An Algorithmic Approach to Information and Meaning

TL;DR

This paper explores the concept of meaning through the lens of algorithmic information theory, proposing that logical depth and algorithmic probability can underpin a robust, interpretative definition of meaning.

Contribution

It introduces a novel perspective linking logical depth and algorithmic probability to the philosophical understanding of meaning in information theory.

Findings

Meaning correlates with Bennett's logical depth.

Algorithmic probability offers stability for defining meaning.

Proposes an interpretation-aware, algorithmic framework for meaning.

Abstract

I will survey some matters of relevance to a philosophical discussion of information, taking into account developments in algorithmic information theory (AIT). I will propose that meaning is deep in the sense of Bennett's logical depth, and that algorithmic probability may provide the stability needed for a robust algorithmic definition of meaning, one that takes into consideration the interpretation and the recipient's own knowledge encoded in the story attached to a message.