How can I control opinions in a social network?
Containment Control Approach for Steering Opinion in a Social Network
February 5, 2025
https://arxiv.org/pdf/2502.01847This paper explores steering opinions in a social network, modeling the process as a containment control problem where "stubborn agents" (leaders) influence "regular agents" (followers) to reach a desired opinion distribution.
Key points relevant to LLM-based multi-agent systems:
- Decentralized control: Regular agents update their opinions based on local interactions with neighbors, mirroring decentralized communication in multi-agent LLMs.
- Influence and bias: The model uses influence weights and biases, which can be analogous to weighting and adjusting LLM outputs based on agent interactions.
- Network topology: The paper analyzes opinion evolution under different network structures (reducible and irreducible), highlighting the impact of communication pathways on multi-agent LLM behavior.
- Reward maximization: Agents adjust their behavior to maximize a reward, a core concept in reinforcement learning for LLMs. This allows the system to achieve a desired outcome.
- Potential for game-theoretic extension: The authors suggest future work using game theory, opening possibilities for strategic interactions between autonomous agents in LLM-based multi-agent systems.