Capturing Argument in Agent-Based Models.

Agent-based models (ABMs) are widely used to study the complex dynamics and emergent properties of systems with many interacting agents. This includes belief and opinion dynamics as are of relevance to understanding contexts as varied as online social media and the practice of science. This paper ar...

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Publicado en:Topoi: An International Review of Philosophy Vol. 44; no. 3; pp. 675 - 694
Autores principales: Assaad, Leon, Fuchs, Rafael, Phillips, Kirsty, Schöppl, Klee, Hahn, Ulrike
Formato: Artículo
Publicado: Springer Nature Aug2025
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Capturing Argument in Agent-Based Models.
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          Assaad, Leon
          Fuchs, Rafael
          Phillips, Kirsty
          Schöppl, Klee
          Hahn, Ulrike
        affil:
          https://ror.org/05591te55 Munich Center for Mathematical Philosophy, LMU, Geschwister-Scholl-Platz 1, 80539, Munich, Bavaria, Germany
          https://ror.org/05591te55 Graduate School of Systemic Neuroscience, LMU, Großhadernerstraße 2, 82152, Planegg-Martinsried, Bavaria, Germany
          https://ror.org/04cw6st05 Birkbeck College, University of London, Malet Street, WC1E 7HX, London, UK
          https://ror.org/012p63287 Faculty of Philosophy, University of Groningen, 9712 GL, Groningen, Netherlands
      su:
        Argument
        Dialectic
        Social dynamics
        Simulation methods & models
        Bayesian analysis
        Multiagent systems
        Model validation
        Attitude change (Psychology)
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        subj:
          Argument
          Dialectic
          Social dynamics
          Simulation methods & models
          Bayesian analysis
          Multiagent systems
          Model validation
          Attitude change (Psychology)
      keyword:
        Agent-based modelling
        Argumentation
        Bayesian epistemology
        Dialectics
        Social epistemology
      ab: Agent-based models (ABMs) are widely used to study the complex dynamics and emergent properties of systems with many interacting agents. This includes belief and opinion dynamics as are of relevance to understanding contexts as varied as online social media and the practice of science. This paper argues that such ABMs can capture rich argumentation scenarios in ways that have not been covered in research to date. To clarify the space of potential agent-based models of argument, we distinguish three interrelated notions of argument from the literature. First, arguments as reasons refer simply to the propositional content encoded in arguments. Second, arguments as syllogism describe premise-conclusion relationships that arise between such reasons when asserted as arguments. Third, arguments as dialectics refer to the deployment of reasons and syllogisms in discussions (be they polylogues or dialogues). We show how modelling each of these three notions of argument naturally involves a continuum of complexity. Specifically, we use the NormAN framework (introduced in Assaad et al. A Bayesian agent-based framework for argument exchange across networks.https://doi.org/10.48550/arXiv.2311.09254, 2023), which bases ABMs on the theory of Bayesian networks, as a point of reference and draw out its relationship to other modelling frameworks along each of these dimensions. This provides a novel organising scheme to aid model comparison and model choice, and clarifies ways in which these three notions of argument constrain one another. This shows also that NormAN's Bayesian framework not only captures familiar facets of argumentation, but also allows one to study how dialectical considerations influence population level diffusion of arguments (as we demonstrate with a small simulation study).
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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