Today’s episode is different from all the previous ones, as for the first time on Scaling Theory, we focus on research methodology, exploring how AI is reshaping the very process of doing research and what that shift means for science and society at large.
I sat down with James Evans, Professor of Sociology, Computational and Data Science at the University of Chicago, External Professor at the Santa Fe Institute, and Faculty Member at the Complexity Science Hub in Vienna, to explore how AI is transforming the way we simulate, scale, and understand human behavior, and what that shift means for science and society.
We dive into his pioneering work on using large language models to simulate individuals, societies, and entire social systems. James and I explore the strengths and limits of AI agents for both the social and hard sciences before reflecting on the future of social science itself. We talk about research centers entirely run by AI and conferences conducted by AI agents, without any human involvement. We also discuss the role of small research teams in disruptive innovation, and how to cultivate proximity and serendipity in a research world where we increasingly cooperate with machines.
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References:
- Simulating Subjects: The Promise and Peril of AI Stand-ins for Social Agents and Interactions (2025) https://osf.io/preprints/socarxiv/vp3j2_v3
- LLM Social Simulations Are a Promising Research Method (2025) https://arxiv.org/pdf/2504.02234
- Large teams develop and small teams disrupt science and technology (2019) https://www.nature.com/articles/s41586-019-0941-9?wpisrc=
- AI Expands Scientists' Impact but Contracts Science's Focus (2024) https://arxiv.org/abs/2412.07727
- The Paradox of Collective Certainty in Science (2024) https://arxiv.org/html/2406.05809v1?utm_source=chatgpt.com
- Being Together in Place as a Catalyst for Scientific Advance (Research Policy, 2023) https://www.sciencedirect.com/science/article/pii/S0048733323001956
