Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation
We introduce a novel non-cooperative game to analyse opinion formation and resistance, incorporating principles from social psychology such as confirmation bias, resource constraints, and influence penalties. Our simulation features Large Language Model (LLM) agents competing to influence a population, with penalties imposed for generating messages that propagate or counter misinformation. This framework integrates resource optimisation into the agents' decision-making process. Our findings demonstrate that while higher confirmation bias strengthens opinion alignment within groups, it also exacerbates overall polarisation. Conversely, lower confirmation bias leads to fragmented opinions and limited shifts in individual beliefs. Investing heavily in a high-resource debunking strategy can initially align the population with the debunking agent, but risks rapid resource depletion and diminished long-term influence.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingLanguage ModelingLanguage ModellingLarge Language ModelMisinformationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been used to encode multi-agent interaction,…
Decision MakingDiffusion Stochastic Learning Over Adaptive Competing Networks
This paper studies a stochastic dynamic game between two competing teams, each consisting of a network of collaborating agents. Unlike fully cooperative settings, where all agents share a common objective, each team in t…
Towards control of opinion diversity by introducing zealots into a polarised social group
We explore a method to influence or even control the diversity of opinions within a polarised social group. We leverage the voter model in which users hold binary opinions and repeatedly update their beliefs based on oth…
DiversityFactorised Active Inference for Strategic Multi-Agent Interactions
Understanding how individual agents make strategic decisions within collectives is important for advancing fields as diverse as economics, neuroscience, and multi-agent systems. Two complementary approaches can be integr…
External Bias and Opinion Clustering in Cooperative Networks
In this work, we consider a group of n agents which interact with each other in a cooperative framework. A Laplacian-based model is proposed to govern the evolution of opinions in the group when the agents are subjected …
Clustering