【论文笔记】量化因果涌现

The beginning of wisdom is found in doubting; by doubting we come to the question, and by seeking we may come upon the truth. — Peter Abelard

有效信息EI

EI depends on both the effectiveness of a system’s mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system’s possible past and future states

EI涉及系统的有效性（是否能产生预期的输出或者结果）与系统状态空间的大小（系统可能存在的所有状态的集合）

如果系统更加具有确定性，也就是说其能更严格地限制或者确定其可能的过去或者未来，那么系统的EI会更高

EI is a general measure for causal interactions because it uses perturbations to capture the effectiveness/selectivity of the mechanisms of a system in relation to the size of its state space. As will be pointed out, EI is maximal for systems that are deterministic and not degenerate, and decreases with noise (causal divergence) and/or degeneracy (causal convergence).

EI是一种通用的度量方法，用于评估因果之间的交互关系

EI利用微小的扰动来评估系统机制的有效性或选择性，也就是说，对系统的输入进行微小的扰动，然后观察它的输出

噪声/因果分歧指的是随机性或不确定性，退化/因果收敛指的是多种不同的条件导向同一结果

时空尺度

时空尺度指的是在时间和空间上分析和描述现象的特定范围或粒度

符号概念

离散系统$S$

每个部分或元素在任何时刻只能拥有有限个不同状态

逻辑函数

与或非那些

状态依赖的因果度量(state-dependent measure of causation)

基于系统的特定状态来评估系统因果的一种度量方法

单个系统状态$s_0$

一个特定的系统状态

文章观点

for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence—the gain in EI when moving from a micro to a macro level of analysis.

宏观状态下系统的有效信息可能比微观状态下更多，因此尽管宏观是附着于微观之上的，但是宏观可以在因果层面上取代微观，从而导致真正的因果涌现

The approach to emergence investigated here provides theoretical support for the intuitive idea that, to find out how a system works, one should find the “differences that make [most of] a difference” to the system itself (25) (cf. ref. 36). It also suggests that complex, multilevel systems such as brains are likely to “work” at a macro level because, in biological systems, selectional processes must deal with unpredictability and lead to degeneracy (18). This may also apply to some engineered systems designed to compensate for noise and degeneracy. More broadly, this view of causal emergence suggests that the hierarchy of the sciences, from microphysics to macroeconomics, may not just be a matter of convenience but a genuine reflection of causal gains at the relevant levels of organization.

作者认为

1. 如果想要发现一个系统真正的工作机理，那么就要去探寻其之所以使其不同的特殊之处
2. 复杂的、多层的系统更类似于(likely)在宏观层面上工作
3. 从微观物理学到宏观经济学的科学层次，可能不仅仅是一种便利，而是在相关组织层次上因果收益的真实反映

理论分析

扫码阅读此文章
点击按钮复制分享信息