Opinion dynamics model based on cognitive biases of complex agents

Abstract

We present an introduction to a novel way of simulating individual and group opinion dynamics, taking into account how various sources of information are filtered due to cognitive biases. The agent-based model presented here falls into the ‘complex agent’ category, in which the agents are described in considerably greater detail than in the simplest ‘spinson’ model. To describe agents’ information processing, we introduced mechanisms of updating individual belief distributions, relying on information processing. The open nature of this proposed model allows us to study the effects of various static and time-dependent biases and information filters. In particular, the paper compares the effects of two important psychological mechanisms: confirmation bias and politically motivated reasoning. This comparison has been prompted by recent experimental psychology work by Dan Kahan. Depending on the effectiveness of information filtering (agent bias), agents confronted with an objective information source can either reach a consensus based on truth, or remain divided despite the evidence. In general, this model might provide understanding into increasingly polarized modern societies, especially as it allows us to mix different types of filters: e.g., psychological, social, and algorithmic.