feat: add LLM Opinion Dynamics example using mesa-llm#360
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abhinavk0220 wants to merge 4 commits intomesa:mainfrom
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feat: add LLM Opinion Dynamics example using mesa-llm#360abhinavk0220 wants to merge 4 commits intomesa:mainfrom
abhinavk0220 wants to merge 4 commits intomesa:mainfrom
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…le model demonstrating LLM-powered opinion dynamics,where agents debate a topic using natural language reasoning insteadof classical mathematical convergence rules (cf. Deffuant-Weisbuch).Each agent:- Holds an opinion score (0-10) on a configurable debate topic- Observes neighboring agents' opinions each step- Uses CoT reasoning via LLM to decide whether to update its opinion- Produces emergent consensus or polarization from genuine reasoningVisualization includes opinion trajectory plot, mean opinion, andvariance over time via SolaraViz.Depends on mesa-llm for LLMAgent and CoTReasoning.
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Summary
Adds a new example demonstrating LLM-powered opinion dynamics, where
agents debate a topic using natural language reasoning instead of
classical mathematical rules.
Motivation
The existing
deffuant_weisbuchexample models opinion change usinga fixed convergence parameter (μ). This example shows what becomes
possible when agents use an LLM to genuinely reason about their
neighbors' arguments — producing emergent consensus or polarization
that no hardcoded rule could replicate.
What's included
agent.py—OpinionAgentextendingLLMAgentwith CoT reasoningmodel.py—LLMOpinionDynamicsModelon anOrthogonalMooreGridapp.py— SolaraViz with opinion trajectories, mean, and variance plotsREADME.md— explanation with comparison table vs Deffuant-WeisbuchHow it works
Each step, agents observe their Moore neighborhood, construct a prompt
summarizing neighbor opinions, and let the LLM decide whether to update
their opinion score (0-10). The topic is fully configurable.
Testing
from llm_opinion_dynamics.model import LLMOpinionDynamicsModelCloses #[issue number if any]
Related: mesa/mesa-llm#153