The information theory of individuality
The information theory of individuality
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DOI：
10.1007/s12064-020-00313-7
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发表时间：
2020-03-24
影响因子：
1.1
通讯作者：
Ay, Nihat
中科院分区：
文献类型：
--
作者：
Krakauer, David;Bertschinger, Nils;Ay, Nihat
尽管生物学中几乎普遍认为个体是个体，但对于个体是什么，人们几乎没有达成一致意见，也很少有严格的定量方法来识别个体。在这里，我们提出个体是保持一定程度的时间完整性的集合，即从过去“传播”信息到未来。我们使用信息论和图形模型形式化这个想法。这个数学公式产生了三种原则和独特的个性形式——有机形式、殖民形式和驱动形式——每种形式在依赖环境和继承信息的程度上都有所不同。这种方法可以被认为是进化的格式塔方法，其中选择使用合适的信息论透镜区分图形-背景（代理-环境）。该方法的一个好处是，它扩大了允许个体的范围，包括多尺度、高度分布的系统中的自适应聚合，并且不一定有细胞壁或克隆体细胞组织等物理边界。这样的个体在选择中可能是可见的，但如果没有合适的测量原则，观察者很难发现。个性的信息理论允许在从分子到文化的所有组织层次上识别个体，并为测试关于系统自然尺度的假设提供了基础，并论证了在自适应系统中通过粗粒度减少不确定性的重要性。
Despite the near universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. Here, we propose that individuals are aggregates that preserve a measure of temporal integrity, i.e., "propagate" information from their past into their futures. We formalize this idea using information theory and graphical models. This mathematical formulation yields three principled and distinct forms of individuality-an organismal, a colonial, and a driven form-each of which varies in the degree of environmental dependence and inherited information. This approach can be thought of as a Gestalt approach to evolution where selection makes figure-ground (agent-environment) distinctions using suitable information-theoretic lenses. A benefit of the approach is that it expands the scope of allowable individuals to include adaptive aggregations in systems that are multi-scale, highly distributed, and do not necessarily have physical boundaries such as cell walls or clonal somatic tissue. Such individuals might be visible to selection but hard to detect by observers without suitable measurement principles. The information theory of individuality allows for the identification of individuals at all levels of organization from molecular to cultural and provides a basis for testing assumptions about the natural scales of a system and argues for the importance of uncertainty reduction through coarse-graining in adaptive systems.