Information flow in a network model and the law of diminishing marginal returns

TL;DR

This paper investigates how information flow in a network model exhibits diminishing returns, characterized by specific statistical distributions, and explores its relevance to brain signals through EEG data analysis.

Contribution

It introduces a simple dynamical network model capturing diminishing marginal returns and demonstrates its applicability to real EEG data.

Findings

Exponential distribution of incoming information in the model

Fat-tailed distribution of outgoing information observed

Similar phenomena found in EEG brain signals

Abstract

We analyze a simple dynamical network model which describes the limited capacity of nodes to process the input information. For a suitable choice of the parameters, the information flow pattern is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. The analysis of a real EEG data-set shows that similar phenomena may be relevant for brain signals.