James Evans is the Max Palevsky Professor in Sociology & Data Science, Director of the Knowledge Lab, and Faculty Director of the Master’s Program in Computational Social Science at the University of Chicago. He is also an External Professor at the Santa Fe Institute.
His research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement (and dispute), accumulation of certainty (and doubt), and the texture—novelty, ambiguity, topology—of understanding. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery.
His research has been published in Nature, Science, the Proceedings of the National Academy of Sciences, the American Journal of Sociology, the American Sociological Review, the Social Studies of Science, and other outlets. His work has been featured in The Economist, The Atlantic Monthly, Wired, NPR, BBC, El País, CNN, Le Monde, and many other outlets.
Paper 1: Simulating Subjects: The Promise and Peril of AI Stand-ins for Social Agents and Interactions
Paper 2: Can Large Language Model Agents Simulate Human Trust Behavior?
Paper 3: In Silico Sociology: Forecasting COVID-19 Polarization with Large Language Models
Paper 4: Localized Cultural Knowledge is Conserved and Controllable in Large Language Models
Paper 5: Biased AI improves human decision-making but reduces trust
Abstract
Large Language and Multi-Modal Models (LLMs), through their exposure to massive collections of online text, audio, and images, learn the ability to reproduce the perspectives and styles of diverse social, cultural, and economic groups. This capability suggests a powerful potential for generative social science – the simulation of empirically realistic, socio-culturally and economically situated human individuals and higher-order collectives, from discussions and teams to cities, economies, and countries. Synthesizing recent research in artificial intelligence and computational social science, I outline an approach to simulate human perspectives and interactive behaviors that enable generative modeling of humans and society and their implications for new social scientific understanding, insights, and institutions. These provide new designs for AI agents interacting with each other and with human agents, and the need for a new ethics in a world increasingly suffused with AI agents--desiderata for how we redesign our world as an agent-based model. Finally, I outline a series of persistent challenges for AI agent models to simulate realistic distributions of human performance and recursively explore how our understanding of humans and societies allows us to improve large models, and also how understanding large models creates new insight into humans and societies, such that agent-based models not only produce improved agents for social science, but AI services and AI institutions that operate as forces in the world.
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