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Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making

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  • George Butler
  • Gabriella Pigozzi
  • Juliette Rouchier

Abstract

In this article, we propose an agent-based model of opinion diffusion and voting where influence among individuals and deliberation in a group are mixed. The model is inspired from social modeling, as it describes an iterative process of collective decision-making that repeats a series of interindividual influences and collective deliberation steps, and studies the evolution of opinions and decisions in a group. It also aims at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and ABM-influence models, which are not obvious to combine as a formal link between them is required. In our model, we find that deliberation, through the exchange of arguments, reduces the variance of opinions and the proportion of extremists in a population as long as not too much deliberation takes place in the decision processes. Additionally, if we define the correct collective decisions in the system in terms of the arguments that should be accepted, allowing for more deliberation favors convergence towards the correct decisions.

Suggested Citation

  • George Butler & Gabriella Pigozzi & Juliette Rouchier, 2019. "Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making," Complexity, Hindawi, vol. 2019, pages 1-31, August.
    • Handle: RePEc:hin:complx:3758159
    DOI: 10.1155/2019/3758159
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    References listed on IDEAS

    as
    1. Juliette Rouchier & Emily Tanimura, 2012. "When overconfident agents slow down collective learning," Post-Print hal-00623966, HAL.
    2. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    3. Victorien Barbet & Noé Guiraud & Vincent Laperrière & Juliette Rouchier, 2019. "Haggling on Values: Towards Consensus or Trouble," AMSE Working Papers 1909, Aix-Marseille School of Economics, France.
    4. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    5. Yao-feng Zhang & Hong-ye Duan & Zhi-lin Geng, 2017. "Evolutionary Mechanism of Frangibility in Social Consensus System Based on Negative Emotions Spread," Complexity, Hindawi, vol. 2017, pages 1-8, June.
    6. Juliette Rouchier & Paola Tubaro & Cécile Emery, 2014. "Opinion transmission in organizations: an agent-based modeling approach," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 252-277, September.
    7. Juliette Rouchier & Emily Tanimura, 2012. "When overconfident agents slow down collective learning," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00623966, HAL.
    8. Wander Jager & Frédéric Amblard, 2005. "Uniformity, Bipolarization and Pluriformity Captured as Generic Stylized Behavior with an Agent-Based Simulation Model of Attitude Change," Computational and Mathematical Organization Theory, Springer, vol. 10(4), pages 295-303, January.
    9. Barabas, Jason, 2004. "How Deliberation Affects Policy Opinions," American Political Science Review, Cambridge University Press, vol. 98(4), pages 687-701, November.
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