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Capturing Argument in Agent-Based Models.

Leon Assaad1, Rafael Fuchs2, Kirsty Phillips3

  • 1Munich Center for Mathematical Philosophy, LMU, Geschwister-Scholl-Platz 1, 80539 Munich, Bavaria Germany.

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PubMed
Summary
This summary is machine-generated.

Agent-based models (ABMs) can model complex arguments by distinguishing arguments as reasons, syllogisms, and dialectics. The NormAN framework, using Bayesian networks, offers a novel way to compare these models and study argument diffusion.

Keywords:
Agent-based modellingArgumentationBayesian epistemologyDialecticsSocial epistemology

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Agent-based models (ABMs) are prevalent for studying complex systems and emergent properties.
  • Belief and opinion dynamics in ABMs are relevant to online social media and scientific discourse.
  • Existing ABMs have not fully captured rich argumentation scenarios.

Purpose of the Study:

  • To introduce a framework for agent-based models of argument.
  • To distinguish and analyze three notions of argument: reasons, syllogisms, and dialectics.
  • To provide an organizing scheme for comparing and choosing argument models.

Main Methods:

  • Distinguishing arguments as propositional content (reasons), premise-conclusion relationships (syllogisms), and conversational deployment (dialectics).
  • Utilizing the NormAN framework, which builds ABMs on Bayesian networks, as a reference model.
  • Analyzing the continuum of complexity in modeling each notion of argument.

Main Results:

  • A novel organizing scheme for comparing and selecting agent-based argument models.
  • Clarification of how the three notions of argument constrain each other.
  • Demonstration that the NormAN framework captures familiar argumentation facets and dialectical influences on argument diffusion.

Conclusions:

  • Agent-based models can effectively represent complex argumentation, including dialectical aspects.
  • The NormAN framework provides a versatile approach for modeling argument exchange.
  • Further research can explore how dialectical considerations impact argument diffusion in populations.