IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
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3 Pith papers cite this work. Polarity classification is still indexing.
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SocaSim uses LLM-based multi-agent simulations to model Putnam's Social Capital Theory, reproducing macro-level patterns and aligning with human group-level decisions, then applies the framework to smart elderly care adoption.
LLM agents display limited alignment with human emotional responses to red tape across cultures, performing worse in Eastern contexts, while cultural prompting offers little improvement.
citing papers explorer
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IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
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From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
SocaSim uses LLM-based multi-agent simulations to model Putnam's Social Capital Theory, reproducing macro-level patterns and aligning with human group-level decisions, then applies the framework to smart elderly care adoption.
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Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents
LLM agents display limited alignment with human emotional responses to red tape across cultures, performing worse in Eastern contexts, while cultural prompting offers little improvement.