Noise, fake news, and tenacious Bayesians

TL;DR

This paper introduces a signal processing-based framework to model decision-making dynamics influenced by reliable information, noise, and disinformation, revealing how confirmation bias can coexist with Bayesian updating and proposing new methods to combat fake news.

Contribution

It presents a unified modeling approach for information dynamics that incorporates disinformation and noise, offering insights into decision-making biases and strategies to address fake news.

Findings

High confidence in false realities leads to persistent beliefs despite contrary evidence

Noise can be exploited to escape false attractors in decision processes

The framework quantifies the impact of disinformation on decision dynamics

Abstract

A modelling framework, based on the theory of signal processing, for characterising the dynamics of systems driven by the unravelling of information is outlined, and is applied to describe the process of decision making. The model input of this approach is the specification of the flow of information. This enables the representation of (i) reliable information, (ii) noise, and (iii) disinformation, in a unified framework. Because the approach is designed to characterise the dynamics of the behaviour of people, it is possible to quantify the impact of information control, including those resulting from the dissemination of disinformation. It is shown that if a decision maker assigns an exceptionally high weight on one of the alternative realities, then under the Bayesian logic their perception hardly changes in time even if evidences presented indicate that this alternative corresponds to a false reality. Thus confirmation bias need not be incompatible with Bayesian updating. By observing the role played by noise in other areas of natural sciences, where noise is used to excite the system away from false attractors, a new approach to tackle the dark forces of fake news is proposed.