Noise, Fake News, and Tenacious Bayesians

A modeling framework, based on the theory of signal processing, for characterizing the dynamics of systems driven by the unraveling 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 characterize the dynamics of the behavior 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.

Automated summary

This article proposes a modeling framework based on signal processing theory to describe the dynamics of systems driven by the unraveling of information, specifically focusing on the process of decision making. By quantifying the impact of information control, including the dissemination of disinformation, the study reveals that decision makers' perception hardly changes over time even when evidences indicate the alternative they heavily weight corresponds to a false reality, which suggests that confirmation bias and Bayesian updating can coexist. Additionally, the article introduces a new approach using noise to combat the dark forces of fake news.