Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model

Ross Williams; Niyousha Hosseinichimeh

physics.soc-phAug 26, 2026

Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model

Authors: Ross Williams, Niyousha Hosseinichimeh

Organizations: Industrial and Systems Engineering, Virginia Tech; Alexandria, VA 22305, USA.

Abstract

As generative AI gains traction, researchers are investigating its potential to serve as proxies for humans. From undergoing cognitive psychology experiments to experiencing an epidemic, generative agents, agents powered by generative AI models, produce realistic human behavior when prompted. This study explores the sensitivity of these generative agents' behavior to prompt modifications and varied persona names of the agents. To assess this sensitivity, we use a generative agent epidemic model, wherein each agent is prompted daily on whether it wants to isolate or commingle with other agents. We found that using synonymous prompts results in negligible changes to the model's outcomes. However, minor variations in prompts, as well as contextual changes, do influence the model's results. Lastly, our data indicates that different persona names assigned to generative agents, specifically those imbued with personas, do not significantly impact epidemic outcomes.

Spatial Epidemic Modeling

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Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content

Ljubisa Bojic, Tijana Stanic, Joerg Matthes, Agariadne Dwinggo Samala+2

Institute for Artificial Intelligence Research and Development of Serbia; Address of correspondence: 1 Frukogorska 1, 21000 Novi Sad, Serbia · University of Belgrade, Institute for Philosophy and Social Theory, Digital Society Lab; Address of correspondence: Kraljice Natalije 45, 11000 Belgrade, Serbia · Complexity Science Hub, Vienna, Austria; Address of correspondence: Metternichgasse 8, 1030 Vienna, Austria+5

Large Language Model-Based Social Simulation User Simulation LLM Agent Evaluation Prompt Engineering Personality Modeling in Language Models

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Platform policies are increasingly tested on artificial users, making agent fidelity important. Yet convincing fake profiles could also manipulate perceived public opinion before elections. Validation has concentrated on agreement with human behaviour and has paid little attention to whether an agent behaves in line with the profile it was given. The present study profiled eight Serbian participants through a questionnaire, a deep interview, and a written self-presentation, recorded their reactions to sixty-eight social media posts, and asked four language models to predict those reactions under five prompt conditions varying profile content and instruction style. Attitudinal content improved prediction over demographic backstories by a wide margin. Agents matched their stated profiles more closely than participants matched their own survey answers, and consistency proved unrelated to fidelity once profile information was present. Instructing models to respond intuitively and immediately rather than analytically gave the highest fidelity of any condition and cut the compression of individual differences from seven times the human level to three. The advantage held on posts about topics the questionnaire never raised, where that condition reached the highest fidelity of any setup and beat a crowd baseline by a wide margin, which suggests that agents prompted this way could serve as general-purpose simulated users rather than specialists on the topics they were profiled for. Results may bear implications for the development of language models, because intuition-based setups appear better suited to some tasks than reasoning-based ones.

Abstract

Spatial Epidemic Modeling

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