🤖 AI Summary
This work addresses the widespread misuse of information-theoretic measures in contemporary AI practice, which often stems from overlooking estimator assumptions, failure modes, and conditions required for reliable inference. The paper introduces the first unified decision framework that systematically integrates established measures—such as entropy and mutual information—with emerging ones like integrated information (Φ) and effective information. For each measure, the framework explicitly clarifies three core considerations: suitable AI application scenarios, appropriate estimators for given data types and dimensionalities, and common pitfalls leading to misinterpretation. Standardization and operationalization of measure selection are achieved through a combination of flowcharts, a primary decision table, and Bridge Box cognitive mapping techniques. The efficacy of this framework is empirically validated across three representative tasks: representation learning, temporal influence analysis, and agent complexity assessment.
📝 Abstract
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.