Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents
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Abstract (arXiv)
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to third places (e.g., cafe, store, park), and fewer conversations throughout the town. In interview responses, agents explicitly attributed their behavioral changes to disease-avoidance motivations. A validity check further indicated that they could distinguish between infectious and noninfectious diseases, selectively reducing social engagement only when there was a risk of infection. Our findings highlight the potential of GABM as an experimental tool for exploring complex human social dynamics at scale.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 10 Jun 2025.
Builds on
18 of 62 references.
References · 18
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023ChatDev: Communicative Agents for Software Developmentcited by 919
- 2023Whose Opinions Do Language Models Reflect?cited by 773
- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
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- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 367
- 2024LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individualscited by 249
- 2023Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology Viewcited by 222
- 2023Do LLMs exhibit human-like response biases? A case study in survey designcited by 180
- 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
- 2023Simulating Opinion Dynamics with Networks of LLM-based Agentscited by 154
- 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 126
6 more
- 2023Generative Language Models Exhibit Social Identity Biasescited by 111
- 2025LLM Social Simulations Are a Promising Research Methodcited by 99
- 2022Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identitycited by 74
- 2023Epidemic Modeling with Generative Agentscited by 49
- 2023Lyfe Agents: Generative agents for low-cost real-time social interactionscited by 49
- 2020Mapping county-level mobility pattern changes in the United States in response to COVID-19cited by 8
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
- 2025Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy
- 2023Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence
- 2026Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model
- 2026From Who They Are to How They Act: Behavioral Traits in Generative Agent-Based Models of Social Media
- 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
- 2025Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism
- 2026A Large Language Model-Driven Agent-Based Modeling Framework with Multi-Round Communication for Simulating Vaccine Opinion Dynamics
- 2026Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems