Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mutual information underpins representation learning and feature selection, and transfer entropy reveals directed influence in dynamical systems. A second, less consolidated family of measures, integrated information (Phi), effective information (EI), and autonomy, has emerged for characterizing agent complexity. Despite wide adoption, measure selection is often decoupled from estimator assumptions, failure modes, and safe inferential claims. This paper provides a practical decision framework for all seven measures, organized around three prescriptive questions for each: (i) what question does the measure answer and in which AI context; (ii) which estimator is appropriate for the data type and dimensionality; and (iii) what is the most dangerous misuse. The framework is operationalized in two complementary artifacts: a measure-selection flowchart and a master decision table. We cover both AI/ML and decision-making agent application domains per measure, with standardized Bridge Boxes linking IT quantities to cognitive constructs. Three worked examples illustrate the framework on concrete practitioner scenarios spanning representation learning, temporal influence analysis, and evolved agent complexity.

翻译：信息论（IT）度量在人工智能中无处不在：熵驱动决策树分裂与不确定性量化，交叉熵是默认分类损失函数，互信息支撑表征学习与特征选择，传递熵揭示动力系统中的定向影响。第二类整合度较低的度量族——整合信息（Phi）、有效信息（EI）与自主性——已开始用于刻画智能体复杂性。尽管应用广泛，度量选择常与估计器假设、失效模式及可靠推理主张脱节。本文针对全部七种度量提出实用决策框架，围绕三个规范性提问进行组织：(i)该度量回答何种问题及适用于何种AI场景；(ii)何种估计器适合当前数据类型与维度；(iii)最危险的误用方式是什么。该框架通过两个互补工具实现：度量选择流程图与主决策表。我们覆盖每种度量的AI/ML与决策智能体应用领域，并通过标准化桥接盒将IT量值与认知构念关联。三个工作示例在具体实践场景（涵盖表征学习、时序影响分析与进化智能体复杂性）中阐释该框架。