A Generalized Information Formula as the Bridge between Shannon and Popper

TL;DR

This paper introduces a generalized information formula linking Shannon and Popper's theories, enhancing understanding of knowledge evolution, data compression, and communication efficiency through a new information measure.

Contribution

It proposes a novel generalized information measure based on logical probability and fuzzy sets, bridging classical information theory with Popper's philosophy and improving data communication strategies.

Findings

The new information measure aligns with Popper's knowledge evolution criterion.

It enhances data compression by replacing average error with mutual information.

Insights into image communication and improvements to Popper's theory are provided.

Abstract

A generalized information formula related to logical probability and fuzzy set is deduced from the classical information formula. The new information measure accords with to Popper's criterion for knowledge evolution very much. In comparison with square error criterion, the information criterion does not only reflect error of a proposition, but also reflects the particularity of the event described by the proposition. It gives a proposition with less logical probability higher evaluation. The paper introduces how to select a prediction or sentence from many for forecasts and language translations according to the generalized information criterion. It also introduces the rate fidelity theory, which comes from the improvement of the rate distortion theory in the classical information theory by replacing distortion (i.e. average error) criterion with the generalized mutual information criterion, for data compression and communication efficiency. Some interesting conclusions are obtained from the rate-fidelity function in relation to image communication. It also discusses how to improve Popper's theory.