W. Davies
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最新提交年份：
2012
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分类信息：

一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Although it has been notoriously difficult to pin down precisely what it is that makes life so distinctive and remarkable, there is general agreement that its informational aspect is one key property, perhaps the key property. The unique informational narrative of living systems suggests that life may be characterized by context-dependent causal influences, and in particular, that top-down (or downward) causation -- where higher-levels influence and constrain the dynamics of lower-levels in organizational hierarchies -- may be a major contributor to the hierarchal structure of living systems. Here we propose that the origin of life may correspond to a physical transition associated with a shift in causal structure, where information gains direct, and context-dependent causal efficacy over the matter it is instantiated in. Such a transition may be akin to more traditional physical transitions (e.g. thermodynamic phase transitions), with the crucial distinction that determining which phase (non-life or life) a given system is in requires dynamical information and therefore can only be inferred by identifying causal architecture. We discuss some potential novel research directions based on this hypothesis, including potential measures of such a transition that may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.
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PDF链接：
https://arxiv.org/pdf/1207.4803