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Opinion Dynamics Model Based on Cognitive Biases of Complex Agents

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  • Pawel Sobkowicz

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.

Suggested Citation

  • Pawel Sobkowicz, 2018. "Opinion Dynamics Model Based on Cognitive Biases of Complex Agents," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(4), pages 1-8.
  • Handle: RePEc:jas:jasssj:2018-13-3
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    References listed on IDEAS

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    1. Pawel Sobkowicz, 2013. "Minority persistence in agent based model using information and emotional arousal as control variables," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(7), pages 1-11, July.
    2. P. Sobkowicz & A. Sobkowicz, 2010. "Dynamics of hate based Internet user networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(4), pages 633-643, February.
    3. Allen, Chris T. & Machleit, Karen A. & Kleine, Susan Schultz & Notani, Arti Sahni, 2005. "A place for emotion in attitude models," Journal of Business Research, Elsevier, vol. 58(4), pages 494-499, April.
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    Cited by:

    1. Christian Stummer & Lars Lüpke & Markus Günther, 2021. "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, vol. 91(9), pages 1469-1497, November.
    2. Agnieszka Kowalska-Styczeń & Krzysztof Malarz, 2020. "Noise induced unanimity and disorder in opinion formation," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-22, July.
    3. Cafferata, Alessia & Dávila-Fernández, Marwil J. & Sordi, Serena, 2021. "Seeing what can(not) be seen: Confirmation bias, employment dynamics and climate change," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 567-586.
    4. Carpentras, Dino & Quayle, Michael, 2022. "Propagation of measurement error in opinion dynamics models: The case of the Deffuant model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

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