Effective models of human attitudes and behavior can empower applications ranging from immersive environments to social policy simulation. However, traditional simulations have struggled to capture the complexity and contingency of human behavior. The argument is that modern artificial intelligence models allow for a re-examination of this limitation. The case is made through generative agents: computational software agents that simulate human behavior. By enabling generative agents to remember, reflect, and plan, an interactive sandbox town of twenty-five agents inspired by The Sims is populated. Then, by anchoring agents’ memories in qualitative interviews of over 1,000 Americans, it is described how generative agents are able to replicate participants’ responses on the General Social Survey 85% as accurately as participants replicate their own answers. Finally, it is explored how these human behavioral models can help design more effective online social spaces, understand the societal disagreement underlying modern AI models, and better embed societal values into algorithms.

Bio:
Michael Bernstein is an associate professor of computer science at Stanford University, where he is a Bass University Fellow, Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence, and Interim Director of the Symbolic Systems Program. His research focuses on designing social, societal, and interactive technologies. His research has been reported in venues such as The New York Times, TED AI, and MIT Technology Review, and Bernstein himself has been recognized with an Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the Computer History Museum’s Patrick J. McGovern Tech for Humanity Prize. He holds a bachelor’s degree in Symbolic Systems from Stanford University, as well as a master’s degree and a Ph.D. in Computer Science from MIT.

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