Opinion dynamics model based on cognitive biases

Abstract

We present an introduction to a novel model of an individual and group opinion dynamics, taking into account different ways in which different sources of information are filtered due to cognitive biases. The agent based model, using Bayesian updating of the individual belief distribution, is based on the recent psychology work by Dan Kahan. Open nature of the model allows to study the effects of both static and time-dependent biases and information processing filters. In particular, the paper compares the effects of two important psychological mechanisms: the confirmation bias and the politically motivated reasoning. Depending on the effectiveness of the information filtering (agent bias), the agents confronted with an objective information source may either reach a consensus based on the truth, or remain divided despite the evidence. In general, the model might provide an understanding into the increasingly polarized modern societies, especially as it allows mixing of different types of filters: psychological, social, and algorithmic.

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References in corpus (12)

• Statistical physics of social dynamics
• Incomplete ordering of the voter model on small-world networks
• Collective emotions online and their influence on community life
• Scaling and universality in proportional elections
• Coarsening and persistence in the voter model
• Dynamics of hate based networks
• Bayesian Updating Rules in Continuous Opinion Dynamics Models
• Non-monotonic spontaneous magnetization in a Sznajd-like Consensus Model
• Opinion dynamics and decision of vote in bipolar political systems
• Killer Geometries in Competing Species Dynamics
• Bias, Belief and Consensus: Collective opinion formation on fluctuating networks
• Unknowable Manipulators: Social Network Curator Algorithms