Publications

This report evaluates the accuracy of Artificial Societies' survey simulation against 1,000 real surveys sourced from UC Berkeley research. Artificial Societies achieved 93% response consistency and 86% distribution accuracy — within 5 points of the 91% human-replication ceiling. Standard LLM synthetic persona approaches, using models including GPT-5 and Gemini 2.5 Pro, achieved only 61–67% distribution accuracy. By drawing on a database of over 2 million real-world profiles and modelling social influence dynamics, Artificial Societies is five times closer to the human benchmark than the best standard LLM approach.

Artificial Intelligence (AI) chatbots, such as ChatGPT, have been shown to mimic individual human behaviour in a wide range of psychological and economic tasks. Do groups of AI chatbots also mimic collective behaviour? If so, artificial societies of AI chatbots may aid social scientific research by simulating human collectives. To investigate this theoretical possibility, we focus on whether AI chatbots natively mimic one commonly observed collective behaviour: homophily, people's tendency to form communities with similar others. In a large simulated online society of AI chatbots powered by large language models (N=33,299), we find that communities form over time around bots using a common language. In addition, among chatbots that predominantly use English (N=17,746), communities emerge around bots that post similar content. These initial empirical findings suggest that AI chatbots mimic homophily, a key aspect of human collective behaviour.

Understanding how vaccine hesitancy relates to online behavior is crucial for addressing current and future disease outbreaks. We combined survey data measuring attitudes toward the COVID-19 vaccine with Twitter data in two studies (N1 = 464, N2 = 1,600 Twitter users) to examine how real-world social media behavior is associated with vaccine hesitancy in the US and UK. We found that following US Republican politicians or hyper-partisan/low-quality news sites was associated with lower vaccine confidence — even controlling for political ideology and education. Network analysis revealed that low and high vaccine confidence participants separated into distinct communities, with centrality in the more right-wing community negatively associated with vaccine confidence in the US but not the UK. In Study 2, likelihood of not getting vaccinated was associated with sharing and favoriting low-quality news on Twitter. Altogether, vaccine hesitancy is associated with following, sharing, and interacting with low-quality information online, illustrating the challenges of encouraging vaccine uptake in a polarized social media environment.