Towards a Synergy-based Approach to Measuring Information Modification

TL;DR

This paper introduces a new information-theoretic measure for quantifying information modification in distributed systems, utilizing partial information decomposition to better understand interactions between information sources.

Contribution

It proposes a novel measure of information modification based on partial information decomposition and discusses how to measure local dynamics of information modification in space and time.

Findings

A new measure of information modification capturing source interactions.

Evaluation of redundancy measures for localizability in space and time.

Framework for quantifying information modification events in complex systems.

Abstract

Distributed computation in artificial life and complex systems is often described in terms of component operations on information: information storage, transfer and modification. Information modification remains poorly described however, with the popularly-understood examples of glider and particle collisions in cellular automata being only quantitatively identified to date using a heuristic (separable information) rather than a proper information-theoretic measure. We outline how a recently-introduced axiomatic framework for measuring information redundancy and synergy, called partial information decomposition, can be applied to a perspective of distributed computation in order to quantify component operations on information. Using this framework, we propose a new measure of information modification that captures the intuitive understanding of information modification events as those involving interactions between two or more information sources. We also consider how the local dynamics of information modification in space and time could be measured, and suggest a new axiom that redundancy measures would need to meet in order to make such local measurements. Finally, we evaluate the potential for existing redundancy measures to meet this localizability axiom.