蓝图
选择(遗传算法)
物理定律
对象(语法)
计算机科学
因果关系
过程(计算)
认识论
现代进化合成
认知科学
数据科学
理论物理学
物理
人工智能
工程类
心理学
哲学
操作系统
机械工程
作者
Abhishek Sharma,Dániel Czégel,Michael Lachmann,Christopher P. Kempes,Sara Imari Walker,Leroy Cronin
出处
期刊:Nature
[Springer Nature]
日期:2023-10-04
卷期号:622 (7982): 321-328
被引量:16
标识
DOI:10.1038/s41586-023-06600-9
摘要
Scientists have grappled with reconciling biological evolution1,2 with the immutable laws of the Universe defined by physics. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary3-5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an 'object' on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.
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