# Opinion dynamics model based on cognitive biases

**Authors:** Pawel Sobkowicz  
**arXiv:** [1703.01501](https://arxiv.org/abs/1703.01501)  
**Date:** 2017-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/1703.01501v1/

## Key points

- The paper presents a novel model of individual and group opinion dynamics that takes into account different ways in which information is filtered due to cognitive biases.
- The agent-based model uses Bayesian updating of the individual belief distribution and is based on recent psychology work by Dan Kahan.
- The open nature of the model allows for studying the effects of both static and time-dependent biases and information processing filters, including psychological, social, and algorithmic filters.
- The paper compares the effects of two important psychological mechanisms: confirmation bias and politically motivated reasoning.
- Depending on the effectiveness of information filtering (agent bias), agents confronted with an objective information source may either reach a consensus based on truth or remain divided despite evidence.
- This model provides insight into increasingly polarized modern societies as it allows mixing different types of filters.
- The study's findings suggest that individuals' beliefs are influenced by their cognitive biases, which can lead to polarization in society.
- When agents have strong confirmation bias or politically motivated reasoning, they are less likely to reach a consensus even when presented with objective evidence.
- Overall, this research highlights the importance of understanding how cognitive biases affect opinion dynamics in society. By identifying these biases and developing strategies to mitigate their effects, we can promote more informed decision-making and reduce polarization in our communities.

## Summary

In the paper "Opinion Dynamics Model Based on Cognitive Biases," author Pawel Sobkowicz presents a novel model of individual and group opinion dynamics that takes into account different ways in which information is filtered due to cognitive biases. The agent-based model uses Bayesian updating of the individual belief distribution and is based on recent psychology work by Dan Kahan. The open nature of the model allows for studying the effects of both static and time-dependent biases and information processing filters, including psychological, social, and algorithmic filters. The paper compares the effects of two important psychological mechanisms: confirmation bias and politically motivated reasoning. Depending on the effectiveness of information filtering (agent bias), agents confronted with an objective information source may either reach a consensus based on truth or remain divided despite evidence. This model provides insight into increasingly polarized modern societies as it allows mixing different types of filters. The study's findings suggest that individuals' beliefs are influenced by their cognitive biases, which can lead to polarization in society. The results also show that when agents have strong confirmation bias or politically motivated reasoning, they are less likely to reach a consensus even when presented with objective evidence. Overall, this research highlights the importance of understanding how cognitive biases affect opinion dynamics in society. By identifying these biases and developing strategies to mitigate their effects, we can promote more informed decision-making and reduce polarization in our communities.