A Short Introduction to Information-Theoretic Cost-Benefit Analysis

TL;DR

This paper introduces an information-theoretic measure for analyzing data workflows, highlighting its broad applicability across machine learning, human cognition, and visualization, while discussing ongoing improvements for practical use.

Contribution

It provides a concise overview of the measure proposed by Chen and Golan, emphasizing its potential across various data intelligence domains and ongoing efforts to enhance its usability.

Findings

The measure explains informative trade-offs in data processes.

It is applicable to machine learning, cognition, and language development.

Ongoing improvements aim to make it more intuitive and practical.

Abstract

This arXiv report provides a short introduction to the information-theoretic measure proposed by Chen and Golan in 2016 for analyzing machine- and human-centric processes in data intelligence workflows. This introduction was compiled based on several appendices written to accompany a few research papers on topics of data visualization and visual analytics. Although the original 2016 paper and the follow-on papers were mostly published in the field of visualization and visual analytics, the cost-benefit measure can help explain the informative trade-off in a wide range of data intelligence phenomena including machine learning, human cognition, language development, and so on. Meanwhile, there is an ongoing effort to improve its mathematical properties in order to make it more intuitive and usable in practical applications as a measurement tool.