[Paper Review] A Bayesian Agent-Based Framework for Argument Exchange Across Networks

Leon Assaad, Rafael Fuchs+4 more|arXiv (Cornell University)|Nov 14, 2023

Opinion Dynamics and Social Influence4 citations

TL;DR

This paper introduces NormAN, a Bayesian agent-based framework that models multi-agent argument exchange across social networks using Bayesian Networks to represent world states and belief updates. It enables dynamic aggregation of diverse, competing arguments with varying strengths, demonstrating how communication rules and network structures shape collective belief dynamics in complex, realistic debate scenarios.

ABSTRACT

In this paper, we introduce a new framework for modelling the exchange of multiple arguments across agents in a social network. To date, most modelling work concerned with opinion dynamics, testimony, or communication across social networks has involved only the simulated exchange of a single opinion or single claim. By contrast, real-world debate involves the provision of numerous individual arguments relevant to such an opinion. This may include arguments both for and against, and arguments varying in strength. This prompts the need for appropriate aggregation rules for combining diverse evidence as well as rules for communication. Here, we draw on the Bayesian framework to create an agent-based modelling environment that allows the study of belief dynamics across complex domains characterised by Bayesian Networks. Initial case studies illustrate the scope of the framework.

Motivation & Objective

• To bridge the gap between dyadic argumentation research and large-scale opinion dynamics modeling by incorporating multiple arguments with varying strengths into agent-based simulations.
• To address the limitation of existing models that treat arguments as single opinions or sentiments, rather than as structured, evidence-based reasoning.
• To develop a normative, Bayesian framework that supports both descriptive modeling of real-world argumentation and normative evaluation of reasoning quality.
• To enable systematic study of how communication rules, network structure, and agent-specific beliefs influence collective belief formation in debates.
• To provide a flexible, extensible modeling framework for studying argument exchange in complex domains such as democratic deliberation or online social media discourse.

Proposed method

• The framework uses Bayesian Belief Networks (BBNs) to represent the ground truth world and the evidential relationships among variables relevant to a debate topic.
• Agents are endowed with individual Bayesian Networks that encode their subjective beliefs, which are updated using conditional probability updates upon receiving new arguments.
• Argument exchange is modeled as evidence transmission: agents communicate arguments (as probabilistic evidence) to peers, with strength and relevance determined by the argument's position in the BBN.
• Communication rules govern which agents exchange arguments, with options for fixed, random, or dynamic network topologies, including homophily and preferential attachment.
• Belief aggregation across agents is achieved through Bayesian conditionalization, allowing for hierarchical and competing arguments to be combined in a normatively principled way.
• The framework supports both normative evaluation (e.g., assessing argument quality) and descriptive simulation of belief evolution under different communication and network conditions.

Figure 1. Illustration of the main components of NormAN. A model within the NormAN framework specifies (1) a ground truth world, (2) a social network, and (3) individual agents communicating across that network. Each of these three components has core specifications. In addition, the square in the m

Experimental results

Research questions

• RQ1 How do different argument aggregation rules affect the convergence or divergence of agent beliefs in a networked debate?
• RQ2 To what extent do communication rules—such as who communicates with whom—affect the accuracy and stability of collective beliefs?
• RQ3 How does network topology (e.g., small-world, scale-free, hierarchical) influence the spread and impact of strong versus weak arguments?
• RQ4 What role does agent-specific subjective modeling of the world (via personal BBNs) play in shaping belief dynamics, especially when beliefs diverge from ground truth?
• RQ5 How do strategic communication behaviors and trust in sources affect the epistemic outcomes of argument exchange in social networks?

Key findings

• The framework successfully models the exchange of multiple, competing, and graded arguments in a way that preserves their evidential structure and probabilistic impact.
• Simulation results show that network structure significantly influences belief convergence: dense, clustered networks promote faster consensus, while sparse or heterogeneous networks lead to persistent disagreement.
• Agents with more accurate Bayesian models of the world (closer to ground truth) are more likely to influence others and achieve higher belief accuracy, especially when communication is frequent.
• The inclusion of argument strength and relevance through BBN structure leads to more nuanced belief updates than simple opinion averaging or contagion models.
• Communication rules that prioritize high-quality arguments or trusted sources lead to more accurate collective outcomes, even in the presence of misinformation.
• The framework enables the identification of 'epistemic bottlenecks'—agents or network positions that disproportionately affect the flow of accurate information—offering new targets for intervention in deliberative systems.

Figure 2. The original ‘Asia’ lung cancer network (Lauritzen and Spiegelhalter, 1988a ) . The Asia BN model was accessed via the bnlearn Bayesian Network Repository ( https://www.bnlearn.com/bnrepository/discrete-small.html ); it is also one of the exemplar BNs used in the bnlearn package documentat